They decided to migrate their 10-year-old architecture and technology stack to a cloud-based solution to support their long-term goals and operate more efficiently. The question was how to make the move while also keeping the power on.  

At Microsoft for Government, we work with organizations and agencies around the world to help solve these kinds of challenges. We help each organization navigate their unique requirements and chart a path that works best for them, balancing the promise of AI and cloud native applications with the need to be more cost efficient, secure, and compliant.  

For Energinet, the solution was to build a new digital operating system based on Microsoft Azure, which is improving efficiency in automating energy balancing processes, lowering costs, and resolving issues in 15 minutes that previously took an hour. Modernizing helped them meet their near-term requirements, and it positioned them to remain agile and open to change as opportunities evolve.  

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Migrate to innovate—the benefits of modernization

In many government organizations, the migration to cloud computing has long been mitigated by important concerns, including cost, security, and a need to maximize legacy systems. As cloud technology has matured, however, the barriers to adoption have declined as the price of inaction has risen.  

With the promise of AI to deliver new efficiencies and opportunities for service improvements, cloud migration is now clearly the best path forward, provided it is done in ways that meet an organization’s unique requirements.  

For government organization, the advantages of cloud computing are compelling, and easier to realize than before. Governments that choose to migrate to Azure are realizing important benefits in many areas of their greatest concerns—among them: 

• Performance and resilience: Azure enables businesses to scale their operations globally with ease, with purpose-built infrastructure, dynamic compute capacity, scalable storage, and real-time disaster-recovery options. Governments such as the State of Alaska are migrating to Azure to achieve their vision of becoming digital public service innovators. To expand access to secure services, Alaska migrated 700 applications and one-third of its infrastructure in just three months. They not only achieved better resilience, cost efficiency, and security, but with the state’s vast geography and often isolated communities, the migration also sparked a cultural shift by bringing agencies together and unlocking unexpected value. 

• Security: To counter the expanding cybersecurity threat facing governments, Azure is supported by more than 8,500 security experts, more than 100 compliance certifications, and a cloud-native application protection platform that spans the application and infrastructure stack. Microsoft plans to invest USD$20 billion in security in the next five years to continue our commitment to a safe future.¹ The importance of ensuring world-class security was a key factor in the decision by Qatar’s Ministry of Communications and Information Technology to digitize government operations with Azure. The ministry established information assurance measures and security programs that not only enhanced the government’s data security and operational efficiency, but also achieved $7.3 million in cost savings. 

• Hybrid and multi-cloud management: Azure supports hybrid, multi-cloud, and edge environments with Microsoft Azure Arc, a solution that allows governments to build applications and services with a consistent development, operations, and security model across deployments. This proved essential for the World Bank, which provides lending services in 189 developing countries around the world to help lift people out of poverty. They wanted to build applications and services that could extract insights from their SQL Server estate and multiple cloud infrastructure providers. Using Azure Arc, they streamlined their cloud migration journey, built new solutions, and gained unexpected efficiencies. 

• Cost savings: By migrating to Azure, customers can optimize costs and resources by consolidating solutions and choosing from a variety of consumption models and flexible migration approaches. For example, the Statistical Office of Republic of Serbia saved time and money by conducting the nation’s first-ever paperless census using a hybrid cloud solution, which reduced the time required to publish official results from 18 months to just six. The solution delivered faster data encoding and more accurate results, and the Statistical Office of Republic of Serbia was able to streamline maintenance while ensuring optimal security, real-time monitoring, and improved data quality.  

A 3-step approach to becoming AI-ready

Modernization, which provides for greater scale, efficiency, and flexibility, also positions an organization to explore the benefits of AI. Governments recognize the potential of AI to generate new cost efficiencies and to power new offerings in service delivery. In the near term, this is motivating many to accelerate their digital transformation journeys. 

Implementing a cloud migration strategy is an absolute prerequisite to adopting and innovating with AI in a government organization. The cloud provides the hyperscale performance required for generative AI functionality, and a modern data strategy not only consolidates disparate data systems but also ensures access control and data security.  

Cloud migration is a long-term process, and the journey is unique to every organization. Whatever the course, governments should remain mindful of the following three steps, which are key to becoming AI-ready in ways that are efficient, effective, and responsible. 

Step 1: Co-locate data and workloads in the cloud

Strategically placing applications, databases, and AI resources into the Microsoft Cloud ecosystem delivers exponential improvements in performance and prepares data and services to take advantage of new AI innovations.  

Step 2: Infuse Microsoft Azure OpenAI Service and copilot integrations

Once your data and applications are co-located, you are ready to take advantage of AI services such as Microsoft Copilot for Microsoft 365, which integrates generative AI into everyday productivity applications, and Azure OpenAI, which enables the development of customized copilots, plugins, integrated AI services, and much more.  

Step 3: Ensure secure and responsible AI

From the outset, AI innovation should be delivered with the highest standards for security, assurance, and trust. Beyond taking a leadership role in ensuring safe, secure, and trustworthy AI at a global level, Microsoft provides comprehensive guidance for governments to ensure secure and responsible AI in their efforts, such as the Microsoft Responsible AI Standard, and Microsoft responsible AI practices.   

Continue your modernization journey and become AI ready 

No matter where your government organization stands in its digital transformation, Microsoft and our network of global partners are ready to help you move forward. For more, please explore the following resources: 

• To learn how Microsoft is helping governments solve society’s biggest challenges, see our Microsoft in Government website.  

• For guidance, expert assistance, and resources to help move to Azure, visit Azure Migrate and Modernize and Azure Innovate.  

• To develop a cost management strategy when migrating or operating in the cloud, see Cloud Migration Cost Management Resources.  

¹Microsoft commits $20 billion to advance cybersecurity following meeting with President Biden, Windows Central.

The post Migrate to innovate: How governments are modernizing in advance of AI appeared first on Microsoft Industry Blogs.

The breadth to which Azure OpenAI technologies can be applied is increasing exponentially but must be applied responsibly and in accordance with responsible AI principles and policies.  

As a former defense leader, this blog considers the breadth of opportunities and will highlight three use cases covering the broad spectrum of defense and intelligence missions:

1. Personnel support.
2. Multi-source intelligence analysis.
3. Enterprise knowledge discovery.

These use cases focus on low-classification data, which can be securely optimized by harnessing the collective value and capabilities of Azure OpenAI and the Microsoft Azure Cloud Services. 

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Azure OpenAI use cases    

1. Personnel support. Personnel support spans the spectrum from recruitment to retention and includes numerous capabilities, such as career management, conditions of service, leave, and pay. Azure OpenAI aids this area by providing individuals with answers to their questions across a plethora of personnel-related topics through intuitive chatbots. It also equips decision makers with tools to analyze data, create reports, and make more informed decisions around human resource (HR) policy development. Currently, large quantities of data are stored in enterprise applications or in siloed systems, and substantial levels of resources and time are required to analyze that data and develop the appropriate HR policy proposals. Most notable is the ability to provide valued, timely insights from personnel data at the individual, collective, and enterprise levels as needed. For example, when service personnel are trying to gain policy advice on benefits, Azure OpenAI natural language query capability allows the investigation of policy, with follow-on questions and queries to support an informed decision.   

