As we enter the next era, what will be the defining measure of our progress?

Every industry has a word that shapes how it thinks. For pilots, it’s safety. For insurers, it’s risk.

For the semiconductor industry, it’s yield.

Yield does not ask how elegant the solution is, how many years it took or what the roadmap promised. Rather, it asks one simple question: What useful output did we produce?

For more than 60 years, the semiconductor industry has asked that question, relentlessly maximizing the number of usable chips produced from every wafer. Generation after generation, wafer after wafer, it is precisely that discipline that turned the transistor from a laboratory curiosity into the foundation of modern life.

Today, we need to apply the same principle to the unprecedented resources that the world is pouring into AI: capital on a scale once reserved for nations, gigawatts of power and record-breaking fabs and datacenters.

The question that will define this decade is the same one this industry has always asked: What actually comes out? Not just chips and tokens, but as affordable intelligence, as work that matters and as outcomes that improve lives.‌

That is the yield imperative.

The limit of more 

AI has proliferated with remarkable speed, at a rate of adoption faster than the internet, the PC or even the smartphone. Yet, global penetration still stands at just 18% of the working population, and the vast majority of that usage is chat-based. As systems move from answering individual prompts to reasoning, planning, using tools and executing longer agentic workflows, the infrastructure equation changes dramatically. A single agentic task can use more than 3,400 times as many tokens as a typical chat interaction.

We are only in the early innings of agentic adoption, and the infrastructure is already strained. Power is setting the limits on what we can build and when. Packages and racks are growing larger and denser. Memory is becoming an even tighter constraint.

For years, the industry’s rational answer to each new requirement resulted in more: more silicon in the package, more memory beside it, more power to feed it and more fiber to connect it. Each generation delivered meaningful progress. But when each new gain requires more input than the one before it, we are on a treadmill. It moves only as long as we keep adding to it.

I believe we need to pursue two paths forward. The first is evolutionary: we continue improving the architectures we have today, driving incremental efficiency, utilization and economics within each generation. The second is transformational: changing the curve itself with innovation in new architectures, new materials and new approaches to system and model design.

The history of our industry is defined by transformations like these. When increasing CPU clock speeds ran into the power wall, we moved to multicore processors. When planar NAND reached its limits, memory went vertical.

And now, once again, we have an opportunity to challenge our assumptions and rethink the fundamentals. Because the next chapter of AI won’t be defined simply by how much infrastructure we build, it will be defined by how much intelligence we can create from it.

Engineering useful yield 

For decades, the computing industry has optimized yield in the context of manufacturing. Today, that discipline has to extend across layers, from datacenters and silicon through models and the agentic harnesses that orchestrate them. And the work does not stop once the technology is built. We must then deploy and scale it faster, while developing new tools and systems to maximize utilization across our fleet.

From development through execution, each layer has a yield of its own, and losses and gains compound across them. Capacity at any one layer is only a starting point. The real measure is how effectively those layers work together to produce useful output from the system as a whole.

Our experience at Microsoft building and operating AI infrastructure at scale has reinforced two key lessons. First, the biggest constraints are rarely solved in the layer where they appear. Second, when we attack a constraint across the whole stack, tradeoffs that seemed inherent to the problem often turn out to be artifacts of the architecture.

The greatest advances often come when we apply these learnings through co-design, working across layers to turn apparent limits into solvable system constraints. Innovations in memory, networking and power show what this approach looks like in practice.

Memory: More intelligence from every byte 

Today, memory is viewed as a supply problem or a component problem. In reality, it is a system problem.

In AI inference, memory is now setting the limits on system performance. It must hold larger models, preserve longer contexts and deliver data fast enough to keep the compute fed. And agents raise the bar even further. Generation, retrieval, tool use and persistent memory run together in loops that can last minutes or hours. The result is a much longer memory horizon, with far more information kept close to the compute and available across an expanding sequence of turns. Doing that efficiently at scale will define the next generation of AI infrastructure.

Our experience building the Azure Maia platform demonstrates that memory bottlenecks are not resolved by a single layer.

Model architecture, data science and compression can reduce the amount of KV cache, which stores the model’s working context during generation. Software can manage memory hierarchies more effectively, silicon can be optimized for data movement efficiency and compilers can place data closer to compute. No one change removes the constraint. Together, they increase the useful intelligence the system can deliver from the same memory resources.

That is useful yield: not simply adding bytes but getting more useful intelligence from every byte we already have.

Networking: Designing across layers

As we zoom out to the cluster level, we see that intelligence does not come from one chip. It comes from thousands of chips operating as one system. Faster links matter, but the productivity of the system also depends on congestion management, failure recovery, workload placement, programming complexity and the boundaries between silicon, system and software. Together, they determine whether expensive compute is producing intelligence or sitting idle.

When architecting the platform for Maia, we did not begin with an existing networking design. We began with the outcome we wanted to deliver: efficient inference at fleet scale, designing across silicon, networking and system software. Instead of separate scale-up and scale-out fabrics, we built a two-tier scale-up network, integrated the NIC functionality directly into the chip and developed a custom transport layer.

The result is scalable, consistent performance across dense inference clusters, with a unified fabric that simplifies programming, improves workload flexibility and makes better use of available capacity. And with less network hardware needed to deliver this performance, we also lowered the cost of running the entire system.

Our objective is not merely to move data faster. It is to keep more compute productive and deliver more tokens from every watt and every dollar.

Power: Co-designing for efficiency, from grid to chip

Moving from the cluster to the grid, AI has introduced new challenges around power availability, distribution and utilization. Racks have gone from tens of kilowatts to hundreds of kilowatts, and datacenter campuses can operate on the scale of gigawatts. Power used to be something the system simply plugged into. Now, it is something we design around, from the grid to the chip.

That is why the industry is rethinking power across the system. Solid-state transformers and 800-volt direct current power delivery can reduce distribution losses as power moves through infrastructure. Power and cooling are no longer downstream of the design, they are part of the product definition from the start. And increasingly, that co-design is needed all the way into the silicon.

Azure Cobalt 200, our Arm-based server CPU, shows what this looks like in practice. We designed Cobalt so that every core has its own voltage and frequency controls, paired with software-based, per-virtual-machine power capping. This finer-grained control enables targeted power adjustments while protecting the performance of critical workloads, allowing us to run more servers within the same power envelope.

As Cobalt demonstrates, hardware-software co-design enables us to more effectively turn every megawatt into customer value.

The breakthroughs between boundaries 

The pattern we see across memory, networking and power extends throughout the system: start with the useful output, then optimize the whole rather than any one layer. This first requires us to be precise about the output we are optimizing for and which design constraints are truly fixed. Are we optimizing for peak performance or sustained system throughput? Would the workload benefit from significantly more capacity with marginally less redundancy? What creates more value: a broader set of capabilities or significantly earlier customer deployment?

