DDoS activity across Bing and other online services at Microsoft has seen a large uptick in the past five to six years. As reported in the Microsoft Digital Defense Report 2025, Microsoft now processes more than 100 trillion security signals, blocks approximately 4.5 million new malware attempts, analyzes 38 million identity risk detections, and screens 5 billion emails for malicious content each day. This helps illustrate both the breadth of modern attack surfaces and the automation cyberattackers can now wield at industrial scale. When we narrow in specifically on DDoS, an even clearer trend emerges: beginning in mid-March of 2024, Microsoft observed a rise in network DDoS attacks that eventually reached approximately 4,500 cyberattacks per day by June 2024. And this persistent volume was paired with a shift toward more stealthy application-layer techniques.

In my role as Vice President, Intelligent Conversation and Communications Cloud Platform at Microsoft, I focus on helping the Microsoft AI and Bing teams build systems that are safe, resilient, and worthy of user trust, even under the sustained pressure we’re receiving from today’s cyberattackers. Whether you are responsible for a single public website or a large portfolio of consumer-facing applications, defending against modern DDoS attacks means more than just absorbing traffic. It means building defense-in-depth robust enough that, even if some attack traffic gets through, your service stays usable for the people who rely on it.

The nature of modern DDoS attacks

Early DDoS attacks were largely about volume. Cyberattackers would flood a target with traffic in an attempt to saturate network capacity and force an outage. While volumetric attacks still happen, most large services now have baseline protections that make this approach less effective on its own.

Modern DDoS attacks are more nuanced. They are often multi-vector, with a single campaign potentially including network-layer floods and application-layer abuse at the same time. Along with the exponential increase in the scale of these cyberattacks, they are also getting more tailored to stress specific applications and user flows. Application-layer attacks are gaining popularity because they are harder to distinguish from legitimate usage.

We also see threat actors utilizing a broader range of devices in botnets, including consumer Internet of Things (IoT) devices and misconfigured cloud workloads. In some cases, cyberattackers abuse legitimate cloud infrastructure to generate traffic that blends in with normal usage patterns. Edge systems, such as content delivery networks (CDNs) and front-door routing services, are increasingly targeted because they sit at the boundary between users and applications.

When attack traffic looks like normal user traffic, typical network-level blocklists aren’t very effective. You need sophisticated fingerprinting (starting with JA4), layered controls, and good operational visibility. This evolution is part of what makes defending against DDoS more than a networking problem. It is now a system design problem, an operational monitoring problem, and ultimately a trust problem.

A defense-in-depth framework

Even if you block 95% of malicious traffic, the remaining 5% can still be enough to take you down if it hits the right bottleneck. That’s why defense-in-depth matters.

A strong defensive posture starts with making abnormal traffic easier to spot and harder to exploit. Techniques like rate limiting, geo-fencing, and basic anomaly detection remain foundational. They are most effective when tuned to your specific traffic patterns. Cloud-native DDoS protection services play an important role here by absorbing large-scale attacks and surfacing telemetry that helps teams understand what is happening in real time. If you run on Azure, there are built-in options that can help when used as part of a broader design. Azure DDoS Protection is designed to mitigate network-layer cyberattacks and is intended to be used alongside application design best practices. At the edge, services like Azure Web Application Firewall (WAF) on Azure Front Door can provide centralized request inspection, managed rule sets, geo-filtering, and bot-related controls to reduce malicious traffic before it reaches your origins.

Microsoft publishes a range of Secure Future Initiative (SFI) guidance and engineering blogs that describe patterns we use internally to harden consumer services at scale, and if you’re looking to assess how robust your site’s current DDoS resilience posture is, here’s a simple tabular framework to work from:

Planning for graceful degradation

One of the most common misconceptions about DDoS defense is that success means “no reduction in services.” In reality, even a partially successful attack can degrade performance enough to frustrate users or erode trust, without triggering a full outage. Graceful degradation is about maintaining core functionality even when systems are under stress. It means being deliberate about which user flows must remain available and which can be temporarily limited without causing disproportionate harm.

For example, our systems prioritize core scenarios over secondary features during extremely large cyberattacks. In practice, this can mean temporarily delaying nonessential personalization or shedding load from less critical features to preserve overall responsiveness. These decisions are made in advance and tested, not improvised during an incident. Here’s an example of how we might do that:

• Prioritizing core user flows: We would focus on keeping core scenarios responsive. That might mean protecting one or two core scenarios while de-emphasizing secondary experiences.

• Reducing expensive work first: Some parts of an experience are computationally heavier. Under attack pressure, those are candidates for temporary reduction, so the overall service stays usable.

• Tiered experience under load: In extreme conditions, you can provide a better experience for users with higher trust signals while still offering an acceptable experience to everyone else. This is not about punishing lower trust users. It is about making sure your system can still serve legitimate demand when resources are constrained.

• Clear user messaging: If you need to disable or simplify a feature temporarily, communicate it in a way that is honest and calm. You do not need to explain your internal architecture. You do need to be predictable.

Designing for resilience means assuming that individual components will fail or be stressed at some point. Systems that are built with that expectation tend to recover faster and maintain user trust more effectively than systems that aim for perfect uptime at all costs.

Get started improving your DDoS defense

If I could leave you with a single practical concept, it would be this: treat DDoS as a normal operating condition for internet-facing services. Build defense in depth. Assume some cyberattack traffic will get through. Design your service so it can degrade gracefully while protecting the user experiences that matter most.

Consumer trust is fragile and hard-earned. Developers and operators who think beyond raw availability, and who design for transparency, prioritization, and resilience, are better positioned to handle the realities of today’s cyberthreat landscape. Modern defensive strategies combine proactive controls, thoughtful architecture, and a clear understanding of what matters most to users.

For those interested in going deeper, I encourage you to explore the Secure Future Initiative resources and the other Office of the CISO blogs provided by my peers at Microsoft. Both of these resources frequently share practical patterns for building and operating resilient services at scale.

Microsoft
Deputy CISOs

To hear more from Microsoft Deputy CISOs, check out the OCISO blog series:

To stay on top of important security industry updates, explore resources specifically designed for CISOs, and learn best practices for improving your organization’s security posture, join the Microsoft CISO Digest distribution list.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.  

The post Defending consumer web properties against modern DDoS attacks appeared first on Microsoft Security Blog.

KuppingerCole Analysts’ 2026 Emerging AI Security Operations Center (SOC) reflects this shift clearly: the future of security automation is not defined by static rules or isolated workflows, but by intelligence‑driven automation that supports analyst decision‑making across the full security lifecycle. This evolution mirrors what many security leaders already experience day to day, that the limiting factor is no longer alert volume, but human capacity.

Microsoft is excited to be named an Overall Leader, and the Market Leader, in this report, as we see automation as a core component of the future of cybersecurity.

From playbook‑driven SOAR to intelligence‑led automation

Traditional security orchestration, automation, and response (SOAR) solutions were built to automate predictable, repeatable tasks: enrichment steps, ticket creation, notifications, and predefined containment actions. These capabilities remain valuable, but they were designed for an era when incidents followed more deterministic patterns.

This is a critical change. In many SOCs today, analysts still spend significant time:

• Stitching together context across alerts and data sources.
• Manually triaging incidents that turn out to be benign.
• Following repetitive investigation and response steps.

The result is slower response times and analyst burnout—at exactly the moment attackers are moving faster and operating more quietly.

Automation built into the analyst experience

Microsoft has evolved the way these common challenges can be addressed, leveraging machine learning, large language models (LLMs), and agents, including releases such as:

• Automatic attack disruption: An always-on capability that limits lateral attackers and reduces the overall impact of an attack, from associated costs to loss of productivity, leaving security operations teams in complete control of investigating, remediating, and bringing assets back online.
• Phishing triage agent: An agent that runs sophisticated assessments—including semantic evaluation of email content, URL and file inspection, and intent detection—to determine whether a submission is a true phishing threat or a false alarm.
• AI powered incident prioritization: A machine learning prioritization model to surface the incidents that matter most, assigning each incident a priority score from 0–100 and explaining the key factors behind the ranking. 
• Playbook generator: An experience that allows users to create python-code playbooks using natural language for flexible workflow automation.