2. Multi-source intelligence analysis. Multi-source intelligence analysis is a method to gather, process, and interpret information from multiple sources. It involves the integration of data from various intelligence disciplines. Azure OpenAI has the potential to assist analysts as they triage, prioritize, search, analyze, and cross-reference intelligence, ultimately producing actionable information for decision makers at the time of need. Currently, analysts are challenged by an ever-increasing volume, variety, and veracity of data, much of which is unstructured and in different formats. In the future, we envisage Azure OpenAI, cloud-based services, and data being accessible from HQ to the edge. This will allow insights to be derived from both historical and real-time data and deliver actionable intelligence for mission success.  

3. Enterprise knowledge discovery. Defense and intelligence organizations store large quantities of data in enterprise systems that are siloed, and across multiple organizational boundaries. This data is often not clean or structured. Azure OpenAI can expose and correlate this data across different types and sources to find relevant information in response to a natural language query. Examples include querying large repositories of lessons learned from previous operations and exercises matched with current doctrine, and the insights gained from After Action Reviews to support mission planning, simulation, and training for future activities.   

Accessing Azure OpenAI for your organization 

Azure OpenAI has created new possibilities that were once seen as very hard and costly to implement in military systems.  

For ‘non-tactical’ scenarios, cloud-based computing provides the most secure and highest security offering available. The computing resources can be put to work continually enhancing and optimizing planning, analysis, and operational management using the best tools available. Advances in Azure OpenAI and multi-agent frameworks usher in a new era of the role of humans in the loop as a manager and orchestrator of agent computing resources rather than conducting technical analysis and planning. The result is a substantial increase in the speed and capacity of our valuable skilled resources to achieve the mission.  

When we consider ‘tactical’ scenarios, the limitation of bandwidth, weight, and power can influence the adoption of Azure OpenAI applications that can be deployed. Smaller models are less capable and must be finely tuned to their purpose to be highly effective. Additionally, carrying a large number of models not relevant to the mission takes up valuable computing and power resources. As such, when deciding on what Azure OpenAI to access in the field, nations must have robust deployment, collection, and ModelOps pipeline updates that can continually—at speed—update models for specificity and relevance to the tactical edge. The ability to access models in disrupted, disconnected, intermittent, and low-bandwidth (DDIL) environments is essential when operating as close to the edge as Size, Weight, Power, and Compute (SWaP-C) permits. 

Responsible use of AI 

Microsoft is committed to responsible use of AI. That is why Microsoft has long been a leader in ensuring the development of responsible AI, with principles designed to put people first. We believe AI exists to enhance human capabilities, not replace them, and we are committed to empowering responsible AI practices that benefit the world at large. The six key principles for responsible AI include: 

1. Accountability—ensuring transparency and responsibility in AI systems. 
2. Inclusiveness—building AI that considers diverse perspectives and avoids bias. 
3. Reliability and safety—prioritizing safety and robustness in AI deployment. 
4. Fairness—striving for equitable outcomes and avoiding discrimination. 
5. Transparency—providing clear explanations of AI decisions. 
6. Privacy and Security—safeguarding user data and privacy. 

The Microsoft Responsible AI Standard provides actionable guidance for their teams, going beyond high-level principles to create AI systems that uphold these values and earn society’s trust. We also have an Office of Responsible AI that sets governance policies, advises leadership, and ensures responsible practices across the company. 

Begin your AI transformation  

Microsoft Azure OpenAI Service offers unprecedented opportunities to augment human capabilities and enhance decision-making across the defense ecosystem. Harness the value of Azure OpenAI to achieve mission success across the spectrum of capability with greater speed, accuracy, and efficiency.  

To learn more:  

• Attend the AI in the Public Sector Microsoft Build session.
• Visit and bookmark the Microsoft Defense and Intelligence page to stay informed.
• Take the LinkedIn Learning Course, Generative AI for Leaders.
• Reach out to your Microsoft Account Team.

The post Informing defense missions with Microsoft Azure OpenAI Service appeared first on Microsoft Industry Blogs.

It is a remarkable shift, which I and others in Microsoft for government have gauged over the past year as we’ve worked to help city governments solve their most important challenges through technology. 

At the SXSW 2024 conference, the Esri Infrastructure Management & GIS Conference, and in my recent meetings with city leaders from Canada, Finland, and the Netherlands, generative AI has been at the center of most conversations. This excitement is notable because cities are traditionally cautious about technology adoption for important reasons such as risk, privacy, security, and governance, and many are still working through their cloud migration journeys.  

The potential benefits of generative AI to improve operations and service delivery are too compelling for many cities to ignore. To cite just one example, the City of Kelowna in Canada launched an early AI initiative and is using cognitive search and conversational AI to deliver a 24-hour helpline for its 150,000 residents. Project leaders report that generative AI also enables them to automate and streamline internal processes around data entry and analysis, refactor legacy databases and code, and create new apps in minutes.  

This type of innovation has prompted other cities to explore AI innovation, beginning with creating an effective and actionable plan. 

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Success factors for AI adoption in city governments 

Every city is unique, with its own ambitions for the use of generative AI and its own set of requirements and technology considerations. In our work with cities, we have identified a set of success factors that are common across cities investing time and money in generative AI and are enjoying early success.  

1. Empower the workforce with effective upskilling  

Realizing the value of AI starts with the workforce. According to research conducted as part of Microsoft’s Public Sector Insights on Skilling, the lack of skilled workers is often the number one barrier to AI implementation among organizations worldwide.

The imperative to upskill the workforce is particularly important for cities, whose early use cases usually focus on employee productivity and the internal processes they manage. Well-trained and confident workers also help ensure the success of public-facing initiatives. Executive support is key. Workers are empowered when leadership gives them the direction and license to responsibly use AI tools within the context of their day-to-day work. 

To answer the skilling challenge, cities should invest in learning programs, building public-private upskilling partnerships, and giving people adequate time to gain skills and confidence. An ongoing learning experience platform, such as the one developed by Bank of Canada, can promote a culture of learning. Microsoft offers effective resources and strategies, including the Public Sector Center for Digital Skills, which provides specialized insights, guidance, and content, and Microsoft Learn, which offers customized training options. 

2. Build an AI-ready data strategy 

AI is only as good as the data that is made available to it. In city systems, data is often siloed or locked in spreadsheets or other static locations. A modern data strategy is one that integrates such diverse data sources, ensures data quality, establishes rules and processes for data access and management, and keeps data and systems secure.   

An excellent example of how a complete data strategy can deliver ongoing AI benefits is the Smart Qatar (TASMU) Program built by the State of Qatar. Essentially a service platform built on a common data model across multiple domains, TASMU will empower a broad array of AI applications that are expected to help contribute 2% to the nation’s gross domestic product (GDP).  

This sort of comprehensive data strategy is an important long-term goal, but cities should not wait to begin innovation on AI. Many cities are taking an incremental approach, leveraging the quality data they have in hand, with their existing cloud foundations and data governance standards to experiment with new AI use cases. A careful step-by-step approach will guide your data strategy. 

3. Establish frameworks for governance, compliance, and sovereignty  

Some city governments have been reluctant to use AI due to concerns about security, privacy, and compliance requirements. To address these concerns, cities should establish transparent frameworks for AI governance and assurance. The goal is to identify risks and goals associated with both externally facing and internally focused use cases and codify courses of action to ensure success.  