Not every constraint in today’s systems is a law of physics. Some are inherited from decisions made elsewhere in the system and can change only when we work across traditional boundaries. Evolution comes from the steady gains each company drives within its own domain, but transformation comes when we challenge those assumptions together and redesign the system as a whole.

The breakthroughs ahead will emerge from collaboration across the ecosystem, spanning hyperscalers and silicon providers, equipment makers and materials innovators, utilities and datacenter operators, model builders and software developers.

Toward full yield 

But tokens and intelligence are not the finish line. What we produce becomes the input for someone else’s work.

What matters next is how broadly that input translates into productivity across the economy and value in people’s lives, whether it helps a scientist accelerate discovery, a clinician identify a signal earlier, a student get help at the right time or a small business find a new path to growth.

That happens as AI becomes part of everyday work across industries and around the world. People build on it, new uses emerge, the tools improve and the value compounds.

For that cycle to spread, AI must be broadly accessible. And at scale, accessibility depends on efficiency. It is the only way to deploy enough intelligence, and at a cost that allows it to reach everyone.

When every person and every company can access intelligence, build on it and create value of their own, that is full yield.

We will continue to build capacity because the world will need it. But our defining measure of progress must be what comes out: not only chips or tokens, but useful intelligence translated into empowerment, opportunity and human achievement.

That is the yield imperative. And it is work our entire industry must take on together.

Rani Borkar leads the core organizations responsible for planning, architecting, developing and deploying hardware and infrastructure for Microsoft’s leading cloud computing platform — from silicon, to systems, to supply chain.

Learn more about Microsoft’s silicon to systems approach to Azure infrastructure.

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Throughout this past fiscal year, customers across every industry and segment moved from AI experimentation to deploying AI for real-world business outcomes. They unlocked innovation and created new opportunities for growth. We saw the emergence of Frontier Firms as they moved beyond efficiency gains to focus on human ambition and embed AI at the core of how they operate. Successful customers are building an intelligence platform so their unique IQ — their knowledge, data, workflows, applications and expertise — can continuously compound, ensuring the value of AI accrues to the customer, not the model. They have a trust platform that is pervasive, with the ability to manage, govern, secure and measure AI across every business process.

Everything we are doing at Microsoft is empowering Frontier Transformation: Copilot enables AI in the flow of human ambition, Microsoft IQ amplifies and protects an organization’s IQ and Agent 365 is the trust platform that enables observability at every layer of the stack.

Businesses are not static, and neither are the AI systems that support them. As organizations evolve, AI systems must continuously learn and improve. Agentic workflows need to be built, observed and tuned against the outcomes organizations seek and the ROI they demand. Microsoft’s open, model-diverse and heterogenous platform powers that improvement loop. We also recently announced Microsoft Frontier Company, bringing our AI engineering approach to customers around the world to help them build these AI systems to accelerate measurable business outcomes.

Throughout the past year, we saw customers put these capabilities to work in powerful ways — embedding AI into core business processes, building agentic systems, strengthening security, accelerating innovation and creating new sources of value. The stories below highlight organizations leading Frontier Transformation, demonstrating how intelligence, trust and human ambition come together across industries.

To advance its journey to become a global AI-powered company, Atos Group deployed Microsoft 365 Copilot to 56,000 employees across 54 countries — from consultants to engineers to frontline workers — and was one of the first organizations globally to adopt Microsoft 365 E7: The Frontier Suite. Using Microsoft Foundry, Microsoft Copilot Studio and Agent 365, Atos is building, operating and governing a growing ecosystem of 19,000 AI agents through a unified operating model that brings together productivity, security, compliance and agent governance. As Atos embeds secure agentic AI across its workforce, the company is creating a repeatable model to continuously improve thousands of agents at scale while applying the same playbook to help customers accelerate adoption across highly regulated industries.

Facing state-sponsored threats and complex global operations, ASM is strengthening cyber resilience with Microsoft Security Copilot, helping protect the intellectual property behind advanced semiconductor manufacturing. By bringing threat investigations into a unified AI-powered experience, ASM enables analysts to investigate incidents faster, apply consistent decision-making across global operations and accelerate the development of cybersecurity talent. The company reduced incident triage time by 68%, cut laptop compromise investigations from 25 minutes to eight and now saves 337 hours each week on investigations while redeploying 20% of its security operations staff to governance, risk and compliance initiatives.

Banco Popular Dominicano, the largest private-sector bank in the Dominican Republic, transformed operational risk management from periodic, sample-based reviews into continuous, AI-powered supervision. Using AURA — an ecosystem of specialized agents built on Microsoft Copilot Studio and Microsoft Power Platform — the bank monitors 100% of its operational risk universe in real time, up from roughly 40% coverage, and can automatically analyze changes, validate controls and surface issues as they occur. The shift has delivered seven times greater analytical capacity, reduced manual operating effort by 70%, achieved 98% methodological accuracy and enabled continuous processing of approximately 80,000 documents per week and more than 300 cases per day. Just as importantly, risk teams have moved from reacting to problems after the fact to anticipating and preventing deviations before they occur. These results demonstrate the power of AI democratization. By enabling business teams to build intelligent solutions themselves through low-code tools, Banco Popular transformed operational risk management while fostering a culture of innovation led by domain experts.

To help reduce the manual burden for employees while meeting the pharmaceutical industry’s strict data security requirements, Cactus Life Sciences modernized scientific workflows with Microsoft 365 Copilot and agents. The company has deployed more than 30 custom automation agents to streamline document review and structure data extraction and information retrieval across scientific writing and project management teams. Supported by a centralized knowledge repository and the Copilot Champions community, the company reports efficiency improvements of approximately 35% to 50% in structured data extraction. By automating labor-intensive tasks and maintaining human review and quality controls, the company is enabling scientific writers to focus on deeper analysis, synthesis and delivering exceptional science to clients.

Chow Tai Fook is redefining luxury retail with Microsoft 365 E5, Microsoft Purview, Microsoft Azure OpenAI Service, Microsoft Fabric and Microsoft Foundry. The company has deployed over 400 customized AI agents supporting more than 24,000 employees, with millions of AI interactions each month and core business-process efficiency gains exceeding 70%. Through its AI Fook super-agent ecosystem, frontline associates can instantly access product expertise, inventory insights and personalized recommendations, helping drive sales conversion improvements of up to 57% while delivering hyper-personalized omnichannel experiences at scale. With hundreds of AI agents operating across the business, Chow Tai Fook is creating a foundation where customer, product and operational intelligence can be applied across every interaction, helping personalize experiences and improve decision-making across its global retail network.

To accelerate AI adoption, EY moved AI from experimentation into enterprise-wide transformation. After deploying Microsoft 365 Copilot to 150,000 employees and realizing a 15% productivity gain, the firm is expanding the Microsoft 365 Frontier Suite across its global workforce of more than 400,000 people, embedding agentic AI capabilities across the enterprise. As Client Zero, EY is applying Microsoft technologies across its own operations, including Microsoft Power Platform, Microsoft Copilot Studio, Microsoft Azure, Microsoft Foundry and Microsoft Fabric. The results include 95% faster lead times, a more than 37% reduction in finance operating costs and up to a 90% reduction in manual workloads across key business processes.