These capabilities are just the beginning of how we are introducing agents and automation to help users move faster, freeing analysts to focus on higher‑value tasks like proactive hunting and threat analysis.

The next evolution: The agentic SOC

The KuppingerCole report reinforces a broader industry trend, that security platforms must do more than automate pre‑defined workflows. They must support adaptive, intelligence‑driven operations that can respond to novel and fast‑moving threats.

This is where Microsoft is making its next set of investments: agentic security operations.

With innovations such as the Microsoft Sentinel MCP (Model Context Protocol) Server, shared security data and graph context, and deep integration with Microsoft Security Copilot, Sentinel is evolving into a platform where AI agents can:

• Reason across identity, endpoint, cloud, and network signals.
• Summarize incidents and investigations in natural language.
• Assist with decision‑making by correlating weak signals over time.
• Take action—with human oversight—when confidence thresholds are met.

These agents are designed to work alongside analysts, augmenting expertise and dramatically accelerating time to response.

Why this matters for security teams

The direction highlighted by KuppingerCole, and reflected in Microsoft’s roadmap, isn’t about chasing AI for its own sake. It’s about addressing real SOC pain points:

• Scale: Human‑only operations don’t scale with modern attack surfaces.
• Consistency: Automated and agent‑assisted workflows reduce variance and errors.
• Speed: Faster reasoning and response directly reduce attacker dwell time.

By combining automation, rich context, and intelligent agents, Microsoft Sentinel helps SOC teams move from reactive alert handling to proactive, intelligence‑led defense without forcing teams to re‑architect their operations overnight.

Looking ahead

Security automation is no longer a bolt‑on capability. As KuppingerCole’s research makes clear, it is becoming a foundational element of modern security operations. The evolution of SOAR reflects the reality of a shift from static playbooks to adaptive, context‑aware assistance that scales human expertise.

Microsoft is investing accordingly, advancing an AI‑first approach to security analytics that helps SOC teams operate with greater speed, confidence, and resilience as threats continue to evolve. Read the Emerging AI Security Operations Center (SOC) report to learn more.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

The post ​​Microsoft named an overall leader in KuppingerCole Analyst’s 2026 Emerging AI Security Operations Center (SOC) report ​​ appeared first on Microsoft Security Blog.

The nature of cyberthreats has never been static, but it’s hard to accurately convey the scale of their recent evolution and proliferation. As we’ve seen in many other arenas, AI has become a very powerful productivity tool for would-be cybercriminals. Between April 2024 and April 2025, Microsoft stopped $4 billion in fraud attempts.¹ And as of the writing of the Microsoft Digital Defense Report 2025, we are tracking 100 trillion security signals each day (a 40% increase since 2023).²

This is why I decided to write a blog about risk reviews. By asking the right questions, risk reviews help us transform the utility of our security data from primarily reactive remediation and response information into key insights helping to inform our proactive security stances. And embracing strong proactive security is something we can all do to mitigate our increased exposure to security threats.  

Risk reviews are also a topic I’ve lent focus to during my first six months as Deputy CISO for Microsoft Security. It’s a very interesting role for me, as I’ve traditionally described myself as performance specialist and a systems specialist more than a security specialist. It’s not necessarily a distinction of skill set, but more one of mindset, and what I’d like to share with you is actually a bit of a synthesis of my inherent performance- and systems-first way of thinking and things I’ve brought into that practice after working with many of the other Microsoft Deputy CISOs over the last few months.

There are roughly eight points I want to bring up concerning risk reviews in this blog. Each point has the potential to help expose potential security vulnerabilities when brought up with security teams. Together, they represent a structured and approachable way to initiate necessary conversations and drive meaningful results:

1. Assets
2. Applications 
3. Authentication 
4. Authorization 
5. Network isolation 
6. Detections 
7. Auditing 
8. Things not to miss 

Now, why did I choose to highlight these areas and not others? Generally, I find that looking at problems from the lens of risk management gives me a fresh perspective. When you very consistently ask specific questions around these areas, they often effectively start the conversation you want to have.

Just one last thing before we dive in: What I’m about to tell you is only approximately correct. There will be edge cases and exceptions, but generally I think you’ll find this information helpful.

1. Assets

The best place to start a review is identifying the assets that you need to protect. This will largely define the scope of the review. A good place to find those assets is, of course, on your architecture diagrams and your threat models. The assets we’re talking about could be storage (where perhaps you’re storing sensitive or otherwise important data) or they could be highly-privileged applications like command-and-control systems or something similar. This is, in short, the list of things that your cyberattacker wants to get to. 

2. Applications

In the next step, you identify your applications. These are, broadly speaking, the active part of your system. They are the outward-facing surfaces that customers will use and the set of microservices that support your interface. These systems could be providing any set of services that you might need—and herein lies the problem. It’s entirely normal for your applications to require access to your most important assets, but that means the applications themselves can become viable targets for a cyberattacker. So how do we make this situation better? At this point, it’s reasonable to start talking about possible controls. 

Read up on Zero Trust for source code access.

3. Good quality authentication 

The next thing you will want to inspect is the form of authentication that your system is using. The best systems are using tokens for authentication, and they are getting these tokens from standard token issuers like, for instance, Microsoft Entra. It’s sometimes viable to have your own token generation system, but remember that such systems tend to have bugs. Those bugs can be exploitable. And even lacking bugs, there could be, say, gaps or vulnerabilities in your token issuing system such that perhaps the tokens cannot be properly scoped. The tokens could also tend to be too long-lived, or difficult to be made fine-grained enough, or lack the capacity to allow for flowing user context from the request to the authorization system. Many such deficiencies are possible. 

Even with a good quality token issuing system, you can easily find yourself in a situation where the tokens that you’re creating are too fungible, or too powerful, or both. Thinking back to the assets you’re trying to protect and the applications that you have, you can likely categorize some of the applications as having more “power,” if you will, than others. Sometimes we call these “highly privileged applications” because they have the capability to do something that is especially of interest to cyberattackers, like reading a lot of data, changing configuration, or anything like that. 

To best manage the privileges associated with these applications, it needs to be the case that the kinds of tokens that they use are as limited as possible. So, a particular token might authorize a capability for a certain customer, on behalf of a certain user, for a certain set of data—and nothing more than that. When privileges are very generic, like “I can do this operation for anyone, anywhere,” things become much more dangerous. So, here the idea is to make sure that the tokens that you’re getting are very specific to the intent that you have and that only the applications that need those tokens can get them, and, again, the tokens are as limited as possible. This goes a long way in reducing the possible damage that a cyberattacker could do if they found such a token errantly stored somewhere. 

A lot of the things we think about when we’re working with tokens and trying to limit them fall into the category of limiting what a cyberattacker can do if they get a foothold somewhere. This is the Zero Trust model, where you assume breach everywhere.  

Additionally, it’s essential to use standard libraries to accurately authenticate with tokens, so that all the aspects and limitations of the token are certain to be honored. 

Learn about phishing-resistant multifactor authentication from the Microsoft Secure Future Initiative (SFI). 

4. Good quality authorization  

Good quality tokens are not going to help you if they’re enforced poorly (or not at all). And bugs can creep into code. Ad hoc authorization code can render the good authentication that you’ve done moot. 

Any time you can use declarative style patterns that help you verify tokens against incoming APIs and the data that the client is attempting to access with your API, you’ll find yourself in a better place. Simple, consistent authorization yields fewer bugs and therefore less risk. 