Ensuring the residency of data within strict geographic borders is a key requirement for many cities. This calls for a solution that can ensure the required level of control of sensitive data while still providing a hyperscale cloud environment for a huge number of applications. 

Microsoft offers guidance for cities to establish AI governance and enhance trust and privacy in AI innovation. For cities with strict data residency concerns, we also offer Microsoft Cloud for Sovereignty, which offers tailored cloud services to help build cloud-based workloads in compliance with specific security, policy, and regulatory requirements. 

Take the first steps in building an AI-empowered city  

For city leaders who want to advance their AI journeys, our experts and industry advisors can work with you to identify potential use cases for early innovation based on your specific goals, requirements, and environmental conditions.  

To learn more about how Microsoft can empower cities and government organizations with technology to help solve society’s biggest challenges, visit the Microsoft for government website, read our Microsoft for Government e-book, or get in touch with your Microsoft representative or technology partner.     

The post How Microsoft empowers city governments on the road to AI adoption appeared first on Microsoft Industry Blogs.

During this wave, we’ve invested heavily in the development of copilot templates to enhance capabilities and integration across various industries. These customizable templates offer improved operational efficiency, enhanced customer engagement, and seamless integration with existing technology, all while supporting a diverse, global customer base. Copilots are valuable assets for Microsoft Industry Clouds customers, helping to drive customer and partner success. Microsoft’s partner ecosystem extends our offerings, with systems integrators and independent software vendors enabling factory data ingestion from different systems and building custom UI experiences for the copilot templates on Microsoft Azure AI.

Here’s a look at what’s been delivered since the release plans announcement in January 2024.

Microsoft Cloud for Manufacturing

Microsoft Cloud for Manufacturing is introducing new solutions in preview to optimize factory operations. These include manufacturing data solutions in Microsoft Fabric and a copilot template for factory operations on Azure AI. These solutions enable manufacturers to ingest and unify data from diverse sources, standardize and enrich data for seamless interoperability, and utilize custom copilots for querying data through conversational interfaces. Fabric allows users to maximize the value of factory data and uncover operational insights for production optimization by unifying information and operation technology data into an open and secure data platform. The copilot template for factory operations on Azure AI enhances responsiveness and streamlines communication across teams and roles.

Azure Data Manager for Agriculture

This release of Azure Data Manager for Agriculture includes new copilot templates that can empower organizations to build agriculture copilots with Microsoft Azure OpenAI Service. These templates provide a powerful tool for organizations to use generative AI and data to optimize their operations and engage better with their customers. Customers are bringing generative AI to life for farmers. ITC, a multi-industry enterprise, has created, Krishi Mitra, an AI copilot, developed using Microsoft copilot templates. With this application, ITC seeks to empower farmers by providing them with timely and relevant information that can boost productivity, increase profitability, and enhance climate resilience.

Copilot templates can support use cases based on tillage, planting, crop protection, harvesting, and other types of farm operations. Users can submit queries such as “show me active fields” or “what is the average yield for my field?”. These use cases can help input providers to plan equipment, seeds, applications, and related services and engage better with the farmer.

Using data from Azure Data Manager for Agriculture and other sources, copilots can provide insights on topics like disease risks, yield forecasts, labor needs, crop protection, weather impacts, and harvest windows. Enabling seamless retrieval of data and allowing for plugins, embedded data structures, and subprocesses to be selected as part of the query flow allows organizations to extend their copilot use cases to many roles and scenarios along the agriculture value chain.

Microsoft Cloud for Sovereignty

Microsoft Cloud for Sovereignty is a solution that helps public sector organizations use the public cloud and advanced technologies while helping meet security, sovereignty, and regulatory requirements. The latest release of Microsoft Cloud for Sovereignty introduces updates and new features to streamline the configuration and deployment of sovereign environments. Guardrails and codified architectures reduce complexity and make the process of building sovereign environments more predictable and repeatable. New preview tools include assessment, policy compiler, and drift detection analysis tools, as well as a new Azure service that allow users to create and deploy Sovereign Landing Zones (SLZs) within the Microsoft Azure Portal. Guidance includes sample reference architectures for using large language models (LLMs) and Azure OpenAI Service with SLZ, as well as guidance on workload migrations and Microsoft Power Platform and Microsoft Dataverse configurations.

Microsoft Cloud for Sustainability

In February 2024, Microsoft Cloud for Sustainability announced new data solutions and generative AI advancements in Fabric, providing new levels of speed and efficiency in processing data to help drive faster progress toward sustainability goals. These new features include sustainability data solutions in Fabric and natural language queries with Microsoft Copilot in Microsoft Sustainability Manager, among other AI-powered features now available in preview.

In March 2024, new features were added to Sustainability Manager, including the ability to create calculation models with Copilot using natural language input, a dedicated energy data model to help track energy usage, activity to emissions traceability to link underlying source activity data to emissions records, and the ability to create a Microsoft Power Query template to streamline and accelerate data import.

Additional release updates to Cloud for Sustainability include enhancements to environmental, social and governance (ESG) insights with what-if analysis to help organizations build the relationship between forecasting and reduction goal planning. Users can link forecasts to existing goals to track actual progress alongside the projected ones. In addition, forecasts with the same historical data can be layered onto a single view, allowing for faster analysis of optimal reduction opportunities.

Another new feature is the ability to import and calculate with product carbon footprint data. This feature allows you to use product carbon footprint data to calculate and understand value chain emissions in Sustainability Manager more easily. Organizations can determine the greenhouse gas emissions that are associated with a product family and more easily import and manage this data within Sustainability Manager.

Azure Data Manager for Energy

Azure Data Manager for Energy is expanding geo availability, adding the Australia east region. This additional region is enabled for both the standard and developer tiers of Azure Data Manager for Energy. Users can now select “Australia east” as a preferred region when creating an Azure Data Manager for Energy resource using the Azure portal.

External data sources (EDS) (preview) allow data from external data sources aligned with the OSDU® Technical Standard to be shared with an Azure Data Manager for Energy resource. EDS is designed to pull specified data (metadata) from OSDU-compliant data sources through scheduled jobs while leaving associated dataset files (such as LAS and SEG-Y) stored at the external source for retrieval on demand.

Microsoft Cloud for Retail

Microsoft Cloud for Retail now includes new features in its retail data solutions architecture, an industry-specific workload for unifying, enriching, and modeling industry data on Fabric. Retailers can take advantage of the available list of connectors, application templates, and business intelligence capabilities, which can be easily configured. Retail data solutions offer application templates tailored for retail-specific scenarios, accelerating time to market. These templates serve as customizable and extendable starter kits, allowing retailers to adapt them to their unique requirements. Additionally, application templates and connectors from specialized partners are available. These capabilities enable the seamless use of data to produce unique insights that can’t be achieved in isolation.

One of the new features is the copilot capabilities in Fabric, which bring new ways to transform and analyze data, generate insights, and create visualizations and reports in Fabric and Microsoft Power BI. Another new feature is the Sitecore OrderCloud data connector, which can be used to bring commerce data from Sitecore OrderCloud (preview) into Fabric in real time. The connector performs transformation and orchestration on top of the data from Sitecore OrderCloud to map it to the retail industry data model, reducing engineering effort and accelerating time to insights.