To reimagine the grocery shopping experience, Grandiose Supermarkets created an AI-powered shopping companion — GrandChef — built on Microsoft Foundry and Azure OpenAI Service. By connecting meal inspiration, recipe discovery and product purchasing into a single experience grounded in live product and inventory data, GrandChef helps shoppers move from intent to purchase faster and with greater confidence. This has led to a 31% increase in conversion, a 20% lift in average basket value and shopping journeys that are 40% faster. Together, these gains are helping Grandiose Supermarkets create more personalized customer experiences while driving measurable business growth. By connecting customer intent, product data and purchasing decisions in a single experience, Grandiose is creating a feedback loop that helps continuously improve recommendations and shopping experiences.

To support audits across its global organization, Grupo Bimbo built two agents with Microsoft Copilot Studio and deployed them through Microsoft 365 Copilot and Microsoft Teams: the result was Audit Assist, an idea generated from a Microsoft-supported internal hackathon and Comatrix. By connecting auditors directly to approved guidance, procedures and templates in SharePoint, the company is helping teams work more efficiently across 39 countries while improving consistency and audit quality. The solution/AI agents reduced planning-phase audit time by 20% and accelerated risk and control matrix creation from days to seconds, enabling auditors to spend less time searching for information and more time on analysis and decision-making. By making approved audit knowledge instantly accessible across its global audit organization, Grupo Bimbo is creating a foundation where expertise can scale with the business — improving consistency, reducing rework and enabling auditors to focus on higher-value analysis and decision-making.

To create a unified foundation for data, automation and AI across its operations in 47 countries, Navien built a connected, intelligent operating model with Microsoft Fabric, Microsoft 365 Copilot, Microsoft Foundry and Microsoft Copilot Studio. By connecting fragmented data and processes across procurement, manufacturing, quality and customer service, the company is enabling more consistent, data-driven decision-making across its operations. Navien saved 28,000 hours annually through AI agents and automation, with 32% of employees using self-service analytics to make decisions without IT support. Through its migration to Azure, it also expects more than 1.4 million in total cost-of-ownership savings over five years. By connecting data, insights and workflows across the business, Navien is enabling faster, more consistent decision-making at scale across its global operations.

NHS England is accelerating AI adoption across the healthcare system with the largest implementation of its kind in the healthcare sector. Following a trial involving 30,000 workers across 90 NHS organizations — where users saved an average of 43 minutes of administrative time per day — NHS England is rolling out Microsoft 365 Copilot to over 500,000 clinicians and support staff. Organizations can use Microsoft Copilot Studio to build and deploy AI agents that streamline clinical, operational and administrative workflows. Through Agent 365, NHS England can govern and scale those agents across the healthcare system while enabling individual trusts to build solutions for local needs — creating a secure framework where agentic workflows can be deployed, managed and expanded consistently to improve service delivery, reduce costs and create more time for patient care.

Novo Nordisk is using AI to help researchers make faster, more quantitative decisions in pharmaceutical R&D. Working with Microsoft’s AI Acceleration Studio within the Forward Deployed Engineering team, the company built a governed reasoning agent on Microsoft Azure with its proprietary dataset, including more than 200,000 patient-years of harmonized clinical trial data, while maintaining rigor, oversight and compliance. The system has expanded the team’s capacity to evaluate potential opportunities from 5 to 10 strong ideas per quarter to more than 50, and the company expects it to reduce time to insight for exploratory analyses from weeks to minutes. By grounding AI in its datasets and governed workflows, Novo Nordisk is helping scientists turn decades of institutional expertise into a reusable intelligence layer that can help accelerate discovery across the organization.

As AI becomes part of investment decision-making, SimCorp is helping financial institutions bring AI into investment workflows without compromising governance, auditability or control. By unifying SimCorp One on Microsoft Azure and leveraging Microsoft Foundry, the company is helping portfolio managers, risk analysts and operations teams access insights faster, automate manual processes and spend more time on higher-value decisions. By standardizing how AI is deployed and governed across global investment operations, SimCorp is creating a foundation for trusted AI at scale, helping organizations embed AI into investment workflows while maintaining the controls required in highly regulated markets. One study found SimCorp One customers realized 134% ROI over three years, improved operational efficiency by 45 percent, saved 10 hours per person per week and accelerated time to market by 50 to 60 days.

Stellantis is accelerating AI-led strategy and digital transformation across its global business, co-developing more than 100 AI initiatives across sales, customer care, product development and operations. By applying AI-powered insights across the business, the automaker is streamlining product development and validation, advancing predictive maintenance and bringing new digital features and services to market faster for customers. Stellantis is also deploying an AI-driven global cyberdefense center to help protect vehicles, customers and operations worldwide while modernizing its infrastructure on Microsoft Azure with a targeted 60% reduction in its datacenter footprint by 2029, powering a more scalable and interconnected digital ecosystem for future digital and connected services and resilient operations.

Facing a growing volume of cyberthreats across a complex healthcare environment, St. Luke’s University Health Network is using Microsoft Security Copilot to help protect the systems clinicians and patients depend on every day. Across 15 campuses, 300 outpatient sites and more than 2.5 petabytes of data and patient records, the organization needed a unified view of threats across a complex environment. Security Copilot connects Microsoft Defender, Microsoft Sentinel, Microsoft Entra, Microsoft Purview and other security tools, helping analysts correlate threats faster, eliminate silos and respond with greater precision. St. Luke’s is saving nearly 200 hours each month in phishing alert triage and creating incident reports in minutes instead of hours, helping security teams focus more time on protecting patient care.

University of Kentucky unified more than 150 AI initiatives across classrooms, research labs, healthcare settings and administrative offices through its CATS AI governance framework. Standardizing on Microsoft’s AI portfolio — including Microsoft 365 Copilot, Microsoft Dragon Copilot, GitHub Copilot and Microsoft Azure — the university achieved campus-wide deployment, providing more than 70,000 students and employees with access to AI capabilities. Clinicians are using Dragon Copilot to reduce documentation burdens and spend more time with patients, and students are using GitHub Copilot to become active builders of digital solutions, such as the Socratic Tutor: an AI-powered learning platform aimed at helping medical students master complex curriculum.

Looking back on FY26, I am inspired not only by the continued pace of AI innovation, but by what our customers are achieving. Across every industry and segment, organizations are turning their unique IQ into strategic advantage with continuously improving agentic workflows. The companies leading this next phase are building, observing and tuning agentic workflows against business outcomes. Intelligence compounds, trust scales and AI can work in the flow of human ambition. As Frontier Firms redefine what is possible with AI, we remain focused on helping our customers amplify and protect their intelligence so they can transform how they operate, compete and grow.