5. Network isolation 

In addition to having good quality tokens, it’s important to isolate the pieces of your environment to the maximum extent possible. Again, this is done because it’s prudent to assume that a cyberattacker has a foothold somewhere in your network. The questions are “where exactly can that foothold be,” and “once they have that foothold, where in my network can they get to?” If a threat actor can reach any part of your system from any other part of your system, this is obviously less good than if your most sensitive systems can be accessed from exactly one or two key places and nowhere else. When properly controlled, most footholds become useless to a cyberattacker—or at least only indirectly useful.  

Use service tags to create boundaries around your various assets such that applications are used by exactly those systems that are supposed to be using them and data is accessed by exactly those applications that are supposed to be accessing the data. This goes a long way to take many cyberthreats off the table.  

Network isolation can happen at several levels in the network stack. Popularly, level 7 is used at the perimeter. Maybe this manifests as some kind of HTTP proxy, for example, or an HTTP routing gateway. However, protection is incomplete without additional work happening at level 3 within your network. You want to limit IP traffic to be going to exactly the places that you want it to go. You might use techniques like virtual LANs, or similar constructs like network security groups (NSGs) in Microsoft Azure. The idea is to limit connectivity to exactly what is necessary to do the job and not give the cyberattacker freedom to move around. 

With good network isolation comes the ability to log any attempts to gain access at the perimeter, and potentially even internally. Depending on what networking technology you’re using, all of this is great for hunting. We’ll talk about that in the next section.  

Learn more about network isolation and other best practices from SFI.

6. Detections  

It’s normal to think about monitoring for reliability. Systems need to stay within their operating parameters in the face of changes and external conditions. But it’s also important to think about detection from the perspective of your threat model. If you identify five or ten risks in your threat model that need controls, it’s useful to think about how you might detect if any of those things are actually happening in your environment.  

In this context, one place to look is at the perimeter—by examining your incoming HTTP traffic, for instance. But you can also look anywhere in your environment where you predict that attacks might happen. You might look for badly formatted requests, or fuzzing, or evidence of DDoS attack—whatever is appropriate to the risks you have. The idea is that you want to be able to create alerts if you have evidence of a threat actor operating in your estate.  

And, of course, security products can be very helpful here.  

7. Auditing

We separate the notions of auditing from detection. Specifically, auditing is what I will call the pieces of data that you would use after a breach to determine the extent of the breach and the customers that were affected by it. In the event that you find a vulnerability without any evidence of threat actor exploitation, you’d want to go and check your auditing again to verify those claims. That way you can have evidence that whatever problem you found was not in fact exploited. If it was exploited, you’ll know to what extent, who was affected, and who needs to be notified. 

Some parts of your endpoint detection and response (EDR) stream will be very useful for auditing. Additional auditing information can come from the logs you create in your applications that record suitable information concerning recent activity. 

8. Things not to miss 

It’s important to think about all the applications and data that you have in your estate. For instance, it’s easy to overlook the backup data that you have stored. A cyberattacker might not be able to get access to your primary systems but might find that your backups are entirely unprotected and they can just read the backup.

Similarly, support systems often go overlooked. There are frequently important customer support scenarios that require access, and it’s easy to fall into the trap of not giving those systems the highest level of scrutiny. 

We should add systems that are under development and test systems to this problematic set. In both these cases, the code that’s running those systems is less trustworthy than normal production code. Development code, for instance, can be presumed to have more bugs than production code. Some of those bugs might be authorization bugs. And if there are authorization bugs, that buggy code might provide access to important assets. Therefore, your plans should include even greater scrutiny when it comes to these kinds of systems. 

Explore actionable patterns and practices from SFI. 

In summary

If you’ve gotten as far as identifying all of your assets, all your applications, and then thinking about the access patterns and controls that you have between them—including authentication, authorization, network isolation, and the use of bug-resistant patterns—you’re in a pretty good place to write a risk summary that can guide your actions for many months. And we haven’t even touched on basic things like vulnerability management, security, bug management, and the usual software lifecycle things that are necessary to keep the system in good health. Combine all of the above and you should have a good-looking risk plan. 

Microsoft
Deputy CISOs

To hear more from Microsoft Deputy CISOs, check out the OCISO blog series:

To stay on top of important security industry updates, explore resources specifically designed for CISOs, and learn best practices for improving your organization’s security posture, join the Microsoft CISO Digest distribution list.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity. 

¹Microsoft Cyber Signals Issue 9. 

²Microsoft Digital Defense Report 2024.

The post 8 best practices for CISOs conducting risk reviews appeared first on Microsoft Security Blog.

That pattern continued as defenders embraced automation and AI to manage expanding digital estates. SOCs were often early scale adopters—using machine learning to reduce noise, improve visibility, and respond faster across growing environments. Cyberattackers became more targeted and multistage, moving deliberately across identities, endpoints, cloud resources, and email, where detection was hardest. Success increasingly depended on moving fast enough to act before analysts could connect the dots. Even with this progress, security operations (SecOps) still feel asymmetrical: threat actors only need to be right once, while defenders are judged by every miss. If defense depends on human intervention to begin, defense will always feel asymmetrical.

To change the outcome, SOCs must change how defense itself works. This is the agentic SOC: where security delivers adaptive, autonomous defense, freeing defenders for strategic, high‑impact work. In this series, we’ll break down what that shift requires, what early experimentation has taught us, and where organizations can start today. Read more about how some organizations moving toward the agentic SOC and access a foundational roadmap for this transformation in our new whitepaper, The agentic SOC: Your teammate for tomorrow, today.

What we mean by “the agentic SOC”

At its core, the agentic SOC is an operating model that shifts security from reacting to incidents to anticipating how cyberattackers move—and actively reshaping the environment to cut off their paths.

It brings together a platform that can increasingly defend itself through built-in autonomous defense, with AI agents working alongside humans to accelerate investigation, prioritization, and action—so teams spend less time on execution and more time on judgment, risk, and the decisions that matter.

How does that change day-to-day work? Imagine a credential theft attempt. Built-in defenses automatically lock the affected account and isolate the compromised device within seconds—before lateral movement can begin. At the same time, an AI agent initiates an investigation, hunting for related activity across identity, endpoint, email, and cloud signals, and correlating everything into a single view.

When an analyst opens their queue, the “noise” of overwhelming alerts is already gone. Evidence has been pre-assembled. Likely next steps are suggested. The analyst can start right away by answering higher impact questions: Is this part of a broader campaign? Should this authentication method be hardened? Are there related techniques this cyberattacker commonly uses that the environment is still exposed to?

In today’s SOC, we see that sequence often takes hours—and the proactive improvement is very limited, if it ever happens; there’s simply not enough time. In an agentic SOC, it happens in minutes, and teams can spend the time they’ve gained on deeper investigation, systemic hardening, and reducing the likelihood of repeat cyberattacks.

A layered model for the agentic SOC

This model works because an agentic SOC is built on two distinct, but interdependent layers. The first is an underlying threat protection platform that has fundamentally evolved how cyberattacks are defended against and disrupted. High confidence cyberthreats are handled automatically through deterministic, policy-bound controls built directly into the platform. Known attack patterns are blocked in real time—without deliberation or creativity—shielding the environment from machine-speed cyberthreats before scarce human attention or token intensive reasoning is required. This disruption layer is not optional; it is the prerequisite that makes an agentic SOC safe, scalable, and sustainable.

The second layer operates at the operational level, where agents take on tough analysis and correlation work to dramatically increase the leverage of security teams and shift focus from uncovering insight to acting on it. These agents reason over evidence, coordinate investigations, orchestrate response across domains, and learn continuously from outcomes. Over time, they help identify recurring attack paths, surface gaps in posture, and recommend changes that make the environment harder to exploit—not just faster to respond.

Together, they transform the SOC from a reactive workflow engine into a resilient system.

What’s real now, and why there’s reason for optimism

The optimism around our view of the agentic SOC comes from operational discipline and proven, real-world impact. Autonomous attack disruption has been operating at scale for years.

Attacks like ransomware are disrupted in an average of three minutes, and tens of thousands of attacks are contained every month by isolating compromised users and devices before lateral movement can take hold. This all done with a 99.99% confidence rating, so SOC teams can trust in its efficacy.

