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AI is not proven to give every attacker a permanent, universal advantage. The near-term concern is more specific: AI can compress reconnaissance, vulnerability discovery, exploit development and social-engineering work into faster, lower-barrier workflows, while defense still has to connect monitoring, validation, incident response, patching and organizational decisions. A June 2026 assessment by the National Academies says that asymmetry may favor attackers in the near term, but also identifies conditions under which continuous, AI-assisted defense could shift the balance back.
The hardest question is not whether a model can perform an impressive security task in a benchmark. It is whether a real deployment improves resilience across systems, teams and time. There is no widely accepted framework for making that comparison, and generative systems do not provide automatic guarantees of reliable behavior.
How is AI changing the relative pace of cyber offense and defense?
AI changes the economics and sequencing of cyber work. An attacker can ask a model to summarize a target, generate variants of a phishing message, inspect code for likely weaknesses, or adapt an exploit-development workflow. These activities still require access, judgment and validation, but AI can reduce the time and expertise needed for each step. That is what the National Academies means by workflow compression: several labor-intensive actions become faster to repeat and easier for less-skilled operators to attempt.
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| Activity | How AI may compress it | What still limits the result |
|---|---|---|
| Reconnaissance | Collecting and summarizing public or authorized technical information; generating hypotheses about exposed services. | Data quality, authorization, target-specific context and the need to confirm findings. |
| Vulnerability discovery | Scanning code or configurations, proposing suspicious paths and prioritizing likely weaknesses. | False positives, incomplete system knowledge and the need for expert validation. |
| Exploit development | Producing code variations, explaining error messages and iterating on proof-of-concept ideas. | Environment-specific conditions, safety controls and the requirement to test without causing damage. |
| Social engineering | Drafting convincing, personalized messages and adapting language at scale. | Reliable target information, delivery access and the victim’s response. |
| Detection and response | Correlating telemetry, summarizing incidents and suggesting containment actions. | Telemetry gaps, alert validation, decision authority, cross-team coordination and operational risk. |
| Remediation | Suggesting patches, configuration changes or safer code and ranking weaknesses. | Change windows, compatibility testing, ownership, deployment and verification. |
This is an asymmetry in the shape of work, not a measured head-to-head time score. The available expert assessment does not establish that attacks are always autonomous, that every organization is losing, or that AI has produced a universal increase in successful compromises.
What makes defenders’ assessment of AI cyber capabilities difficult?
Organizations lack a broadly accepted way to evaluate offensive or defensive cyber capability as it changes over time. The National Academies’ June 2026 rapid expert consultation notes that individual task benchmarks do not directly establish real-world security outcomes. A model that finds a flaw in a controlled sample may not improve the security of a production service whose architecture, data and change process are different.
Task performance is not resilience
A benchmark can measure whether a system writes a working snippet, classifies an alert or identifies a known weakness. Resilience asks different questions: Did the system reduce exploitable exposure? Did it detect a realistic intrusion earlier? Did responders contain the incident without unacceptable disruption? Was the fix deployed and verified? Those outcomes depend on people, access, data, software quality and governance in addition to model output.
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Generative behavior has no automatic guarantee
Generative systems are probabilistic. They may be correct in one context and confidently wrong in another, omit a critical condition, or produce unsafe instructions. A security team therefore has to evaluate the model in the environment where it will operate, constrain its permissions, log its actions and retain human or automated checks appropriate to the risk. A high score on a public test cannot substitute for those controls.
| Claim a vendor or team might make | Evidence needed before treating it as a security improvement |
|---|---|
| “The model finds more vulnerabilities.” | Precision, coverage and severity results on representative code, followed by confirmed fixes and retesting in the organization’s systems. |
| “The assistant reduces incident-response time.” | Measured time from alert to validated diagnosis and containment, with false positives, escalation delays and analyst review included. |
| “The agent can operate autonomously.” | A defined permission boundary, failure handling, audit trail, rollback path and tests against realistic adversarial and operational conditions. |
| “The AI improves security posture.” | Longitudinal evidence such as reduced exploitable exposure, faster verified remediation or fewer repeat incidents, not a task score alone. |
What evidence shows that attackers are already using AI?