Judson Althoff is the chief executive officer of the commercial business at Microsoft. He is responsible for the product strategy, sales, services, support, marketing, operations and revenue growth of the company’s commercial business, which operates in more than 120 regional and national subsidiaries globally.

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Why security needs a new Cyber Stack — Introducing Project Perception

The physics of cybersecurity are changing. Autonomous systems can now reason, adapt and operate continuously. At the same time, the cost of offense is falling, while the volume, velocity and complexity of what must be secured continues to grow. Attackers can generate exploits faster, scale campaigns further and operate with unprecedented efficiency. The approaches built for a world of human actors cannot keep pace with a world of AI, agents and machine-speed attacks.

Security needs a new Cyber Stack. A new Cyber Stack must continuously perceive risk across the entire digital estate, reason across vast amounts of context and take action at machine speed. It must learn and adapt as environments evolve, helping organizations stay ahead of threats. And because security is ultimately a human mission, it must amplify defenders with better insights and more powerful ways to act. The defining characteristic of the next generation of security systems will not be their ability to generate more alerts. It will be their ability to continuously perceive, reason and act.

That vision led us to build Project Perception. A new agentic security system designed for the realities of AI. It turns signals into real-time protections using AI to defend against AI.

Project Perception brings together signals, context, models and specialized agents into a continuously learning system of defense. It can reason, prioritize and act at machine speed while keeping humans firmly in control and empowering them with powerful new workflows.

Project Perception is based on a simple idea: effective defense requires continuous understanding of how an attacker sees the world, how a defender evaluates risk and how protections are improved over time. To accomplish this, Perception coordinates three classes of specialized agents. Red team agents identify potential paths to compromise before an attacker can exploit them. Blue team agents investigate, reason over context and determine what represents meaningful risk. Green team agents take corrective actions and strengthen defenses across the environment. Working together, these agents form a closed-loop system that continuously discovers, evaluates and improves an organization’s security posture.

A system like Project Perception is only as effective as the visibility it has, the actions it can take, the experience of the teams building it and the models it can use. Microsoft brings together all four.

We see across identities, endpoints, applications, data, clouds and AI systems, providing broad visibility across the digital estate. Equally important, we can help customers take action across those environments. Combined with decades of security research, threat intelligence and real-world operational experience defending organizations, these capabilities shape how Project Perception reasons, prioritizes and responds.

Security is a 24/7 mission. Organizations need protection that is highly effective, continuously available and affordable at scale. That requires more than access to the most capable model. It requires applying the right model to the right task. Project Perception adopts a multi-model architecture that combines frontier and specialized cyber models, optimizing for both quality and cost.

As part of this multi-model strategy, we are committed to bringing customers the best models for each security task, including innovating with our own specialized models. The first scenario is software vulnerability management, bringing MAI-Cyber-1-Flash inside MDASH, our software vulnerability multi-model team of agents. MDASH with MAI-Cyber-1-Flash delivers 96% on CyberGym, an industry leading benchmark, +12 points above Mythos. And this same configuration delivers almost 50% of cost savings vs. the current MDASH configuration in market today. That’s the power of a well-tuned, multi-model system with access to uniquely rich historical training data. Next, Project Perception will take advantage of MAI-Cyber-1-Flash for many more security workflows, beyond the software vulnerability scenario.

We are bringing this vision to customers around the world through Project Perception, which enters public preview on August 3.

A Cyber Stack built for agentic security

Delivering agentic security requires more than adding agents to existing workflows. It requires a new Cyber Stack, designed from the ground up.

The stack begins with signals and sensors that provide awareness across the digital estate. Security context transforms those signals into token-efficient understanding that agents can use. Models provide intelligence and reasoning. A harness coordinates models and agents across security workflows. Agents apply that intelligence across security workflows and actuators translate decisions into protection. Together, these layers create a continuous learning system that can understand risk, adapt to changing conditions and improve security outcomes over time.

While each layer provides important capabilities, the power of Project Perception comes from how they work together.

Security context built for AI

Effective reasoning requires more than raw signals. Agents need context.

Microsoft transforms its breadth of visibility, threat intelligence and security expertise into a security context that connects security data, knowledge and semantics across the digital estate. The result is a continuously updated representation of an organization’s assets, identities, relationships, risks and activities that gives agents a shared, near real-time, understanding of the environment they are helping to defend.

This shared understanding is foundational to how Project Perception operates. Rather than forcing agents to continuously gather, correlate and reconstruct context from raw signals, it provides them with immediate and token-efficient access to the information they need to reason over risk, prioritize actions and make decisions. By grounding every interaction in this rich security context, Project Perception improves the accuracy and consistency of reasoning while reducing the time, compute and cost required to operate at scale.

A multi-model architecture built for security

No single model will be optimal for every security task. Effective cyber defense requires applying the right model to the right problem at the right time.

For Project Perception, the right model is determined by the combination of quality, reliability, latency and cost. Rather than relying on a single model, Project Perception adopts a multi-model architecture that continuously selects the capabilities best suited to the task, optimizing for both effectiveness and economics. Because security is an always-on mission, sustainable economics are essential to operating protection at scale.

This approach is shaped by ongoing research, benchmarking and evaluation across frontier and specialized models. Our security researchers continuously assess models against real-world security workflows, enabling us to match each task with the model that delivers the best outcome. This allows customers to benefit from advances in AI without being tied to any single model.

Actuators — insights to actions

Security teams do not need more information. They need better outcomes.

That is why actuators are a critical part of the Cyber Stack. Project Perception is deeply integrated across Microsoft Security products, enabling agents to connect insights to actions. Organizations can continuously reduce risk rather than simply identify it, helping defenders strengthen security while remaining in control.

Built with safety first

Underpinning every layer of the Cyber Stack is a foundation of trust. Project Perception is built in alignment with Microsoft’s Responsible AI principles and inherits the security, compliance, governance and operational controls our customers already rely on. This ensures these capabilities are delivered with the same rigor, accountability and enterprise readiness that customers expect.

The future of security

Security has always been a race between attackers and defenders. AI changes the speed, scale and economics of that race. Defenders need systems that can continuously perceive, reason and act alongside them.

 Project Perception is how we begin to build that future.

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.

Other resources:

• Enhancing AI Security Through Global AI Red Teaming 
• From research to reality: An interview with Microsoft VP of Security Research Taesoo Kim

Hayete Gallot leads Microsoft’s work to help organizations operate securely in an AI-driven world. Her scope includes identity, threat protection, compliance and data security at global scale.

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Today, we’re excited to share a long-term commitment to the Department of Energy’s (DOE) Genesis Mission, backed by a $60 million investment designed to accelerate AI for science and the breakthroughs it can deliver for the country. This deepened commitment includes Microsoft’s new Scientific Partnership Advancing Research & Knowledge coordination hub and program office, otherwise known as SPARK, that is focused on facilitating collaboration and scientific discovery for the Genesis Mission.