Building on that proven foundation, newer capabilities like predictive shielding extend autonomous defense further—anticipating how cyberattacks are likely to progress and proactively restricting high-risk paths or assets during an intrusion.

Together, these system-level protections show that platforms can safely intervene earlier in the cyberattack chain without introducing unnecessary disruption.

Agentic capabilities are also being similarly scoped. Internally, we’ve been testing task agents for triage and investigations under our expert supervision of our defenders. In live environments, these agents automate 75% of phishing and malware investigations. We’ve also tested agents on more complex analytical tasks, such as assessing exposure to specific vulnerabilities—work that once required a full day of engineering effort and can now be completed in less than an hour by an agent.

How day-to-day SOC work will change in the future

In an agentic SOC, the center of gravity will change for roles like an analyst. Fewer analysts are pulled into firefighting; more time is spent investigating how the organization is being targeted and what steps can be taken to reduce exposure. Within this new operating model, security teams will be freed to evolve the team structure and their day-to-day responsibilities.

Agentic systems increase demand for oversight, tuning, and governance. Detection and response engineering becomes more central, as teams design policies, confidence thresholds, and escalation paths. New roles emerge around supervising outcomes and refining system behavior over time.

Expertise becomes more valuable, not less. Judgment, context, and institutional knowledge are no longer consumed by repetitive tasks—they shape how the SOC operates at scale. And skilled practitioners closer to strategy, quality, and accountability.

To make this shift tangible, here’s how key roles are evolving:

• Analysts: from triaging alerts to supervising outcomes. Analysts validate agent‑led investigations, determine when deeper inquiry is needed, focus on ambiguous cases, and guide system learning over time.
• Detection engineers: from writing rules to teaching the system what matters. Engineers decide which signals are trustworthy, add the right context, and set confidence thresholds so detections can be acted on automatically—without human review every time.
• Threat hunters: from manual queries to hypothesis-driven exploration. Hunters use AI to surface anomalies and focus on creative investigation and adversary simulation.
• SOC leadership: from managing queues to orchestrating autonomy. Leaders define automation policies, oversee governance, and align AI actions with business risk.

Each shift reflects a broader truth: in the agentic SOC, people don’t do less—they do more of what matters.

The agentic SOC journey

This is a significant change in how security teams operate, and it doesn’t happen overnight. Based on our own experience, we’ve outlined a maturity model that shows how organizations can progress toward an agentic SOC over time.

Organizations begin by establishing a trusted foundation that unifies security tooling, enables the deployment of autonomous defense and begins unifying security signal in earnest. From there, they introduce agents to take on bounded, high-volume work under human supervision, learning where automation adds leverage and where judgment still matters most. Over time, as confidence, governance, and operational discipline mature, agents expand from assisting individual workflows to coordinating broader security outcomes. At every stage, progress is measured not by how much work is automated, but by how effectively human expertise is amplified.

SOC 1—Unify your platform foundation

The shift begins with a unified security platform that enables autonomous defense. Deterministic, policy-bound protections stop high confidence cyberthreats automatically—removing urgency, reducing blast radius, and eliminating the constant context switching that slows human response. By integrating signals across identity, endpoints, and cloud, defenders gain a shared view of cyberattacks instead of stitching evidence together across tools. This foundation is what makes cross-domain action possible—and separates experimental automation from production-ready operations.

SOC 2—Accelerate operations with generative AI and task agents

With urgency reduced, generative AI changes how work flows through the SOC. Instead of pushing alerts forward, AI assembles context, synthesizes signals across domains, and produces coherent investigations. Repetitive, high-volume tasks like triage, correlation, and basic investigation are absorbed by the system, allowing analysts to focus on higher impact decisions. This stage establishes new operational patterns where humans and AI work together—accelerating response while preserving judgment and accountability.

SOC 3—Deploy agentic automation

As trust grows, agents move from assistance to action. Specialized agents autonomously orchestrate specific tasks—containing compromised identities, isolating devices, or remediating reported phishing—while humans shift into supervisory roles. Over time, agents help identify patterns, anticipate attack paths, and optimize defenses across the environment. Security teams spend less time managing queues and more time shaping posture, risk, and outcomes. These shifts compound across all three stages.

What comes next for the SOC evolution?

We believe the strongest agentic SOC models will begin with autonomous defense—deterministic, policy‑bound actions that safely stop what is already known to be dangerous at machine speed. That foundation removes urgency, noise, and latency from security operations.

Additionally, agents and humans work differently. Agents assemble context, coordinate remediation, and optimize how the SOC operates. Humans provide intent, judgment, and accountability—turning time saved into smarter, more strategic security outcomes.

This is the first of a series of posts that will explore what makes the agentic SOC model real: the platform foundations required to defend autonomously, the governance and trust mechanisms that keep autonomy safe, and the adoption journey organizations take to get there. Some organizations are already rebuilding their businesses around AI, a new class of Frontier Firms. Read more about how they’re making their move toward the agentic SOC and access a foundational roadmap for this transformation in our new whitepaper, The agentic SOC: Your teammate for tomorrow, today.

Learn more

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity. 

The post The agentic SOC—Rethinking SecOps for the next decade appeared first on Microsoft Security Blog.

This case highlights a growing class of cyberattacks that exploit trust, collaboration platforms, and built-in tooling, and underscores why defenders must be prepared to detect and disrupt these techniques before they escalate. Read the full report to dive deeper into this vishing breach of trust.

What happened?

Once remote interactive access was established, the threat actor shifted from social engineering to hands-on keyboard compromise, steering the user toward a malicious website under their control. Evidence gathered from browser history and Quick Assist artifacts showed the user was prompted to enter corporate credentials into a spoofed web form, which then initiated the download of multiple malicious payloads. One of the earliest artifacts—a disguised Microsoft Installer (MSI) package—used trusted Windows mechanisms to sideload a malicious dynamic link library (DLL) and establish outbound command-and-control, allowing the threat actor to execute code under the guise of legitimate software.

Subsequent payloads expanded this foothold, introducing encrypted loaders, remote command execution through standard administrative tooling, and proxy-based connectivity to obscure threat actor activity. Over time, additional components enabled credential harvesting and session hijacking, giving the threat actor sustained, interactive control within the environment and the ability to operate using techniques designed to blend in with normal enterprise activity rather than trigger overt alarms.

How did Microsoft respond?

Given the growing pattern of identity-first intrusions that begin with collaboration-based social engineering, DART moved quickly to contain risk and validate scope. The team confirmed that the compromise originated from a successful Microsoft Teams voice phishing interaction and immediately prioritized actions to prevent identity or directory-level impact. Through focused investigation, we established that the activity was short-lived and limited in reach, allowing responders to concentrate on early-stage tooling and entry points to understand how access was achieved and constrained.

To disrupt the intrusion, DART conducted targeted eviction and applied tactical containment controls to protect privileged assets and restrict lateral movement. Using proprietary forensic and investigation tooling, the team collected and analyzed evidence across affected systems, validated that threat actor objectives were not met, and confirmed the absence of persistence mechanisms. These actions enabled rapid recovery while helping to ensure the environment was fully secured before declaring the incident resolved.

What can customers do to strengthen their defenses?

Human nature works against us in these cyberattacks. Employees are conditioned to be responsive, helpful, and collaborative, especially when requests appear to come from internal IT or support teams. Threat actors exploit that instinct, using voice phishing and collaboration tools to create a sense of urgency and legitimacy that can override caution in the moment.

To mitigate exposure, DART recommends organizations take deliberate steps to limit how social engineering attacks can propagate through Microsoft Teams and how legitimate remote access tools can be misused. This starts with tightening external collaboration by restricting inbound communications from unmanaged Teams accounts and implementing an allowlist model that permits contact only from trusted external domains. At the same time, organizations should review their use of remote monitoring and management tools, inventory what is truly required, and remove or disable utilities—such as Quick Assist—where they are unnecessary.