Microsoft Cloud for Healthcare

The 2024 release wave 1 also brings new features and innovations to Microsoft Cloud for Healthcare. One of the new features is the ability to improve clinical and operational insights by ensuring health data is accessible across provider, payor, and pharma; and facilitating clinical, operational, and performance analytics using healthcare data solutions in Fabric (preview).

Some other new features in the 2024 release wave 1 for Microsoft Cloud for Healthcare include support for additional data storage needs, support for availability zones for Microsoft Azure Health Data Services, FHIRLink Power Platform connector, and the ability to use the digital imaging and communications in medicine (DICOM) service with Azure Data Lake integration.

Microsoft Cloud for Nonprofit

Microsoft Tech for Social Impact is proud to announce the April 2024 release for Fundraising and Engagement. This release brings significant enhancements, mainly to nonprofit gift processors, including valuable enhancements to Fundraising and Engagement Azure services and new Stripe API (payment intents) integration. Customers who rely on Stripe for their payment processing can now benefit from the latest Stripe APIs, addressing the requests of current customers and the requirements of future customers. It is highly recommended that customers upgrade and use the new Stripe API when creating a payment processor associated to a configuration profile. For more details, read more here.   

Learn more about Microsoft Industry Clouds

• Official Microsoft Cloud for Manufacturing documentation
• Azure Data Manager for Agriculture Preview
• Microsoft Azure Data Manager for Agriculture Preview
• Official Microsoft Cloud for Sovereignty documentation
• Microsoft Cloud for Sustainability documentation
• Microsoft Azure Data Manager for Energy
• Official Microsoft Cloud for Retail documentation
• Official Microsoft Cloud for Healthcare documentation
• Microsoft Cloud for Nonprofit documentation

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This is of particular interest to our Microsoft for Government team, where our mission is to help governments solve society’s biggest challenges. With the accelerated pace of innovation surrounding generative AI in the past 18 months, we’ve taken a special interest in understanding the full range of factors that either promote or hinder adoption in government organizations. One concern we invariably hear is about skilling—that is, how to provide the training and support required to help employees not just understand the technology but embrace it. Without this, even the best implementations are at risk of failure.

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How a skilling strategy can advance AI adoption in governments

The growing interest in generative AI has already produced dramatic results in businesses globally, including many noteworthy impacts on governments. Gartner predicts that nearly 25% of governments plan to deploy generative AI solutions by March 2025, with an additional 25% projected the following year.¹ While impressive, this is a bit less than projected global adoption, which is not surprising given that government organizations face an especially high bar in terms of trust, risk, and community expectations.

The promise of generative AI is prompting many governments to complete their cloud migrations and upgrade their data strategies, gradually retiring costly and increasingly inadequate legacy systems. These upgrades will deliver multiple benefits and are essential prerequisites to enabling generative AI innovation.

As it turns out, however, technology alone is not enough. According to a recent IDC study, the number one barrier to implementing and scaling AI is a lack of skilled workers. Nearly 52% of global business leaders named skilling as their top challenge—more than cost (28%), concerns about data or IP loss (28%), or concerns about governance and risk.²

To help fully understand the challenges of skilling that governments face, we commissioned our own research, Public Sector Insights on Skilling. We learned that the obstacles include limited training budgets and a lack of employee time and resources dedicated to learning.

New strategies for upskilling the government workforce for generative AI

Governments who want to be successful with AI should recognize that people are as important as technology in delivering a strong return on investment (ROI). That’s why upskilling is essential, both in terms of funding and employee time.

To help their workforce take advantage of major investments in digital transformation, the Bank of Canada developed an ongoing learning experience platform that provides targeted learning journeys and skills pathways for staff of many specializations. The goal is to free workers from many time-consuming administrative tasks and encourage them to focus on more meaningful work.

Effective practices can be integrated seamlessly into everyday activities, becoming an integral part of the government culture. To help ensure effective upskilling and successful AI adoption, we recommend three important strategies:

1. Make learning an organizational priority

Embracing generative AI is often as much a cultural challenge as a technical one. It starts at the top. Committed, consistent leadership that aligns organizational goals with skilling, and demonstrates learning as a priority, are important factors in creating lasting change. Agencies and organizations need to allocate appropriate resources and understand the gaps in learning, with tactics to fill them. Build a learning culture by allocating time for employee training, identifying opportunities for applying new skills in problem-solving and innovation, and celebrating their success. A long-term mindset is key, as upskilling must be an ongoing employee priority, with opportunities for continuous learning.

2. Build public-private upskilling partnerships

An effective upskilling program is always specific to the government organization. Skilling programs should be developed in the context of their workforces, their local and national requirements, and the unique demands of their agencies and roles. But that doesn’t mean governments need to do it alone. To build a comprehensive approach to embracing AI, you can incorporate upskilling as part of the process from the start. Microsoft, our partners, and other technology providers can help develop skilling strategies. Microsoft offers effective training resources, such as our Public Sector Center for Digital Skills, which offers guidance and resources for public sector organizations in areas such as AI, cloud migration, and cybersecurity.

3. Give people the time to learn

There’s an old Confucian adage that says, “I hear and I forget. I see and I remember. I do and I understand.” Wise words, which in the context of upskilling mean ensuring employees have adequate opportunity to hone their skills. It’s one thing to promise someone that generative AI will save them time. It’s profoundly more impactful when they click on Copilot and generate a document in a fraction of the time it previously required. Such experiences win people over, but they require time. The global head of an IT workplace, interviewed for a Forrester study, estimated that it took employees between four and eight hours of experimenting with Microsoft Copilot for Microsoft 365 before they could use it effectively in their work.³

Empower your government workforce with an effective skilling strategy

We invite you to engage with Microsoft and your local technology partner to develop a skilling strategy as a key part of a holistic approach to AI adoption. For more resources and to learn more about skilling and other training opportunities, please visit:

• Microsoft Learn for government, Microsoft’s flagship resource for technical skilling.
• The AI Skills Initiative, a global resource with free coursework developed in collaboration with LinkedIn.
• AI Explained, a series of virtual community events to educate on generative AI and how it can empower people and organizations (available on-demand in 10 different languages).

Sources:

• ¹ Gartner, “Gartner Predicts Less Than 25% of Government Organizations Will Have Generative AI-Enabled Citizen-Facing Services by 2027”, March 6, 2024.
• ² IDC Infographic, sponsored by Microsoft, The Business Opportunity of AI, IDC #US51315823, November 2023.
• ³ A commissioned study conducted by Forrester Consulting, “New Technology: The Projected Total Economic Impact™ Of Microsoft Copilot For Microsoft 365″, April 2024.

The post 3 ways to solve the skilling challenge for an AI-empowered government workforce appeared first on Microsoft Industry Blogs.

We are excited to announce the latest release of Microsoft Cloud for Sovereignty. This builds upon our December 2023 general availability release, driven by customers and partner feedback, providing tools that simplify the configuration and deployment of complex sovereign controls and expand on best practice guidance. We are also releasing new guidance for comparable approaches to support implementations for Microsoft Power Platform and Microsoft Dataverse. 

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What’s new in this release

Guardrails—Codified architectures and tooling that reduce complexity and make the process of building sovereign environments designed to help achieve regulatory requirements simple, predictable, and repeatable.  