The Genesis Mission represents an ambitious vision, bringing together DOE’s 17 National Laboratories, world-class experimental facilities, decades of irreplaceable scientific data and next-generation computing into a single, unified platform capable of transforming how science gets done. The critical goal of the Genesis Mission is to double the productivity and impact of American research and innovation within a decade by embedding AI directly into the scientific process. It’s a bold step forward and exactly the kind of moonshot that defines American science.

At Microsoft, we believe that this goal is not only achievable, but also essential as a national security imperative and an economic engine for generations to come. With this commitment and the launch of SPARK, we’re ready to partner fully in this mission and build on our collaborations across government and with academia.

Microsoft investing in AI for science

Today, Microsoft’s $60 million investment package will begin to accelerate AI for science in support of DOE and the National Laboratories. As sustained scientific impact requires more than infrastructure alone to enable lasting scientific outcomes, Microsoft’s investment package is structured in two parts:

• $40 million in Azure compute and AI credits, distributed over three years, to power the large-scale AI and scientific workloads at the heart of the mission. This gives researchers room to train, simulate and iterate at a scale that matches their ambition.
• $20 million in solution engineering enablement services. Dedicated engineering, architecture, deployment, adoption and acceleration support to turn cloud and AI capacity into operational scientific outcomes. This investment helps ensure that the Genesis Mission advances in practice and in principle.

Together, this investment reflects a simple conviction: While infrastructure opens the door, it is people, expertise and disciplined delivery that carry discoveries across the finish line. Microsoft is committing to both.

Introducing SPARK: Microsoft’s catalyst for advancing scientific partnerships

Great partnerships need more than good intentions and good technology — they need a clear way to work together. So, alongside this commitment, we are standing up a new program office and coordination hub: SPARK, or Scientific Partnership Advancing Research & Knowledge.

SPARK is the single front door and coordination hub for Genesis Mission collaboration with Microsoft. SPARK orchestrates the full breadth of what Microsoft brings to bear: program, technical, research, engineering, security, compliance, partner and field teams into one clear, consistent path from first idea to deployed science.

SPARK is built around five commitments:

• A dedicated Genesis Mission Program Management Office. This office handles intake, prioritization, a disciplined sprint-and-checkpoint cadence and a steady operational interface with DOE for alignment, reporting and sustained partnership.
• An AI for Science Center of Excellence. An integrated delivery team that moves use cases from concept to secure, compliant, scalable implementation, with enablement through office hours, hackathons and training grounded in responsible AI and reproducible research.
• Optimization of Azure credits. Ensures computing resources are directed toward the projects where they can accelerate science most.
• Management of technical services. Dedicated support to help labs adopt and operationalize AI-for-science capabilities, delivered by Microsoft teams and select partners.
• Joint research and development. A focused set of Genesis Mission-aligned challenge problems, co-developed by Microsoft researchers and DOE scientific leadership, with milestones, publications and IP governed by mutually agreed terms.

Underneath SPARK sits the full breadth of Microsoft’s secure, FedRAMP-authorized cloud and AI portfolio. This includes Microsoft Azure infrastructure, Microsoft Foundry and Zero Trust security through Microsoft Defender, Sentinel and Entra. Azure capabilities will help extend the DOE’s American Science Cloud capabilities by providing computing, AI, data and collaboration services that complement existing scientific infrastructure. Together, these capabilities will help accelerate discovery, enable secure collaboration across institutions and provide researchers with flexible access to advanced technologies and resources. This will enable DOE and National Laboratory scientists to move faster from hypothesis to discovery, without compromising the security, reproducibility and governance that mission-critical research demands.

Accelerating science with Microsoft Discovery and Quantum

Microsoft’s new commitments build on capabilities we recently announced through Microsoft Discovery and Microsoft Quantum. They are designed to accelerate scientific research and will support the Genesis Mission’s ambition to bring AI, advanced computing and emerging technologies more deeply into the scientific process.

To enable a unified research platform for Genesis Mission work, Microsoft will provide access to Microsoft Discovery, our integrated platform that unites AI models, simulation, data and experimental workflows driven by advanced cognition rooted in scientific method, within a single governed environment. Microsoft Discovery, now generally available, includes support for autonomous lab orchestration, integration of Microsoft Research’s AI models for science, continuous AI learning through the scientific loop and agentic memory, advances in Discovery Bookshelf for data curation and scalable indexing and deeper integration and multi-hop reasoning over enterprise science data estates, including data governance. Together, these capabilities are designed to help researchers move more quickly from data to insight, from simulation to experiment, and from promising idea to scientific breakthrough.

We also recently announced the preview of the Microsoft Discovery app, a local desktop experience that helps researchers, students and scientific teams begin working with Microsoft Discovery today. Our support for the Genesis Mission will include access to the Microsoft Discovery app to enable DOE and National Laboratory teams with early access to its capabilities.

Microsoft’s recent quantum progress further strengthens the foundation for Genesis Mission work. With Majorana-based quantum advances, including more reliable topological qubits and a roadmap toward scalable quantum computing, Microsoft is helping move quantum from long-range research toward practical scientific capability. For DOE and National Laboratory teams, that progress can open new ways to model complex materials, chemistry, energy systems and national security challenges that are difficult or impossible to solve with classical computing alone. Just as important, the same agentic AI for science approach behind Microsoft Discovery is helping accelerate quantum research itself, creating a reinforcing cycle in which AI speeds quantum breakthroughs and quantum computing expands what scientists can ultimately discover.

The work is already underway

Working alongside and supporting federal researchers and universities, we’re already seeing real-world impact, from faster materials screening to advanced biosurveillance modeling. Four of the first projects taking shape under this partnership show what that looks like in practice:

Discovering critical energy storage materials and biosystems design (Pacific Northwest National Laboratory)
Using AI to dramatically accelerate the discovery of new energy storage materials through novel experimentation design and shrinking analysis of massive volumes of scientific data from years to weeks. In biosystems design, Microsoft Discovery is plugging directly into PNNL’s laboratory automation infrastructure to launch self-driving scientific workflows that autonomously design, run and fine-tune biological experiments in real time.

Strengthening biosecurity (Lawrence Livermore National Laboratory)
Pairing advanced AI with modern bioinformatics to identify emerging biological threats sooner and develop responses faster, strengthening the nation’s ability to stay ahead of the next outbreak and developing proactive defenses against evolving biosecurity risks. By applying advances in AI, scientific computing and biotechnology, Microsoft and the Lawrence Livermore National Laboratory are helping strengthen the nation’s ability to prevent, detect and respond to biological threats, advancing US biosecurity and biodefense priorities while supporting the long-term resilience and competitiveness of the US bioeconomy.

Autonomous labs for accelerated materials discovery (Johns Hopkins University Applied Physics Laboratory)
Supporting the development of AI-enabled autonomous (“self-driving”) laboratories through the integration of advanced foundation models and simulation tools — including MatterGen and MatterSim — to rapidly design, execute and optimize experiments for structural materials and superconductors. By combining autonomous experimentation with accelerated computational materials discovery, this approach dramatically shortens the path from scientific hypothesis to validated, manufacturable, mission-enabling materials, while exploring a vastly larger design space than is possible through traditional methods.