Together, these measures help shrink the attack surface, reduce opportunities for identity-driven compromise, and make it harder for threat actors to turn human trust into initial access, while preserving the collaboration employees rely on to do their work.

What is the Cyberattack Series?

In our Cyberattack Series, customers discover how DART investigates unique and notable attacks. For each cyberattack story, we share:

• How the cyberattack happened.
• How the breach was discovered.
• Microsoft’s investigation and eviction of the threat actor.
• Strategies to avoid similar cyberattacks.

DART is made up of highly skilled investigators, researchers, engineers, and analysts who specialize in handling global security incidents. We’re here for customers with dedicated experts to work with you before, during, and after a cybersecurity incident.

Learn more

To learn more about DART capabilities, please visit our website, or reach out to your Microsoft account manager or Premier Support contact. To learn more about the cybersecurity incidents described above, including more insights and information on how to protect your own organization, download the full report.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

¹Microsoft Digital Defense Report 2025.

The post Help on the line: How a Microsoft Teams support call led to compromise appeared first on Microsoft Security Blog.

Today, we’re continuing that conversation. With the latest Microsoft benchmarking data, we’re sharing what real-world telemetry reveals about how effectively modern email threats are detected, mitigated, and stopped by Microsoft Defender, secure email gateway (SEG) providers, and integrated cloud email security (ICES) solutions.

This is part of our ongoing commitment to openness: regularly publishing performance data so customers can see how protections perform at scale.

What’s new in the latest benchmarking data

The newest benchmarking results reflect updated telemetry across recent months and reinforce several consistent trends:

• Microsoft Defender removes an average of 70.8% of malicious email post-delivery, helping reduce dwell time even when cyberthreats bypass initial filtering.
• Layered protection matters. When Defender operates alongside ICES partners, organizations benefit from incremental detection gains across promotional, spam, and malicious messages.
• Overlapping detections remain, meaning ICES solutions can flag the same messages and the incremental value-add can vary by scenario and email type.

This kind of data-driven visibility is critical for security teams who want to understand not just whether cyberthreats are blocked, but how and where defenses are adding value across the email attack lifecycle.

Benchmarking results for ICES vendors

Microsoft’s quarterly analysis shows that layering ICES solutions with Microsoft Defender continue to provide a benefit in reducing marketing and bulk email, improving their filtering by an average of 13.7%. This reduces inbox clutter and boosts user productivity in environments with high volumes of promotional email. For filtering of spam and malicious messages, the incremental gains remain modest, and the latest quarter shows a smaller uplift than the prior period—averaging 0.29% and 0.24% respectively, compared to 1.65% and 0.5% in the prior report.

Focusing only on malicious messages that reached the inbox, the latest quarter shows Microsoft Defender’s zero hour auto purge performing the majority of post‑delivery remediation—removing an average of 70.8% of these threats. ICES vendors provided additional post‑delivery filtering, contributing an average of 29.2%. Together, this highlights two points: post‑delivery remediation is a critical backstop when cyberthreats evade initial filtering, and in these results Microsoft Defender delivered most of the post‑delivery catch, while ICES vendors add incremental coverage in this scenario.

Benchmarking results for SEG vendors

For the SEG vendor benchmarking metrics, a cyberthreat was classified as “missed” if it was not detected prior to delivery. Using this definition, Microsoft Defender missed fewer high-severity cyberthreats than other solutions evaluated in the study, consistent with patterns observed in our prior benchmarking report.

Reinforcing our commitment to the ICES vendor ecosystem

Transparency doesn’t stop at Microsoft’s own detections. It also extends to how we work with partners.

When we introduced the Microsoft Defender for Office 365 ICES vendor ecosystem, our goal was clear: enable customers to integrate trusted, non-Microsoft email security solutions into a unified Defender experience, without fragmenting workflows or visibility.

That commitment continues today.

• The ICES vendor ecosystem now includes four partners—Darktrace, KnowBe4, Cisco, and VIPRE Security Group—all integrated directly into Microsoft Defender across experiences such as Quarantine, Explorer, email entity pages, advanced hunting, and reporting.
• Customers retain a single operational plane in the Defender portal, even when layering multiple email security technologies.
• Integrations are deliberate and additive, designed to enhance protection and clarity without increasing operational complexity.
• The ecosystem supports defense-in-depth strategies while preserving a single, coherent security experience.

The recent additions reinforce our belief that email security is strongest when it combines native platform intelligence with specialized partner capabilities, surfaced through a single pane of glass.

We continue to actively evaluate additional partnerships based on customer demand, detection quality, and the ability to deliver meaningful, differentiated signals.

Why this matters for security teams

Email remains one of the most targeted and exploited attack vectors, and modern campaigns rarely rely on a single technique or control gap.

By pairing transparent benchmarking with integrated, multi-vendor protection, security teams gain:

• Clear insight into detection coverage across native and partner solutions.
• Reduced investigation friction with unified views and workflows.
• Confidence in layered defenses, backed by regularly published data.

This isn’t about claiming perfection. It’s about showing the work, sharing the numbers, and giving customers the information they need to make informed security decisions.

Looking ahead

We’ll continue to publish updated benchmarking insights on a regular basis, alongside ongoing investments in Microsoft Defender and the ICES vendor ecosystem.

To explore the latest benchmarking data and learn more about how Defender and ICES partners work together, access the benchmarking site.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

¹Clarity in complexity: New insights for transparent email security, Microsoft. December 10, 2025.

The post From transparency to action: What the latest Microsoft email security benchmark reveals appeared first on Microsoft Security Blog.

As our customers rapidly embrace agentic AI, chief information officers (CIOs), chief information security officers (CISOs), and security decision makers are asking urgent questions: How do I track and monitor all these agents? How do I know what they are doing? Do they have the right access? Can they leak sensitive data? Are they protected from cyberthreats? How do I govern them?

Agent 365 and Microsoft 365 E7: The Frontier Suite, generally available on May 1, 2026, are designed to help answer these questions and give organizations the confidence to go further with AI.

Agent 365—the control plane for agents

As organizations adopt agentic AI, growing visibility and security gaps can increase the risk of agents becoming double agents. Without a unified control plane, IT, security, and business teams lack visibility into which agents exist, how they behave, who has access to them, and what potential security risks exist across the enterprise. With Microsoft Agent 365 you now have a unified control plane for agents that enables IT, security, and business teams to work together to observe, govern, and secure agents across your organization—including agents built with Microsoft AI platforms and agents from our ecosystem partners—using new Microsoft Security capabilities built into their existing flow of work.

Here is what that looks like in practice:

As we are now running Agent 365 in production, Avanade has real visibility into agent activity, the ability to govern agent sprawl, control resource usage, and manage agents as identity-aware digital entities in Microsoft Entra. This significantly reduces operational and security risk, represents a critical step forward in operationalizing the agent lifecycle at scale, and underscores Microsoft’s commitment to responsible, production-ready AI.

—Aaron Reich, Chief Technology and Information Officer, Avanade

Key Agent 365 capabilities include:

Observability for every role

With Agent 365, IT, security, and business teams gain visibility into all Agent 365 managed agents in their environment, understand how they are used, and can act quickly on performance, behavior, and risk signals relevant to their role—from within existing tools and workflows.

• Agent Registry provides an inventory of agents in your organization, including agents built with Microsoft AI platforms, ecosystem partner agents, and agents registered through APIs. This agent inventory is available to IT teams in the Microsoft 365 admin center. Security teams see the same unified agent inventory in their existing Microsoft Defender and Purview workflows.
• Agent behavior and performance observability provides detailed reports about agent performance, adoption and usage metrics, an agent map, and activity details.
• Agent risk signals across Microsoft Defender*, Entra, and Purview* help security teams evaluate agent risk—just like they do for users—and block agent actions based on agent compromise, sign-in anomalies, and risky data interactions. Defender assesses risk of agent compromise, Entra evaluates identity risk, and Purview evaluates insider risk. IT also has visibility into these risks in the Microsoft 365 admin center.
• Security policy templates, starting with Microsoft Entra, automate collaboration between IT and security. They enable security teams to define tenant-wide security policies that IT leaders can then enforce in the Microsoft 365 admin center as they onboard new agents.