Tools—New assessment, policy compiler, and drift detection analysis tools to help better manage cloud environments. Introducing a new regional Microsoft Azure service that simplifies the management of Sovereign Landing Zone (SLZ) within the Azure Portal.  

Guidance—Sample reference architectures on how to take advantage of LLMs and Microsoft Azure OpenAI Service based on Retrieval Augmented Generation (RAG) pattern with SLZ and guardrails, as well as guidance on workload migrations. 

Learn more about how to help your organization unlock the power of the hyperscale cloud on the Microsoft Cloud for Sovereignty home and product documentation pages.

Guardrails to simplify building sovereign environments  

We’ve released new features to the Sovereign Landing Zone (SLZ) on GitHub, policy portfolio, and new capabilities of the private preview of Microsoft Cloud for Sovereignty services on the Azure Portal. 

Sovereign Landing Zone on GitHub  

SLZ (generally available) is now configured to use the most recent version of the Azure built-in Sovereignty Baseline Policy Initiative. Similarly, users can also configure the SLZ to deploy any policy set in our portfolio. With this, users will be able to take advantage of our expanding policy portfolio. This capability is now also available for the Azure Landing Zone (ALZ). For more information about when to use either the ALZ or SLZ, review our comparison guidance. 

Users can also configure specific policies (within policy sets) and include additional policies during deployment. For example, customers can easily enable rollout of policies starting with an audit mode and going into an enforcement mode on a granular level. SLZ compliance modules can now be deployed by engineers and administrators to “uplift” an existing landing zone with a similar structure, bringing it closer in line with sovereignty settings and an organization’s requirements. 

Policy portfolio  

Microsoft Cloud for Sovereignty policy portfolio has been updated to better help public sector customers meet several key regulatory frameworks. In collaboration with NATO’s Communication and Information Agency (NCIA), we have reviewed and validated the compliance of cloud deployments with NATO’s D32 directive on information protection (preview). Other updates include custom Azure policy initiatives and control mappings for the Cloud Security Alliance (CSA) Cloud Controls Matrix (now on GitHub), the Netherlands BIO (Baseline Informatiebeveiliging Overheid) Initiative, and the Italian National Cybersecurity Agency custom policy initiatives. These initiatives provide users helpful tools for navigating several diverse regulatory landscapes. 

Tools in preview for Sovereign Landing Zones

Microsoft Cloud for Sovereignty Services on Azure Portal  

Users now have the ability to search for “Microsoft Cloud for Sovereignty” within the Azure Portal. This search will provide visibility to new policy initiatives, documentation regarding the Government Security Program, and transparency logs. Additionally, users can conveniently locate instructions for onboarding into the preview program directly from the portal.  

Managing landing zone configurations in the Azure Portal 

This release streamlines the process of managing landing zone configurations within the Azure Portal. This enables the establishment of efficient and uniform infrastructure at scale by allowing users to create, update, duplicate, generate code, deploy, and delete configurations all from a single pane. For users who wish to customize their deployment, the platform offers the ability to generate a Bicep code package for their infrastructure. Additionally, for organizations seeking a no-code approach to configure and deploy an enterprise-scale landing zone, users can now deploy a landing zone configuration directly from the Azure Portal. 

Sovereignty Baseline Policy Initiatives are automatically assigned to landing zone configurations within management groups, thanks to the fact that Sovereign Baseline Policy Initiatives are now Azure built-in policies. 

Microsoft Cloud for Sovereignty lifecycle tools  

The introduction of new capabilities (preview) offer support for pre-deployment evaluations to streamline policy management processes. These features, available in GitHub, are designed to empower users with greater visibility, control, and compliance across their Azure environments. Our goal is to continually improve the efficiency, reliability, and compliance capabilities of our tools to better meet the evolving needs of our customers. 

Assess and evaluate Azure resources ahead of deployment 

The assessment feature provides a pre-deployment evaluation of Azure resources against established best practices, including the evaluation of resource locations and Azure policy assignments. The tool assesses various aspects, such as the SLZ Baseline Policy assignment, Custom Policy Initiatives usage, and individual policy assignments, offering results categorized as good, better, or best based on severity findings. This is especially helpful during the planning stage of a sovereign implementation and works well with brownfield environments. 

Policy compiler 

The policy compiler is a tool that streamlines the policy management process. It systematically analyzes your organization’s policy initiatives by examining key components—such as display names, descriptions, parameters, and effects. By comparing these elements across different policies, the tool detects redundancies, conflicts, and gaps. It then uses this analysis to provide a set of reconciled policy initiatives, making policy management more efficient and reliable.   

Landing zone drift analyzer 

The drift analyzer monitors and compares the current state of the deployed cloud environment with its original intended landing zone configuration, identifying critical deviations or changes. These deviations, whether intentional or unintentional, are essential indicators of environmental integrity and compliance. Customer feedback will help the evolution of these tools for potential integration with sovereignty services on the Azure Portal. 

Guidance for workload migration and AI configuration

We have recently updated the Microsoft Cloud Adoption Framework to include a sovereignty strategy when using cloud services.  

Our product documents have been updated with guidance on:

• Sovereignty choices for monitoring Azure workloads
• Implement encryption with Customer-Managed Keys in Microsoft Cloud for Sovereignty
• Migrate and modernize with Microsoft Cloud for Sovereignty

This release includes our first guidance and reference architecture around AI and LLM configurations. These articles offer an illustrative example of using LLMs and Azure OpenAI Service within the context of the retrieval augmented generation (RAG) pattern for generative AI. Specifically, it explores how government and public sector organizations can apply these technologies within SLZ while also considering important guardrails.  

We have also recently published guidance on how to configure Microsoft Power Platform and Dataverse environments to improve control over your data and enhance your digital sovereignty posture. This guidance is part of our ongoing efforts to promote digital sovereignty across Microsoft services, including non-Azure services.

Learn more about Microsoft Cloud for Sovereignty

• Microsoft Cloud for Sovereignty 
• Microsoft Cloud for Sovereignty product documentation 
• Sovereign Landing Zone (GitHub) 
• Policy Portfolio (GitHub) 
• Quickstarts (GitHub) 
• Microsoft Cloud for Sovereignty on Azure Portal (preview) 
• Microsoft Cloud for Sovereignty preview (GitHub for preview customers)  
• Log into your Active Azure Directory (AAD) account to request to join Microsoft Cloud for Sovereignty Preview

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The underwater and littoral environments remain critical to a nation’s economic interests and its ability to exploit marine resources. In addition to the traditional resources, energy production from water and wind are vital resources carried by subsea cables. These cables provide high-speed, low-cost, and reliable connections, with more than 400 cables spanning over 1.3 million kilometers across the world. They are essential for global data transmission, including the financial and digital economies.

In October 2020, NATO Defense Ministers discussed the threat to critical subsea infrastructure posed by increasing state actor capability and aggression.¹ Subsea protection was renewed in 2022 by the United Kingdom Defence Chief, Admiral Sir Tony Radakin, and is a current priority by NATO in the Digital Ocean initiative.² Subsea protection is a subject of debate across the globe spanning the Atlantic Ocean, Pacific Ocean, and European seas, which experience highly congested sea-borne infrastructures.³ Furthermore, in 2024, NATO is exploring the “Digital Ocean” as a pioneering initiative for maritime situational awareness.⁴

Protection of the subsea domain relies on allied defense organizations and asset operators developing and deploying innovative solutions and data-driven insights to enhance situational awareness, communication, and decision-making capabilities. However, this is a complex environment, and several technical and operational challenges need to be addressed, such as high latency, low bandwidth, power consumption, security, data quality, multi-domain sensor integration, and processing at the edge.