Accelerating nuclear energy permitting and autonomous energy operations (Idaho National Laboratory)
Applying AI to streamline the licensing, permitting and review of new nuclear projects. AI helps automate the development of complex engineering and safety analysis materials, a historically time-consuming and expensive process, to get this critical documentation into the hands of regulators and safety reviewers sooner. The lab also demonstrated that secure, distributed, hyperscale cloud can enable autonomous remote operations for nuclear power generators. Together, these efforts are helping bring safe and reliable power online faster and ensure scalable, efficient and reliable operations to meet the nation’s surging demand for energy.

Why this matters

We are entering an era where AI and quantum don’t just support the scientific process — they are essential to it. Through the Genesis Mission, Microsoft is committing to work as part of America’s research enterprise by providing hyperscale compute, advanced models, emerging quantum capabilities and dedicated expertise that run alongside the labs’ own world-leading systems. We are helping to ensure that the nation’s scientific strength is built on American technology. This commitment builds and expands upon the decades of collaboration through Microsoft Research and partnerships across the US government and our national labs. Now is the time to scale these breakthroughs across the entire R&D ecosystem.

The mission ahead is significant, and it should be. Whether it’s advancing the frontiers of energy, chemistry and materials science, hardening our national security or simply giving the country’s best scientists better tools to ask bigger questions, this is work worth doing and worth doing together. With a $60 million investment and SPARK, Microsoft is ready to help ensure America’s scientific leadership continues to accelerate.

Fueling scientific discovery is one of the most important things these technologies can do. AI and quantum are transforming how we tackle the toughest challenges facing the country, from sustainable materials and clean energy to biosecurity and drug discovery. The Genesis Mission is a pivotal step toward advancing that progress. We commend the DOE’s vision in building a unified, holistic approach to these critical national missions. For America to seize this opportunity and set the global standard, we must put these breakthrough technologies to work advancing the nation’s research and development.

We’re ready to deepen this partnership across government and academia. We move faster together. I’ve never been more optimistic about what’s possible when the nation’s scientific talent meets the best of modern AI and quantum. The Genesis Mission is that moment. Let’s get to work.

To get started, reach out to your Microsoft account team or email genesismission@microsoft.com.

Chris Barry leads Microsoft’s US Public Sector business. He partners with federal, state and local government and higher education agencies and organizations to accelerate digital transformation, bringing Microsoft’s secure cloud and AI capabilities to mission-critical work that serves communities across the nation.

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AI workloads are scaling faster than any single infrastructure approach can support — with more models, new agent-driven workloads and surging compute demand driving the need for greater specialization across the stack. To meet this need, Microsoft continues to evolve Azure’s infrastructure, including expanding its AI fleet with AMD’s most advanced AI and high-performance computing (HPC) solutions.

Our approach to AI infrastructure is designed to support the breadth of how AI systems are built and run. We closely work with industry innovators like AMD as well as our own purpose-built silicon and systems to provide customers with a comprehensive, open and heterogenous platform to achieve the best performance, cost and energy efficiency outcomes.

Building on our close collaboration with AMD, Microsoft is bringing AMD’s latest Helios AI platform and next-generation EPYC datacenter processors to Azure. These technologies will power three upcoming Azure offerings: HDv2 VMs for data processing, HXv2 VMs for electronic design automation (EDA) and ND MI455X v7 VMs for AI inference workloads.

Expanded infrastructure for inference, AI data systems and chip design

Built for AI data systems — Azure HDv2

CPU infrastructure is essential to the performance and efficiency of modern AI systems. AI accelerators depend on high-density, power-efficient CPU compute to process data, coordinate workloads and keep pipelines running at scale. Without this, training jobs don’t have enough data to learn from, and agents don’t have enough capacity to perform tasks on behalf of customers. Azure HDv2 virtual machines are one of our latest offerings designed from the ground up to eliminate these bottlenecks and empower massive agentic workload adoption.

Co-designed with AMD, HDv2 VMs expand Azure’s portfolio of purpose-built solutions for the most demanding CPU workloads from AI customers, including data preparation, search, reinforcement learning and agent coordination at scale. Featuring nearly 500 physical 6^th Gen AMD EPYC CPU cores, 4 terabytes of RAM, 32 terabytes of local NVMe storage and 400 Gb Azure Boost networking, HDv2 VMs are built for the workload needs of our most demanding AI customers.

Optimized for silicon design and technical computing — Azure HXv2

The AI era has created tremendous need and opportunity for firms developing the silicon products that power this infrastructure. For this reason, Azure HX virtual machines, launched in partnership with AMD in 2023 and featuring AMD’s unique 3D V-cache technology, have seen significant adoption among silicon design firms working to bring more capable and efficient AI silicon to market. Today, we are announcing the next step in our workload optimized journey for these customers, HXv2.

HXv2 virtual machines build on and extend the strengths of HX. They both continue the differentiation Azure offers for RTL simulation workloads by again employing 3D V-cache technology, while offering significant improvements to single threaded performance and memory. HXv2 VMs will feature 176 AMD 6^th Gen EPYC CPU cores with a clock frequency of more than 5 GHz, 50% more addressable cache per core and VM sizes with nearly 2 or 4 terabytes of RAM, helping customers optimize their workloads to memory needs.

Azure HXv2 is also designed to support a broader range of technical computing workloads including scientific simulation, engineering analysis and other distributed memory applications. The significantly increased per VM and per core performance, and the inclusion of 800 Gb InfiniBand, enable large-scale MPI-based simulations and make HXv2 an ideal fit for a wide variety of HPC customers.

AMD, a leading HX-series customer, highlights this impact directly:

“Engineering teams are pushing the limits of simulation, chip design and scientific computing. At AMD, we experience those demands firsthand as we design future AMD EPYC CPUs and AMD Instinct GPUs. Azure HX is an important platform for scaling complex EDA workloads, and we’re excited about Azure HXv2, which is designed to deliver even greater performance and scalability. We look forward to continuing our collaboration with Microsoft as we help advance infrastructure for the world’s most demanding engineering and scientific workloads.”

— Mark Papermaster, Executive Vice President and CTO, AMD

The HXv2 also leverages Microsoft’s long-standing collaboration to optimize Synopsys AI-powered EDA solutions on Azure:

“As AI compute continues to push the limits of semiconductor design, our collaboration with Microsoft on the Azure HX-series demonstrates a shared vision for enabling customers to deliver next-generation AI systems with precision and scale in accelerated design cycles. These systems have enabled Synopsys customers to reliably and efficiently leverage cloud-based compute, extending EDA workloads beyond traditional infrastructure constraints so they can meet ambitious development schedules while maximizing design quality and delivering dramatic performance gains.”