*These capabilities are in public preview and will continue to be on May 1.

Secure and govern agent access

Unmanaged agents may create significant risk, from accessing resources unchecked to accumulating excessive privileges and being misused by malicious actors. With Microsoft Entra capabilities included in Agent 365, you can secure agent identities and their access to resources.

• Agent ID gives each agent a unique identity in Microsoft Entra, designed specifically for the needs of agents. With Agent ID, organizations can apply trusted access policies at scale, reduce gaps from unmanaged identities, and keep agent access aligned to existing organizational controls.
• Identity Protection and Conditional Access for agents extend existing user policies that make real-time access decisions based on risks, device compliance from Microsoft Intune, and custom security attributes to agents working on behalf of a user. These policies help prevent compromise and help ensure that agents cannot be misused by malicious actors.
• Identity Governance for agents enables identity leaders to limit agent access to only resources they need, with access packages that can be scoped to a subset of the users permissions, and includes the ability to audit access granted to agents.

Prevent data oversharing and ensure agent compliance

Microsoft Purview capabilities in Agent 365 provide comprehensive data security and compliance coverage for agents. You can protect agents from accessing sensitive data, prevent data leaks from risky insiders, and help ensure agents process data responsibly to support compliance with global regulations.

• Data Security Posture Management provides visibility and insights into data risks for agents so data security admins can proactively mitigate those risks.
• Information Protection helps ensure that agents inherit and honor Microsoft 365 data sensitivity labels so that they follow the same rules as users for handling sensitive data to prevent agent-led sensitive data leaks.
• Inline Data Loss Prevention (DLP) for prompts to Microsoft Copilot Studio agents blocks sensitive information such as personally identifiable information, credit card numbers, and custom sensitive information types (SITs) from being processed in the runtime.
• Insider Risk Management extends insider risk protection to agents to help ensure that risky agent interactions with sensitive data are blocked and flagged to data security admins.
• Data Lifecycle Management enables data retention and deletion policies for prompts and agent-generated data so you can manage risk and liability by keeping the data that you need and deleting what you don’t.  
• Audit and eDiscovery extend core compliance and records management capabilities to agents, treating AI agents as auditable entities alongside users and applications. This will help ensure that organizations can audit, investigate, and defensibly manage AI agent activity across the enterprise.
• Communication Compliance extends to agent interactions to detect and enable human oversight of risky AI communications. This enables business leaders to extend their code of conduct and data compliance policies to AI communications.

Defend agents against emerging cyberthreats

To help you stay ahead of emerging cyberthreats, Agent 365 includes Microsoft Defender protections purpose-built to detect and mitigate specific AI vulnerabilities and threats such as prompt manipulation, model tampering, and agent-based attack chains.

• Security posture management for Microsoft Foundry and Copilot Studio agents* detects misconfigurations and vulnerabilities in agents so security leaders can stay ahead of malicious actors by proactively resolving them before they become an attack vector.
• Detection, investigation, and response for Foundry and Copilot Studio agents* enables the investigation and remediation of attacks that target agents and helps ensure that agents are accounted for in security investigations.
• Runtime threat protection, investigation, and hunting** for agents that use the Agent 365 tools gateway, helps organizations detect, block, and investigate malicious agent activities.

Agent 365 will be generally available on May 1, 2026, and priced at $15 per user per month. Learn more about Agent 365.

*These capabilities are in public preview and will continue to be on May 1.

**This new capability will enter public preview in April 2026 and continue to be on May 1.

Microsoft 365 E7: The Frontier Suite

Microsoft 365 E7 brings together intelligence and trust to enable organizations to accelerate Frontier Transformation, equipping employees with AI across email, documents, meetings, spreadsheets, and business application surfaces. It also gives IT and security leaders the observability and governance needed to operate AI at enterprise scale.

Microsoft 365 E7 includes Microsoft 365 Copilot, Agent 365, Microsoft Entra Suite, and Microsoft 365 E5 with advanced Defender, Entra, Intune, and Purview security capabilities to help secure users, delivering comprehensive protection across users and agents. It will be available for purchase on May 1, 2026, at a retail price of $99 per user per month. Learn more about Microsoft 365 E7.

End-to-end security for the agentic era

Frontier Transformation is anchored in intelligence and trust, and trust starts with security. Microsoft Security capabilities help protect 1.6 million customers at the speed and scale of AI.¹ With Agent 365, we are extending these enterprise-grade capabilities so organizations can observe, secure, and govern agents and delivering comprehensive protection across agents and users with Microsoft 365 E7.

Secure your Frontier Transformation today with Agent 365 and Microsoft 365 E7: The Frontier Suite. And join us at RSAC Conference 2026 to learn more about these new solutions and hear from industry experts and customers who are shaping how agents can be observed, governed, secured, and trusted in the real world.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

¹Microsoft Fiscal Year 2026 Second Quarter Earnings Conference Call.

The post Secure agentic AI for your Frontier Transformation appeared first on Microsoft Security Blog.

Throughout my career, I’ve been inspired by extraordinary women leaders—trailblazers who broke barriers, opened doors, and reshaped what leadership in technology looks like. But today, I want to shine a light on another group that inspires me just as deeply: women early in their careers—the builders, learners, and question-askers who are defining the future of cybersecurity and developing their skills in the era of AI.

These women are entering the field at a moment of unprecedented complexity. Cyberthreats are accelerating. AI is reshaping how we defend, detect, and respond. And the stakes—for trust, safety, and resilience—have never been higher.

That’s exactly why it has never been more critical to have a wide range of experiences and perspectives in our defender community.

Be Cybersmart

Help educate everyone in your organization with cybersecurity awareness resources and training curated by the security experts at Microsoft.

Get the Be Cybersmart Kit.

Why diversity of perspectives is not optional in cybersecurity

Cybersecurity is fundamentally about understanding people—how they behave, how they make decisions, how systems can be misused, and where harm can occur. That’s why diversity of perspectives, backgrounds, experiences, and people is a security imperative.

The ISACA paper titled “The Value of Diversity and Inclusion in Cybersecurity” concludes that cybersecurity teams lacking diversity are at greater risk of engaging in limited threat modeling, exhibiting reduced innovation, and making less robust decisions in complex security environments. At Microsoft Security, we recognize that the cyberthreats we encounter are as varied and multifaceted as humanity itself.

To stay ahead, our teams must reflect that diversity across gender, background, culture, discipline, and lived experience.

When teams bring different perspectives to the table,

• They ask better questions;
• They surface risks earlier;
• They design systems that work for more people;
• And they build security that is resilient by design.

The power of women early in career and beyond

Women early in their career bring something incredibly powerful to cybersecurity and AI: fresh perspective paired with fearless curiosity. Women bring empathy, clarity, systems thinking, and collaborative leadership that directly strengthen our ability to detect cyberthreats, understand human behavior, and build secure products that work for everyone.

This makes me think of my valued friend and colleague, Lauren Buitta, who is the founder and chief executive officer (CEO) of Girl Security. Lauren has been a tireless advocate for providing women early in career—especially those from underrepresented backgrounds, with the skills and confidence needed to enter security careers. She often says, “Security isn’t just a discipline—it’s empowerment through knowledge.” That philosophy extends to Girl Security’s work preparing the next generation to navigate and lead in an AI-powered world. Her efforts show us that nurturing curiosity early on can have lasting effects throughout life.

They challenge assumptions that may no longer hold. They ask “why” before accepting “how.” They’re often the first to notice gaps—in data, in design, in who is represented and who is missing. Supporting women at this stage isn’t just about equity. It’s about strengthening the future of security itself. These actions build a stronger, more resilient security ecosystem.