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Strategies can be implemented to address these challenges by using industry-leading capabilities and integrating them with hyperscale data fabric. This collaborative effort involves working closely with allied defense organizations, coast guard, government entities, and asset operators to determine innovative ways of deploying a secure and intelligent digital backbone to safeguard critical underwater infrastructure.

Meeting the challenge

Fundamentally, meeting the operational information demands requires the ingestion, analysis, distribution, and visualization of data across the subsea domain:

• Data ingestion: Securely and reliably collect, store, and process data from various sources, such as sensors, devices, satellites, and surface links. Additionally, this may require technology and infrastructure that can operate disconnected for extended periods and with an innate ability to conduct safe, secure syncing when connectivity becomes available.
• Data analysis: Applying artificial techniques, such as anomaly detection, classification, and prediction, to data to generate actionable insights and intelligence. This may require those AI and machine learning models to be developed using high-performance computing and packaged for inferencing at the extreme edge, with processes that allow efficient deployment and an effective feedback loop.
• Data distribution: Sharing and exchanging data across different domains, platforms, and stakeholders in a timely and efficient manner while respecting data governance, together with the ability to support interoperability standards (such as Standardization Agreements [STANAGs], CATL, and others) and also meeting the challenge of denied, degraded, intermitted, and latent network requirements while maintaining security policies.
• Data visualization: Presenting and displaying the data and insights in a user-friendly and intuitive way, such as dashboards, holographic displays, geospatial data infrastructure, and charts to support situational awareness and enhance decision-making.

Meeting the challenges is a significant undertaking and requires a flexible, modular, and interoperable solution. That solution must support and adapt to the dynamic and complex underwater environment, while meeting security requirements and the priorities of allied defense organizations and industry partners.

Therefore, we aim to support the construction of a common reference architecture for a test and development environment based on the principles of a digital software factory. This will enable the implementation and underpin the sustainment of a machine learning ecosystem for an interoperable, underwater infrastructure in support for continuous innovation at speed and at scale.

A proposed solution

At the core, there are four principles of design to help ensure security and intelligence in an underwater environment. They include:

1. Hyperscale compute at scale in the cloud and at the edge to enable secure information operations that extends cloud services to the edge, providing a trusted collaboration environment for a wide range of infrastructure partners. The provision of a hyperscale environment also meets the ongoing needs for security, scalability, and reliability.
2. Secure data exchange to support a Zero Trust, multi-domain network of asset operations and sensors, while maintaining security and data controls. A secure data exchange capability also meets the operational requirements for trusted data and information sharing across a complex ecosystem of agencies and public and private infrastructures within a multi-dimensional legal framework that cross the boundaries of territorial waters.
3. Ubiquitous connectivity through terrestrial and non-terrestrial networks, leveraging current infrastructures at scale and meeting the asymmetric growth in data volume and near real-time data insights. Furthermore, we envision the (future) application of private 5G and satellite backhaul connecting sensors at subsurface, surface, and above surface that enhance risk management and reduce mitigation at sea.
4. Assurance and risk management services for the maritime and energy sectors combining the requirement for public and private partnerships in current and future energy infrastructure.

Applying these design principles provides a comprehensive solution for a secure undersea digital backbone. It will:

• Enable enhanced situational awareness for all parties across the operating landscape.
• Facilitate data sharing and exchange across domains, platforms, and stakeholders.
• Ensure security and reliability across the infrastructure and environment.
• Optimize the performance and operations through the provision of undersea digital services for the maritime, energy, and defense sectors.  

Ultimately, delivering enhanced human-machine teaming in military and non-military operations provides the backbone for computational decision-assist capabilities that are data-driven, trusted, and transparent.

Enhance maritime operations with Microsoft solutions

The domain of critical underwater infrastructure is a strategic and challenging environment that requires cloud-enabled innovative and interoperable capabilities to enhance data and network management for undersea operations.

Microsoft for Defense and Intelligence believes this approach will provide a comprehensive and flexible solution that delivers significant benefits for situational awareness, communication, collaboration, security, reliability, performance, as well as efficiency of undersea operations and the critical infrastructure situated in the maritime environment.

For more insights, read the “Transforming Sub-Surface Operations with Data-Driven Decision Support” whitepaper.

Empowering militaries. Improving operations. Protecting national security.

¹NATO seeks ways of protecting undersea cables from Russian attacks, Euractiv.

²Chief of Defence Staff: Russia cutting underwater cables could be ‘an act of war, Froces.net.

³Australia must do more to secure the cables that connect the Indo-Pacific, ASPI Strategist.

⁴NATO Digital Ocean Industry Symposium, NATO.

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The new AI opportunities for public finance  

Public finance organizations are generally well positioned to take advantage of AI’s ability to redefine their services, operations, and impacts on government. Those who have adopted the Microsoft Cloud and Microsoft productivity applications are benefiting from the fact that, over the past decade, and especially the last 18 months, Microsoft has been integrating AI capabilities throughout our offerings.  

Copilots—AI-driven software assistants—are delivering immediate opportunities for ingenuity among ground-level and operational employees, as seen in Microsoft Copilot for Microsoft 365, the new Microsoft Copilot for Finance, and Microsoft Bing. For business and IT leaders, copilots across Microsoft Power Platform, Dynamics 365, Security, and GitHub make it simple to unlock powerful new capabilities with embedded, low-code features. And in cases where advanced solutions are called for, Microsoft Azure OpenAI Service enables the development of customized solutions such as virtual assistants, chatbots, automated systems, and intelligent applications.  

Thanks to the broad range of these capabilities, public finance agencies can move quickly to redefine their role and leverage new opportunities. To make this vision a reality, we focus on AI innovation with three specific objectives in mind.  

1. Simplify taxpayer experience and revenue collection 

One great opportunity with AI is to make it easier for people and businesses to pay the right amounts of taxes and fees, in ways that are embedded into their lives. AI powered solutions for taxation, customs, and license and permit processes can be simple and sensible while providing the security and compliance benefits of the cloud. AI helps with managing data and fostering faster ways of information sharing across agencies—a challenge that by one estimate costs European Union governments more than 50 billion euros a year.¹ 

In this regard, AI can help in many ways, including: 

• Modernizing taxpayer services—New engagement models can proactively build data intelligence to improve revenue collection, while reducing the cost of compliance, increasing agility, and improving productivity. Taxpayers can enjoy seamless experiences fostering a greater sense of trust in agencies. This proved to be the case with Estonia’s Information System Authority, which created an AI assistant that grants people with access to secure information about vital government services in a matter of seconds as opposed to days. 
• Boosting employee productivity—Employees tend to be more effective with tools that minimize drudgery and help them focus on delivering results and finding solutions. User-friendly, interoperable, and automated processes foster better productivity, and AI enables new ways to help employees find the right information at the right time. A key factor in realizing this potential is upskilling the workforce, which is why the Bank of Canada implemented an ongoing training initiative based on Microsoft Learn that provides individualized learning pathways with online on-demand and instructor-led content.  