— Shankar Krishnamoorthy, Chief Product Development Officer, Synopsys

Production-scale AI inference — ND MI455X v7

ND MI455X v7 is designed for the reasoning, search and agentic workloads behind modern AI services. Powered by the AMD Helios rackscale solution, it expands Azure’s infrastructure options for large-scale inference and is designed to deliver strong performance and efficiency for demanding AI workloads.

Together, these new capabilities expand Azure capabilities while giving customers more flexibility to choose the right compute for each unique AI workflow: from inference, to data systems, to chip design. Customer choice is a core design principle built directly into Microsoft Azure, and we’re excited to bring AMD’s most advanced innovations at production scale.

To learn more about Azure’s high-performance computing and AI infrastructure capabilities, visit Azure.com.

Scott Guthrie is responsible for a set of hyperscale cloud computing solutions and services including Azure, Microsoft’s cloud computing platform, generative AI solutions, data platforms and information and cybersecurity. These platforms and services help organizations across the globe solve urgent challenges — and transform for the future.

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In 2026, reviewing Microsoft 365 security should not be seen as an IT housekeeping task. It is a business risk review. The question for owners and senior managers is simple: if someone gained access to your Microsoft 365 account today, what could they see, send, delete or steal?

The good news is that many of the most important protections are already available within Microsoft 365. The challenge is making sure they are switched on, configured properly and reviewed regularly.

Why Microsoft 365 Security Settings Matter to SMEs

Cyber criminals do not only target large enterprises. SMEs are often attractive because they may have valuable data, regular payment activity and fewer dedicated security resources. A compromised mailbox can lead to invoice fraud, customer data exposure, reputational damage and operational disruption.

Microsoft 365 is secure when it is managed properly, but default settings are not always enough for today’s threat landscape. A yearly review helps confirm that your business has kept pace with staff changes, new devices, hybrid working, new applications and evolving cyber risks.

1. Multi-Factor Authentication: The First Line of Defence

Multi-factor authentication, often called MFA, adds an extra check when someone signs in. Instead of relying only on a password, the user also confirms the login through an app, security key or another approved method.

For senior managers, the business benefit is clear: a stolen password should not be enough to access company email, files or Teams. MFA is one of the simplest and most effective ways to reduce account takeover risk.

Management question: Is MFA enforced for every user, including directors, finance staff, administrators and remote workers?

2. Conditional Access: Sensible Rules for Safer Sign-ins

Conditional Access is a way of applying sensible rules to Microsoft 365 sign-ins. For example, your business may decide that staff can access email from approved countries, trusted devices or managed company laptops, while unusual sign-ins require extra checks.

This is useful because not every login attempt carries the same level of risk. A finance manager signing in from their usual laptop in the UK is very different from an unexpected sign-in from overseas at 2am.

Management question: Do your Microsoft 365 access rules reflect how your business actually works today?

3. Microsoft Defender: Built-in Protection That Needs Attention

Microsoft Defender helps protect Microsoft 365 users, devices and email from threats such as malware, phishing and suspicious activity. Depending on your licence, it can provide protection for email, endpoints and wider cloud activity.

The important point for business leaders is that having Defender available is not the same as having it properly configured. Policies, alerts and response processes need to be reviewed so that threats are blocked or acted on quickly.

Management question: Are alerts being monitored, and does someone know what to do when Defender flags a problem?

4. Secure Score: A Board-Friendly Security Health Check

Microsoft Secure Score gives your organisation a measurable view of its Microsoft 365 security posture. It highlights recommended actions and helps track improvement over time. For senior managers, it can turn a complex technical topic into a more manageable business dashboard.

A higher score does not guarantee total protection, but it can show whether important settings are being ignored. It also helps create a prioritised improvement plan rather than relying on guesswork.

Management question: Do you know your current Secure Score, and is it reviewed as part of regular business risk management?

5. Backup: Microsoft 365 Is Not a Complete Backup Strategy

Many businesses assume that because their data is in Microsoft 365, it is automatically backed up in the way they need. Microsoft provides strong platform resilience, but that is not the same as a full business backup and recovery plan.

Accidental deletion, malicious deletion, ransomware, departed staff and retention settings can all create recovery challenges. SMEs should be clear about what can be restored, how far back they can go and how quickly the business could recover.

Management question: If a mailbox, SharePoint folder or Teams file library was deleted today, how quickly could you recover it?

6. Email Protection: Still the Front Door for Many Attacks

Email remains one of the most common ways criminals target SMEs. Fake invoices, supplier impersonation, password reset scams and malicious attachments can all arrive in an inbox looking convincing.

Good email protection should include spam and phishing filtering, attachment scanning, link protection where available, and correct domain settings such as SPF, DKIM and DMARC. Just as importantly, staff need to know how to report suspicious messages.

Management question: Are you actively reducing the risk of phishing and invoice fraud, or simply hoping staff will spot every fake email?

A Simple 2026 Microsoft 365 Security Review Checklist

• Confirm MFA is enforced for all users and administrators.
• Review Conditional Access rules for location, device and admin access.
• Check Microsoft Defender policies, alerts and response responsibilities.
• Review Microsoft Secure Score and agree priority actions.
• Confirm Microsoft 365 backup coverage, retention and restore testing.
• Review email protection settings, phishing controls and user reporting.
• Document who owns Microsoft 365 security inside the business.
• Schedule the next review before settings become outdated again.

Final Thoughts

Microsoft 365 is central to the way most SMEs operate, which is why its security settings should be treated as a business priority. A review does not have to be overly technical, but it should be structured, documented and repeated regularly.

For UK SME owners and senior managers, the goal is not to become cyber security experts. The goal is to ask the right questions, make sure the right protections are in place and reduce the chances of a preventable incident disrupting the business.

Call to action: If you have not reviewed your Microsoft 365 security settings recently, 2026 is the right time to start. A short review could identify simple changes that significantly improve your protection. Fruition stand ready to assist – contact us at helpdesk@fruitionsystems.co.uk.

Security | Fruition Systems | Microsoft Cloud Services in Hampshire

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Amy Coleman, EVP and Chief People Officer, shared the following communication with employees today.

When I stepped into this role, I promised to communicate more openly with you and share the “why” behind our decisions.

Today we are eliminating around 4,800 roles, about 2.1% of our global workforce, as we focus our people, investments, and energy on the priorities that will keep Microsoft positioned to deliver for customers in a fast-changing industry. The people whose jobs are impacted today are our colleagues and friends. They have made meaningful contributions to Microsoft, and we are deeply grateful for everything they have done.

Decisions like these are never easy, and you have my commitment that we are always looking for ways to reduce the need for job eliminations. Whenever possible, our priority is to place people into new roles aligned to the company’s highest priorities and greatest areas of opportunity. Over the past year, we have redeployed more than 4,000 employees into new roles, including another 500 this month. We will also transition four of our gaming studios to operate under new management, with the goal of preserving both their intellectual property and ongoing projects. In addition, more than 30% of eligible employees chose to participate in our recent voluntary retirement program, and we will continue exploring similar approaches in the future. While this doesn’t change the difficulty of today’s news, we will continue to do everything we can to create opportunities for our people, reduce the need for job eliminations where possible, and responsibly support those affected with care and respect.