Building and cultivating pathways for the next generation

Investing in women early in their cybersecurity and AI security careers is essential. Early access to education, opportunity, and confidence building experiences helps more women see themselves in this field—and choose to stay.

But if we stop there, we shouldn’t be surprised when the numbers don’t move.  In fact, independent global analyses from the Global Cybersecurity Forum and Boston Consulting Group show that women represent just 24% of the cybersecurity workforce worldwide—a figure reinforced by LinkedIn’s real-time labor market data. What I’ve realized is this: To change outcomes, we have to cultivate women throughout their careers—from first exposure to technical mastery, from early roles to leadership, and from individual contributor to decisionmaker. Otherwise, we’ll continue to bring women into the field without creating the conditions that allow them to grow, advance, and remain.

That means pairing early career investment with sustained support, inclusive cultures, and everyday actions that reinforce belonging and opportunity over time.

Here are meaningful steps we can all take—not just to widen the pipeline, but to strengthen it end to end:

1. Share stories from a diverse set of role models at every career stage.
Representation fuels imagination. When women early in career see themselves reflected in cybersecurity, they’re more likely to enter the field. When women midcareer and in senior roles see paths forward, they’re more likely to stay and lead.

2. Reevaluate job descriptions at entry and beyond.
Rigid expectations or narrow definitions of technical expertise discourage qualified candidates from applying, and can also limit progression into advanced or leadership roles.

3. Invest in inclusive training and early career programs and sustain learning over time.
Accessible, hands-on learning builds confidence early. Continued upskilling, reskilling, and leadership development ensure women can evolve alongside rapidly changing security and AI technologies.

4. Volunteer with organizations driving cybersecurity and AI education.
Groups like Girl Security and Women in CyberSecurity (WiCyS) are changing outcomes for thousands of girls and women. Your time, mentorship, or sponsorship helps build momentum early—and reinforces pathways later. I welcome you to join Nicole Ford, Vice President Customer Security Officer at Microsoft, who will be hosting a leadership lunch at the WiCyS conference to discuss cultivating leaders for the future and though advocacy and sponsorship.

5. Partner with community groups offering mentorship and sponsorship opportunities.
Mentorship is one of the strongest predictors of early career success. Sponsorship—advocacy that opens doors to stretch roles, visibility, and advancement—is critical for long term progression.

6. Be an ally every day across the full career journey.
Introduce emerging talent to your networks. Encourage them to speak up. Create space for them to lead. Advocate for their ideas in rooms they aren’t in yet—especially as stakes and visibility increase.

Our commitment—and our opportunity

At Microsoft, our mission is to empower every person and every organization on the planet to achieve more. That starts by ensuring the next generation of cybersecurity and AI security professionals has equitable access to opportunity, education, and belonging.

This Women’s History Month, let’s celebrate not only the women who have led the way — but the women who are just getting started.

They’re actively shaping security today, not just influencing its future. Security is a team sport and we need everyone in this team because together, we can build a safer, more inclusive digital future for all.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

The post Women’s History Month: Encouraging women in cybersecurity at every career stage appeared first on Microsoft Security Blog.

We cannot anticipate every misuse or emergent behavior in AI systems. We can, however, identify what can go wrong, assess how bad it could be, and design systems that help reduce the likelihood or impact of those failure modes. That is the role of threat modeling: a structured way to identify, analyze, and prioritize risks early so teams can prepare for and limit the impact of real‑world failures or adversarial exploits.

Traditional threat modeling evolved around deterministic software: known code paths, predictable inputs and outputs, and relatively stable failure modes. AI systems (especially generative and agentic systems) break many of those assumptions. As a result, threat modeling must be adapted to a fundamentally different risk profile.

Why AI changes threat modeling

Generative AI systems are probabilistic and operate over a highly complex input space. The same input can produce different outputs across executions, and meaning can vary widely based on language, context, and culture. As a result, AI systems require reasoning about ranges of likely behavior, including rare but high‑impact outcomes, rather than a single predictable execution path.

This complexity is amplified by uneven input coverage and resourcing. Models perform differently across languages, dialects, cultural contexts, and modalities, particularly in low‑resourced settings. These gaps make behavior harder to predict and test, and they matter even in the absence of malicious intent. For threat modeling teams, this means reasoning not only about adversarial inputs, but also about where limitations in training data or understanding may surface failures unexpectedly.

Against this backdrop, AI introduces a fundamental shift in how inputs influence system behavior. Traditional software treats untrusted input as data. AI systems treat conversation and instruction as part of a single input stream, where text—including adversarial text—can be interpreted as executable intent. This behavior extends beyond text: multimodal models jointly interpret images and audio as inputs that can influence intent and outcomes.

As AI systems act on this interpreted intent, external inputs can directly influence model behavior, tool use, and downstream actions. This creates new attack surfaces that do not map cleanly to classic threat models, reshaping the AI risk landscape.

Three characteristics drive this shift:

• Nondeterminism: AI systems require reasoning about ranges of behavior rather than single outcomes, including rare but severe failures.
• Instruction‑following bias: Models are optimized to be helpful and compliant, making prompt injection, coercion, and manipulation easier when data and instructions are blended by default.
• System expansion through tools and memory: Agentic systems can invoke APIs, persist state, and trigger workflows autonomously, allowing failures to compound rapidly across components.

Together, these factors introduce familiar risks in unfamiliar forms: prompt injection and indirect prompt injection via external data, misuse of tools, privilege escalation through chaining, silent data exfiltration, and confidently wrong outputs treated as fact.

AI systems also surface human‑centered risks that traditional threat models often overlook, including erosion of trust, overreliance on incorrect outputs, reinforcement of bias, and harm caused by persuasive but wrong responses. Effective AI threat modeling must treat these risks as first‑class concerns, alongside technical and security failures.

Start with assets, not attacks

Effective threat modeling begins by being explicit about what you are protecting. In AI systems, assets extend well beyond databases and credentials.

Common assets include:

• User safety, especially when systems generate guidance that may influence actions.
• User trust in system outputs and behavior.
• Privacy and security of sensitive user and business data.
• Integrity of instructions, prompts, and contextual data.
• Integrity of agent actions and downstream effects.

Teams often under-protect abstract assets like trust or correctness, even though failures here cause the most lasting damage. Being explicit about assets also forces hard questions: What actions should this system never take? Some risks are unacceptable regardless of potential benefit, and threat modeling should surface those boundaries early.

Understand the system you’re actually building

Threat modeling only works when grounded in the system as it truly operates, not the simplified version of design docs.

For AI systems, this means understanding:

• How users actually interact with the system.
• How prompts, memory, and context are assembled and transformed.
• Which external data sources are ingested, and under what trust assumptions.
• What tools or APIs the system can invoke.
• Whether actions are reactive or autonomous.
• Where human approval is required and how it is enforced.

In AI systems, the prompt assembly pipeline is a first-class security boundary. Context retrieval, transformation, persistence, and reuse are where trust assumptions quietly accumulate. Many teams find that AI systems are more likely to fail in the gaps between components — where intent and control are implicit rather than enforced — than at their most obvious boundaries.

Model misuse and accidents 

AI systems are attractive targets because they are flexible and easy to abuse. Threat modeling has always focused on motivated adversaries:

• Who is the adversary?
• What are they trying to achieve?
• How could the system help them (intentionally or not)?

Examples include extracting sensitive data through crafted prompts, coercing agents into misusing tools, triggering high-impact actions via indirect inputs, or manipulating outputs to mislead downstream users.

With AI systems, threat modeling must also account for accidental misuse—failures that emerge without malicious intent but still cause real harm. Common patterns include:

• Overestimation of Intelligence: Users may assume AI systems are more capable, accurate, or reliable than they are, treating outputs as expert judgment rather than probabilistic responses.
• Unintended Use: Users may apply AI outputs outside the context they were designed for, or assume safeguards exist where they do not.
• Overreliance: When users accept incorrect or incomplete AI outputs, typically because AI system design makes it difficult to spot errors.