2. Drive informed budgeting for economic development 

Trillions of dollars are distributed by governments around the world to households, businesses, local authorities, and others for stimulus, recovery, and resilience plans.² This puts an enormous responsibility on budget and treasury agencies, which have the complex task of allocating public resources in ways that measurably impact economic development. These agencies require innovative, secure tools that enable them to allocate the right support to the right beneficiary at the right time. Done well, these solutions also enhance collaboration between government and the private sector, which ultimately fosters financial inclusion. 

To meet these challenges, AI can help by: 

• Modernizing budget planning and execution—Employees in budgeting agencies often deal with legacy systems that are insufficient for accurate forecasting and planning. AI can help with new tools that improve these processes, as well as automate budget distribution, deliver reports, and improve forecast precision. Low-code, AI-enhanced tools can accelerate the creation of powerful solutions. In Japan, for example, the Ministry of Economy, Trade and Industry slashed development timeframes from one year to as little as one month by adopting Microsoft Power Platform.  
• Boosting employee productivity—Treasury agencies have significant management responsibilities, and high demands to operate with accountability and transparency. Cloud and AI can empower teams to better manage liquidity, optimize debt management, and manage reserves and investments, while also being efficient and secure. Productivity at the United Kingdom Department for Environment, Food, and Rural Affairs improved dramatically with a new cloud solution to provide a customer portal, streamline business processes, and add real-time reporting. By automating claim entry, they processed more than 40% of their annual 250,000 payments in the first three months.  

3. Mitigate fraud and corruption in public finance 

Fraud and corruption are major problems that incur trillions of dollars in losses worldwide every year.³ AI can help public finance agencies reverse the trend by helping to spot activities that may signal fraud, evasion, or abuse of public funds. To do this, agencies need solutions that give them fast, accurate, and comprehensive analytics, with a 360-degree view of taxpayer profiles to make informed decisions. 

Examples of how AI can help mitigate fraud and corruption include: 

• Improving compliance and protecting against fraud and corruption—Public finance agencies need solutions to combat illegal activities while also safeguarding taxpayers, employees, and all their data. AI enables solutions that analyze data from traditionally siloed, disconnected sources to detect anomalies and derive insights in previously impossible ways, while inherently protecting to ensure security and regulatory compliance. SymphonyAI, an enterprise software company that employs AI to solve financial crime, recently launched a copilot for investigators that automatically collects, collates, and summarizes financial and third-party information. Early experience shows that it can improve investigator productivity by more than 60%.  
• Enabling modern risk management—Risk is often inherent for public finance agencies, so teams need effective, modern approaches to risk management. These solutions should deliver monitoring and reporting across organizations and agencies, ensuring that everyone has complete, appropriate access to information. A good example is an Electronic Invoice Anomaly Detector built jointly by Microsoft and the Inter-American Center of Tax Administrations (CIAT), which strengthens electronic invoicing and reduces fraud and evasion for tax administrators in its member and associate member countries worldwide. 

The essential role of responsible AI 

The promise of AI would be impossible without trust. That is why Microsoft has long been a leader in ensuring the development of responsible AI, with principles designed to put people first. We believe AI exists to enhance human capabilities, not replace them, and we are committed to empowering responsible AI practices that benefit the world at large.  

The Microsoft Responsible AI Standard defines product development requirements for Microsoft technologies, guided by the principles of fairness, inclusivity, reliability and safety, transparency, privacy and security, and accountability. We believe this holistic approach can help public finance agencies deliver actionable results for their communities, with a minimum of risk and unintended consequences.  

Empowering public finance agencies with data and AI

It is an exciting time to be in public finance. Connected systems driven by data and AI set the stage for governments to unlock new possibilities such as tax compliance by design, intelligent connected trade windows, digital currency, outcomes-based budgeting, and cyber and financial crime detection and prevention convergence.

To learn more about how Microsoft is helping public finance agencies to reignite economies and improve accountability with AI, visit Microsoft for Public Finance.

¹ Politico, “Billions of euros lost to poor tax data, EU watchdog says,” Bjarke Smith-Meyer, January 2021.

² OECD, Global Outlook on Financing for Sustainable Development 2021, November 2020.

³ United Nations, “Global Cost of Corruption at Least 5 Per Cent of World Gross Domestic Product, Secretary-General Tells Security Council, Citing World Economic Forum Data,” September 2018.

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The themes of the summit included government acceleration and transformation, AI, economic development, sustainability, and global health. As leaders in Microsoft for Government, we were struck by how deeply technology has become intertwined with governance. This was especially true with AI, which was pervasive in almost every discussion. The energy and excitement were palpable, and at times it felt as though we were attending a technology forum.

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Top-of-mind AI adoption concerns for governments worldwide

Across all interactions with attendees, we heard a broad range of perspectives around AI and its role in the future of governments. The potential value of AI was not at issue. Overall, participants regarded the advent of AI, especially generative AI, as a historical shift on par with the internet or the Industrial Revolution, and there was overwhelming interest in putting it to work broadly for the advancement of society.  

We also heard a range of perspectives surrounding generative AI. Among the common themes and questions on the minds of government leaders were the following: 

• Developing legislation and regulations: How can governments take action to ensure safe, fair, and responsible AI adoption without stifling innovation? 
• Enabling inclusion: How can AI be infused throughout the world, and not just in wealthy societies, to create an inclusive and equitable environment based on the principles of privacy, trust, and transparency? 
• Ensuring security: How can governments protect sensitive data and assets in a world where nation state actors and criminals are launching aggressive cyberattacks using AI? 
• Ensuring digital sovereignty: How can data be made available to AI while still conforming to the laws and regulations of the specific country or region where it resides? 
• Training government workforces: How can government employees get appropriate, effective learning resources to quickly embrace and leverage new AI skills? 

Clearly, these issues span the broad domains of technology, regulation, and legal and political considerations. It will take broad, long-term partnership and collaboration from many quarters to ensure that AI is employed in ways that benefit everyone, with minimal negative impact.  

How governments are getting started with AI innovation 

Every government’s situation and challenges are unique, but there are some important principles for every leader and organization to bear in mind. First is understanding that data underpins AI innovation. It is not possible to reap the benefits of generative AI without a modern data strategy, one that integrates diverse data sources, ensures data quality, establishes rules and processes for data access and management, and keeps data and systems secure.  

This is why governments are increasingly recognizing that embracing AI requires migrating to the cloud. A modern platform, such as the Microsoft Cloud, provides the rich data services and hyperscale compute capabilities required to enable AI, plus the important security and compliance benefits that are built in.  

With a strong cloud foundation, governments can start early innovation with AI. This begins by defining clear objectives that address an important, manageable priority. From there, a good first step is to build a limited use case using tools such as Microsoft Azure OpenAI Service, which let you test, learn, and iterate. 

According to IDC, 64% of governments worldwide are in the early stages of exploring generative AI proof-of-concepts and use cases, with about half of those making significant investments. The top investments for state, regional, and local governments over the next 18 months are focused on supporting employees. Also essential for governments are programs to help train and hire for generative AI expertise, and working with trusted cloud providers who can deliver the scale and responsible partnership required for success. 

