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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →AI is changing cybersecurity on both sides: it can help defenders find and fix weaknesses, while helping attackers conduct reconnaissance, discover vulnerabilities, and scale social engineering. The concern is not that AI has made security obsolete; it is that organizations may struggle to assess and control new risks as quickly as AI capabilities and deployments change.
How AI is changing cybersecurity
Generative AI, reasoning systems, and agents can assist with tasks across the attack lifecycle. The National Academies of Sciences, Engineering, and Medicine’s 2026 rapid expert consultation identifies vulnerability discovery, exploit development, reconnaissance, and social engineering as activities that may become faster, cheaper, or easier to scale. Defenders can also use AI to identify weaknesses and support security work.
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The near-term balance may favor attackers because parts of an attack can be compressed more readily than defense across complex organizations. That is an expert assessment, not a universal or quantified result. Monitoring, validating alerts, coordinating responses, and maintaining software still require people and organizations to act across interconnected systems. Faster discovery does not reduce risk unless findings are validated, assigned, prioritized, and fixed.
The same consultation says there are no widely accepted frameworks for evaluating AI-enabled cyber capabilities or tracking their evolution. A model demonstration on a particular task does not establish improved security outcomes in a live organization, where results depend on the systems involved, adversarial behavior, and resilience under manipulation. NIST likewise frames AI as dual-use: it may help defenders address vulnerabilities and enhance attacks against information technology and operational technology.
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Two different problems: attacks on AI and AI-assisted attacks
Organizations need to distinguish protecting AI systems from protecting conventional systems and people against attackers using AI. The risks overlap, but the security controls and assets at stake are not identical.
| Problem | What is at risk | Examples |
|---|---|---|
| Attacks on AI systems | Inputs, model data and weights, serving infrastructure, user data, generated outputs, and supply-chain components | Manipulation, theft, or disruption of AI components; attacks on confidentiality, integrity, or availability |
| AI used to attack other targets | Conventional computer systems, people, sensitive information, and potentially physical capabilities | AI-assisted reconnaissance, vulnerability discovery, exploit development, and social engineering |
NIST’s 2025 adversarial machine learning taxonomy gives names to several technical risks. For predictive AI, it covers evasion, poisoning, and privacy attacks; for generative AI, it also includes misuse attacks. These categories help describe possible failure or abuse modes, but their inclusion does not mean every attack is prevalent in deployed systems.
Why agents make access and action control more important
An agent can interact with tools, services, data, and other systems. That means securing it involves more than judging whether its model produces appropriate text: its identity, permissions, connected components, and ability to take action all matter.
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Joint guidance announced by the NSA and partner agencies on April 30, 2026, identifies risks involving privilege, design and configuration, behavior, system structure, and accountability. An over-privileged agent can increase the impact of a compromise; connected components expand the attack surface; and opaque behavior can complicate audits and responsibility.
Microsoft’s 2026 Digital Defense Report groups agent risks into prompt and intent manipulation, sensitive-data exposure, identity and privilege compromise, excessive agency, and operational integrity. Its examples of mitigations include prompt inspection, scoped memory and credentials, verifiable agent identity, least privilege, tool allow-lists, runtime action gates, anomaly detection, and protections for configuration and supply chains. These are measures to consider, not guarantees that any one control will eliminate a risk.
The agency guidance recommends incremental deployment, continuous assessment against evolving threat models, clear governance and accountability, rigorous monitoring, and human oversight. These safeguards are especially relevant when agents can reach sensitive data or take consequential actions.
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What organizations can do now
Keep core security practices in place
AI does not replace basic cybersecurity. In a May 1, 2026 article, Microsoft Corporate Vice President for Customer Security & Trust Amy Hogan-Burney recommends rapid patching, access control, resilience, secure-by-design practices, Zero Trust, multifactor authentication (MFA), least privilege, and ongoing security training. These practices reduce exposure; none guarantees prevention of an AI-enabled attack.
Limit what each AI service can access and do
- Inventory the data, identities, tools, and systems available to each AI service or agent.
- Grant only the permissions needed for its task, and require validation or approval before consequential actions.
- Monitor activity and preserve enough audit information to investigate misuse and establish accountability.
For identity protection, MFA is one recommended layer. A FIDO2-compatible hardware security key is one possible way to implement phishing-resistant authentication; the sources cited here do not test or endorse any particular product.
Secure AI components as part of system security
Include models, data, infrastructure, and supply-chain dependencies in security reviews and risk management. NIST’s AI security and resilience work describes implementation-oriented control overlays for large language models, predictive AI, agents, and AI developers as work in progress—not completed final standards.
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Prepare to handle more vulnerability findings
Faster discovery helps only if security teams can keep up. Validate findings, prioritize them according to exploitability and real-world risk, assign clear owners, and ensure there is capacity to remediate. A larger queue of unverified or unaddressed findings is not itself a security improvement.
Measure operational outcomes, not just model capability
Evaluate how systems perform in their actual deployment context, including under adversarial conditions. Track whether teams can detect, investigate, and remediate problems—not merely whether a model completes a security task in a demonstration.
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Microsoft’s 2026 Digital Defense Report, which describes threat data covering July 1, 2025 through June 30, 2026, reports that 88% of enterprises are experimenting with AI agents and that 82% of leaders plan broader agent rollouts within 12 to 18 months. The report passage does not identify the survey methodology, so these figures should be read as Microsoft-reported findings, not independently verified global estimates.
The same report cites an industry projection of roughly 1.3 billion AI agents in production by 2028; the passage does not name the originating organization. It also reports 60 to 70% faster threat summarization among organizations leveraging Microsoft Security Copilot. That is a vendor-reported, product-associated result, not an independent measure of AI’s overall effect on security operations.
These figures describe adoption expectations and a particular reported productivity result. They do not quantify AI’s net effect on cybersecurity outcomes. The National Academies consultation and the reviewed NIST material do not establish a broadly generalizable numerical estimate of that effect.
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