Every boundary where external data can influence prompts, memory, or actions should be treated as high-risk by default. If a feature cannot be defended without unacceptable stakeholder harm, that is a signal to rethink the feature, not to accept the risk by default.

Use impact to determine priority, and likelihood to shape response

Not all failures are equal. Some are rare but catastrophic; others are frequent but contained. For AI systems operating at a massive scale, even low‑likelihood events can surface in real deployments.

Historically risk management multiplies impact by likelihood to prioritize risks. This doesn’t work for massively scaled systems. A behavior that occurs once in a million interactions may occur thousands of times per day in global deployment. Multiplying high impact by low likelihood often creates false comfort and pressure to dismiss severe risks as “unlikely.” That is a warning sign to look more closely at the threat, not justification to look away from it.

A more useful framing separates prioritization from response:

• Impact drives priority: High-severity risks demand attention regardless of frequency.
• Likelihood shapes response: Rare but severe failures may rely on manual escalation and human review; frequent failures require automated, scalable controls.

Every identified threat needs an explicit response plan. “Low likelihood” is not a stopping point, especially in probabilistic systems where drift and compounding effects are expected.

Design mitigations into the architecture

AI behavior emerges from interactions between models, data, tools, and users. Effective mitigations must be architectural, designed to constrain failure rather than react to it.

Common architectural mitigations include:

• Clear separation between system instructions and untrusted content.
• Explicit marking or encoding of untrusted external data.
• Least-privilege access to tools and actions.
• Allow lists for retrieval and external calls.
• Human-in-the-loop approval for high-risk or irreversible actions.
• Validation and redaction of outputs before data leaves the system.

These controls assume the model may misunderstand intent. Whereas traditional threat modeling assumes that risks can be 100% mitigated, AI threat modeling focuses on limiting blast radius rather than enforcing perfect behavior. Residual risk for AI systems is not a failure of engineering; it is an expected property of non-determinism. Threat modeling helps teams manage that risk deliberately, through defense in depth and layered controls.

Detection, observability, and response

Threat modeling does not end at prevention. In complex AI systems, some failures are inevitable, and visibility often determines whether incidents are contained or systemic.

Strong observability enables:

• Detection of misuse or anomalous behavior.
• Attribution to specific inputs, agents, tools, or data sources.
• Accountability through traceable, reviewable actions.
• Learning from real-world behavior rather than assumptions.

In practice, systems need logging of prompts and context, clear attribution of actions, signals when untrusted data influences outputs, and audit trails that support forensic analysis. This observability turns AI behavior from something teams hope is safe into something they can verify, debug, and improve over time.

 Response mechanisms build on this foundation. Some classes of abuse or failure can be handled automatically, such as rate limiting, access revocation, or feature disablement. Others require human judgment, particularly when user impact or safety is involved. What matters most is that response paths are designed intentionally, not improvised under pressure.

Threat modeling as an ongoing discipline

AI threat modeling is not a specialized activity reserved for security teams. It is a shared responsibility across engineering, product, and design.

The most resilient systems are built by teams that treat threat modeling as one part of a continuous design discipline — shaping architecture, constraining ambition, and keeping human impact in view. As AI systems become more autonomous and embedded in real workflows, the cost of getting this wrong increases.

Get started with AI threat modeling by doing three things:

1. Map where untrusted data enters your system.
2. Set clear “never do” boundaries.
3. Design detection and response for failures at scale.

As AI systems and threats change, these practices should be reviewed often, not just once. Thoughtful threat modeling, applied early and revisited often, remains an important tool for building AI systems that better earn and maintain trust over time

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

The post Threat modeling AI applications appeared first on Microsoft Security Blog.

The new e-book, Unlocking Microsoft Defender: A guide to autonomous defense and expert-led security, explores why this model has become unsustainable and how organizations can shift to a more integrated approach to modern defense. Implementing genuine SOC transformation is no easy task, and many organizations seek outside expertise to affect real change. Sign up to download the e-book now and learn more about topics like how autonomous defense paired with human judgment can help organizations tackle today’s toughest cyberthreats, and how adding services from Microsoft Security Experts can help defend against threats, build cyber resilience, and modernize security operations.

Why autonomous defense is now the standard

To keep pace with this new class of threat actor, security teams need to move beyond incremental automation and fundamentally rethink how defense operates. For years, SOCs have relied on manual triage—analysts chasing large volumes of low confidence alerts across disconnected tools. Security orchestration, automation, and response (SOAR) platforms improved efficiency by automating known responses, but they remain reactive by design, engaging only after an incident has already taken shape. This model struggles when attacks unfold in minutes, not days.

The next evolution is an agentic SOC—one where defense is driven by continuous signal correlation, automated decision making, and human expertise applied where it matters most. Microsoft Defender XDR provides a unified operational layer across domains, closing visibility gaps created by siloed tools and enabling automated disruption of complex attacks before they escalate. By shifting routine investigation and response to AI-powered agents, security teams can reduce response time, contain cyberthreats earlier, and refocus human effort on proactive hunting, strategic analysis, and resilience rather than constant firefighting.

The blueprint for autonomous defense

The shift toward autonomous defense starts with unifying how security operations work. Fragmented tools force teams to interpret cyberthreats one signal at a time, leaving context scattered and response uneven. The guide explores how coordinated defense brings threat signals and protection actions together, revealing patterns that individual alerts may never reveal on their own. Instead of adjudicating noise, teams gain clear attack narratives that support faster, more confident decisions.

Autonomous defense builds on that foundation by using AI to act early in the attack lifecycle—not after damage is done. The e-book examines how modern platforms can contain in-progress threats and anticipate attacker movement, reducing reliance on manual escalation and static response models. The result is a SOC that spends less time reacting to incidents and more time shaping security outcomes—an operating model designed for speed, scale, and the inevitability of attack.

See how Microsoft Security Experts uncover fake remote workers

In the e‑book, we explore how autonomous defense is most effective when paired with human judgment and deep experience managing real incidents. Automated protection serves as the foundational security layer, blocking cyberthreats at machine speed, and reducing operational strain. When cyberattacks evolve or escalate, expert‑led hunting and managed detection and response bring global threat intelligence and real‑world insight to contain incidents and strengthen defenses. Human insights feed back into the platform, continuously improving automated protections and sharpening the organization’s overall security posture. In this video, we share a story of how fake profiles and fabricated identities can sometimes appear all too real.

Turn autonomous defense into resilient security

The e-book includes information about how organizations layer expertise at every stage of modern defense—combining autonomous protection with continuous human insight. Microsoft Security Experts helps in three key ways: with technical advisory to help modernize security operations, managed extended detection and response for around the clock defense against cyberthreats, and incident response and planning to build cyber resilience. The e-book further explains how this model emphasizes earlier threat discovery, reduced noise, and faster, more confident decision‑making as part of day‑to‑day security operations.

Sign up to download the e-book and read about how intelligence‑led incident response and direct access to security advisors can help organizations build long‑term resilience—not just recover from individual incidents. With expert guidance on readiness, response, and platform optimization, security teams can modernize operations, reduce integration overhead, and measurably improve outcomes. The result is a more resilient security program—one that resolves cyberthreats faster, lowers breach risk, consolidates cost, and enables teams to focus on solving meaningful security problems rather than chasing alerts.

Learn more about the Microsoft Defender Experts Suite

As security teams confront faster, more complex cyberattacks—and persistent gaps in skills and capacity—many are looking for practical ways to strengthen defenses without adding operational strain. The Microsoft Defender Experts Suite provides expert‑led security services to help organizations defend against advanced cyberthreats, improve resilience, and modernize security operations. If you’re exploring how to combine autonomous protection with continuous human expertise, read the full announcement for deeper context on what’s new and how these services work together.

Learn more

Learn more about Microsoft Security Experts and Microsoft Defender XDR.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity. 

¹Microsoft and Omdia, State of the SOC: Unify Now or Pay Later report, 2026.

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