The “why” is this: Our business is changing because the world around it is changing. The way technology is built, deployed, and used is transforming faster than at any point in my time here. Our customers’ needs are shifting, the business models that serve them are shifting, and that means the work itself – what we do, where we focus, and how we’re organized – has to transform too. Companies don’t get to choose whether their industry changes; they only get to choose whether they change with it. That means we will need to adjust resources and roles and shift how we operate so we can have the greatest impact for our customers.

I also want to be direct that the roles eliminated today are not being replaced by AI. At the same time, what is true is that AI is changing how work gets done. Some of the tasks we do every day can now be automated, and that means we all need to keep learning, keep building new skills, and keep adapting as the work evolves. Our customers are navigating this same shift, and they’re counting on us to help them through it. We can’t do that well unless we’re doing it ourselves. This comes down to two commitments: making the decisions needed to drive differentiated customer value, and supporting the people affected by them.

First, we will make the hard changes required to build differentiated products and services that deliver differentiated customer value. We are aligning our investment, people, and energy to our business priorities. Today’s changes mostly fall within our Commercial and XBOX organizations. In our Microsoft Commercial Business, they build on last week’s Frontier Company announcement, reshaping how we work and embedding our engineering experts alongside customers so we can help them accelerate their technology deployments. In XBOX, we are restructuring to position the business for long-term success. Engineering teams across the company will also evolve their structure and priorities to meet customer needs and innovate for the future.

Second, we will do this thoughtfully.  As mentioned above, we are working on alternative solutions to job eliminations, and beyond this, we will continue to invest in equipping employees with new skills, including in AI. For those who are impacted, we provide financial support and resources to help them take their next step.

I know many of you want to help those who are leaving but aren’t sure how. Reach out and check in on your colleagues. Use your network to bring people together, share what makes them exceptional, and help create connections to opportunities that might not happen otherwise.

We are still early on this journey, and there will be more changes ahead; other parts of our business will need to make similar changes. Each time, you can hold us to the two commitments.

During my time at Microsoft, I’ve seen this company reinvent itself again and again. What makes that possible has always been our people – their resilience, creativity, and willingness to keep learning.

Thank you for everything you bring to Microsoft.

Amy

Read more: Resetting XBOX.

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The good news is that you do not need to be technical to take sensible action. This guide explains what AI cyber attacks mean in plain English, why UK SMEs are attractive targets, and the practical steps business owners and senior managers should take to reduce risk.

Why AI has changed cyber crime

Traditional cyber scams often had obvious warning signs: poor spelling, strange wording, unusual email addresses or requests that simply did not sound right. AI has removed many of those clues. Criminals can now create emails, messages, invoices and even voice recordings that look and sound professional, personal and believable.

AI helps attackers research companies, write convincing messages, imitate tone of voice, translate content into perfect English and launch scams at scale. That means a small business can be targeted with the same level of sophistication once seen only in attacks against major organisations.

Why UK SMEs are at risk

SMEs are often attractive targets because they hold valuable data, make regular payments, work with larger customers and suppliers, and may not have a dedicated cyber security team. Criminals know that smaller businesses are under pressure, short on time and reliant on trusted relationships.

For an SME, one successful attack can lead to stolen data, fraudulent payments, operational downtime, reputational damage and loss of customer confidence. In some cases, the financial impact can be difficult to recover from.

The most common AI cyber threats facing SMEs

1. More convincing phishing emails

Phishing is still one of the most common ways criminals break into businesses. The difference is that AI-generated phishing emails can now be well written, highly personalised and relevant to your business. They may appear to come from a supplier, customer, colleague or senior manager.

2. Fake invoices and payment requests

AI can help criminals copy the style of real emails and documents. A finance team may receive a realistic request to change bank details, approve an urgent payment or settle an overdue invoice. These scams are designed to create pressure and bypass normal checks.

3. Voice cloning and deepfake calls

Criminals can use AI to imitate voices or create fake video content. A member of staff could receive what sounds like a call from a director asking them to make a payment, share login details or take urgent action. This is why relying on voice alone is no longer safe for high-risk decisions.

4. Faster attacks on weak systems

AI tools can help attackers find vulnerable systems more quickly. If software is out of date, passwords are weak or remote access is poorly protected, criminals may be able to identify and exploit those gaps faster than before.

5. Data theft and ransomware

AI can make it easier for criminals to identify valuable information and pressure businesses into paying. Ransomware attacks can stop access to files, systems and customer data, leaving the business unable to operate normally.

What senior managers should do now

Cyber security should not sit only with IT. AI-powered attacks often target people, processes and decision-making. Senior managers and business owners should treat cyber risk as a business risk, just like finance, health and safety or compliance.

• Introduce payment verification: Always confirm changes to bank details or urgent payment requests using a separate trusted method, such as a known phone number.
• Use multi-factor authentication: Add an extra layer of protection to email, cloud services, accounts and remote access.
• Keep software updated: Make sure devices, applications and security tools receive updates promptly.
• Train staff regularly: Focus training on realistic examples, including AI-written emails, fake invoices and voice scams.
• Back up important data: Keep secure backups and test that they can be restored.
• Limit access: Give staff access only to the systems and information they need for their role.
• Agree an incident plan: Decide in advance who does what if a cyber incident happens.
• Consider Cyber Essentials: The UK Government-backed Cyber Essentials scheme is a practical way to improve protection against common cyber threats.

Questions every SME leadership team should ask

• If someone requested an urgent payment today, how would we verify it?
• Do all key accounts have multi-factor authentication switched on?
• Could we operate if email or core systems were unavailable tomorrow?
• When did we last test our backups?
• Do our staff know how to report a suspicious message or call?
• Who would lead our response if customer data was compromised?

AI is not all bad news

It is important to remember that AI can also help businesses improve security. Modern tools can identify unusual behaviour, detect suspicious messages and support faster response. The key point for SME leaders is not to avoid AI, but to understand that criminals are using it too.

The bottom line for UK SMEs

AI cyber attacks are a serious and growing threat, but SMEs are not powerless. Most attacks still rely on familiar weaknesses: rushed decisions, weak passwords, poor verification, outdated systems and lack of preparation. By improving basic controls and making cyber security a leadership priority, UK SMEs can significantly reduce their risk.

If you are a business owner or senior manager, now is the time to review your cyber resilience. A short conversation today could prevent a costly incident tomorrow.

Need help protecting your SME from AI cyber threats?

If you are unsure where to start, begin with a practical cyber security review. Identify your biggest risks, strengthen your most important controls and make sure your people know what to do when something does not feel right. Fruition Systems can provide help and support for any aspect of Cyber Security – Security | Fruition Systems | Microsoft Cloud Services in Hampshire

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