Microsoft’s responsibility in the era of AI 

Responsible AI has long been a cornerstone of Microsoft’s leadership in cloud and AI. Our recognition of the need for a global agreement on responsible AI and cybersecurity was articulated by Microsoft President and Vice Chair Brad Smith in his 2017 call for “The need for a Digital Geneva Convention.” Our commitment was further defined in the June 2022 Microsoft Responsible AI Standard, which defines a set of principles for responsible AI and offers a set of processes that governments can use to build their own governance frameworks and controls. 

In an October 2023 update prepared for the United Kingdom AI Safety Summit, Microsoft’s AI Safety Policies were explained in depth. Also, at the Mobile World Congress in Barcelona in February 2024, Brad Smith discussed Microsoft’s AI Access Principles, detailing our role and responsibility as an AI innovator and a market leader, and our commitments to promote innovation and competition in the new AI economy. As more than 4 billion people in over 40 countries prepare to vote, Microsoft announced at the Munich Security Conference that it has joined a group of 20 leading technology companies to help combat the deceptive use of AI in 2024 elections.  

Our experience at the World Governments Summit illuminated the sheer magnitude of the challenge of bringing AI to life for governments in ways that are fair, safe, equitable, and ultimately net-positive for all. It is a monumental challenge, which no single company, government, or entity can fully address. We are proud of Microsoft’s leadership in responsible AI, and we also recognize that there is much work yet to be done, and we cannot go at it alone. 

Looking forward  

For insights and assistance in moving forward with confidence, we invite government leaders to reach out to their Microsoft representative or technology partner. For a complete view of Microsoft’s commitments to responsible AI, see how we are empowering responsible AI practices.  

The post AI innovation takes center stage at the World Governments Summit 2024 appeared first on Microsoft Industry Blogs.

Historically, a nation’s security and sovereignty depended on its ability to defend its interests in the land, air, sea, and space domains. However, with the growing reliance of nations on the digital ecosystem, the “cyber domain” has become as crucial to national security as the traditional domains. The number of cyber incidents targeting government agencies worldwide from December 2022 to August 2023 rose by an astonishing 150%—our adversaries are increasing their volume of attacks.

The Defense and Intelligence team at Microsoft understands defending a nation’s interests requires a comprehensive national strategy that covers both the physical and digital domains. Accordingly, a reliable and secure digital backbone is the foundation, and a prerequisite for a national cybersecurity system.

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The cybersecurity gap

Cyber offensive adversaries have a substantial advantage over national security agencies across four key areas:

1. Skills and innovation
2. Approach
3. Mindset
4. Technology

The image below illustrates the differences between the national security agencies and their cyber offensive adversaries in terms of cybersecurity capabilities.

The public sector is the most vulnerable, with 53% of attacks in the last 12 months focused on critical infrastructure and government organizations. Cybercrime will cost the global economy up to $10.5 trillion by 2025.¹

Reducing the impact on a nation’s economy requires the latest in cyber technology to protect national digital assets, critical infrastructure, and the wellbeing of its residents.

Countering the rise of AI-enabled attacks 

Microsoft collaborated with OpenAI to produce a cyber threat intelligence research study focusing on AI-based cyber activity and threat actors.

The focus of the collaboration is to ensure the safe and responsible use of AI technologies, upholding the highest standards of ethical application, and to protect the community from potential misuse. Additionally, in line with Microsoft’s leadership across AI and cybersecurity we also announced principles mitigating the risks associated with the use of AI tools and application programming interfaces (APIs) by nation-state advanced persistent threats (APTs), and advanced persistent manipulators (APMs), and cybercriminal syndicates. As the research illustrates, Microsoft and OpenAI took action to disrupt assets and accounts associated with threat actors, improve the protection of large language models technology and users, and shape the guardrails and safety mechanisms around the models.

Microsoft is further committed to using generative AI to disrupt threat actors and leverage the power of new tools, such as Microsoft Copilot for Security, to elevate defenders everywhere, including across the defense and national security ecosystem.

Using an AI-centric approach to shift the advantage to the cyber defenders  

Defense and national security organizations need to modernize their approach and rapidly adopt new technologies to counter the significant advantage of their agile cyber adversaries. Implementing a National AI-cyber shield system, which is powered by Microsoft Copilot for Security, will help to shift the advantage to the cyber defenders. 

A National AI Cyber Shield System aggregates key security information and event management (SIEM) and extended detection and response system (XDR) hosted on a hyperscale platform, achieving efficiencies of scale and centralized reporting. Government organizations have multiple generations of technology, spanning clouds, devices, and operating systems. The National AI Cyber Shield System monitors those systems, whether on premises, in a public cloud, or elsewhere. The National AI Cyber Shield System provides cyber threat intelligence, behavior analytics, security orchestration, and response, to deliver a unified, comprehensive view of a customer’s security posture. 

The national AI-cyber system needs a Hyperscale XDR and SIEM platform to provide rich high quality security signals and security posture across the digital estate. This is delivered by Microsoft Defender XDR, Microsoft Sentinel, and the Hyperscale native SIEM providing a solution that can correlate events from disparate data sources and aggregate the results.

Cyber professionals can then use natural language prompts in Copilot for Security to dramatically increase the speed of hunting for threats as well as the necessary focus to hone in on high-risk events. This AI-centric approach resulted in an improvement of up to 40% in speed when completing tasks like investigation and response, threat hunting, and threat intelligence assessments.  

Your strategy and application of AI tools is your cybersecurity shield against ever increasing threats from cyber offensive adversaries. 

Shift the advantage and seize the power of cloud and AI to thwart cyber offensive adversaries. Modern cloud-based AI-cyber defense systems deliver speed, scale, and sophistication to stay ahead of cyber offensive adversaries, and attain mission outcomes.

Get started on your AI cyber defense journey

A private and public partnership must be forged between Microsoft and defense and national security organizations to shift the advantage towards defense organizations, and counter the volume, innovation, and sophistication of threat actors and cyber criminals.

 “Artificial Intelligence will be a critical component of successful defense. In the coming years, innovation in AI-powered cyber defense will help reverse the current rising tide of cyberattacks.” 

—Tom Burt, Corporate Vice President, Customer Security and Trust, Microsoft 

Start your AI-centric cyber defense modernization journey now. Follow these steps: 

1. Prepare your AI-cyber defense transformation by implementing key foundational elements in the Hyperscale XDR and SIEM platform that would interact and feed your national AI-cyber defense system.
2. Get familiar with the generative AI cyber defense system: Copilot for Security.
3. Ensure your cyber defense and security operations teams understand how the generative AI cyber defense system works and start skilling them on how to implement and operate Copilot for Security.

Visit the Microsoft Defense and Intelligence to learn more about how we’re helping defense and national security organizations protect national interests and ensure security.

Next steps

To learn more: Listen and apply insights from defense, government, and industry leaders on how AI-cyber capabilities are shifting the competitive advantage: 

• Malta Gov National Cyber security Center Podcast
• Canadian Cybersecurity Centre Podcast
• Military Lessons on Cyberdefense
• Podcast with Tom Burt
• Read the Microsoft Digital Defense Report, 2023

¹Cybercrime To Cost The World $10.5 Trillion Annually By 2025, Cybercrime Magazine.

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