The Office of the Director of National Intelligence (ODNI) provides an official example in its 2026 Annual Threat Assessment opening statement. It says that in August 2025, cyber actors used an AI tool in a data-extortion operation targeting international government, health-care, public-health emergency-service and religious institutions.
That account establishes reported use of an AI tool in a malicious operation. It does not establish that the operation was fully autonomous, that the tool performed every stage, or that AI was the decisive cause of success. The distinction matters because “AI-assisted” can include drafting, analysis or code generation under human direction, while “autonomous attack” implies a much broader capability that the statement does not claim.
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Why are defenders facing roadblocks?
Defense crosses systems and organizations
An attacker can stop after obtaining a useful result. A defender must connect identity, endpoint, network, cloud, application and physical processes, often across separate teams or suppliers. The response may require legal review, communications, business approval and a maintenance window. If the organization cannot access the necessary logs or does not know who can authorize containment, a faster analytic model cannot complete the job.
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Validation is part of the work, not an optional review
False positives waste scarce analyst time; false negatives leave an intrusion undetected. AI-generated vulnerability reports and remediation suggestions therefore need confirmation against source code, configuration, runtime behavior and business impact. Skipping validation can turn a tool intended to reduce risk into a source of outages, insecure changes or misplaced priorities.
Software quality and patch processes set the ceiling
Weak development practices, unsupported components, untracked assets and slow change approval create exposure that an AI assistant cannot remove by itself. The National Academies places stronger baseline security, improved software quality, faster threat response and better threat-intelligence sharing at the center of the defensive response. AI can help discover and prioritize weaknesses, but experts and owners still have to repair them.
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Dual-use capabilities create control problems
The same technique that identifies an exposed service or generates a test case can help an attacker. Access controls, environment separation, monitoring and review must reflect that dual use. A model connected to production systems should not receive broad permissions merely because it can produce useful suggestions.
AI adds a new attack surface
NIST explains that AI security overlaps with conventional software and data security while adding risks such as evasion, model extraction, membership inference and availability attacks. Evasion manipulates inputs to cause a system to misclassify; model extraction attempts to recover information about the model through queries; membership inference seeks to determine whether particular data appeared in training; and availability attacks aim to degrade or deny service. These risks apply alongside familiar threats to credentials, networks, applications and data.
NIST’s AI security and resilience program, updated August 14, 2026, describes work on control overlays for large language models, predictive AI, agents and AI developers, as well as a test platform for evaluating vulnerabilities and defenses. Its Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, published March 24, 2025, provides terminology for analyzing these threats. The practical implication is that an AI deployment needs protection for its data, model weights, configuration, interfaces, access, integrity and availability.
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What can defenders do to respond to AI-enabled threats?
The following measures are mutually reinforcing. None is a guarantee against compromise; each addresses a dependency that workflow compression alone cannot solve.
- Raise baseline security before adding automation. Maintain an accurate asset inventory, remove unnecessary exposure, enforce strong authentication and least privilege, keep supported software, and collect logs that responders can actually access. Improve software quality through secure design, code review, dependency management and repeatable release controls.
- Shorten the path from discovery to verified repair. Use AI to inspect code, configurations and vulnerability data, then route findings to an owner with a severity rationale and a due date. Require human or rule-based confirmation before changes reach production. After deployment, retest the weakness and record whether exposure was actually reduced.
- Use AI for triage with bounded authority. Let systems summarize alerts, correlate related events and propose containment. Define which actions are automatic, which require approval and how to roll back a mistaken change. Preserve the underlying evidence so an analyst can reproduce the reasoning rather than trusting an opaque conclusion.
- Measure operational outcomes, not just model scores. Track time to validate an alert, time to contain, time to remediate verified critical weaknesses, repeat findings and the rate of harmful recommendations. Compare results with a defined baseline in the same environment. A benchmark can inform selection, but it cannot prove that a deployment improves resilience.
- Secure the AI system as a production asset. Protect training and retrieval data, model weights, prompts, connectors, secrets and configuration. Restrict who can query or update models, monitor for anomalous use, test for evasion and extraction, and plan for service degradation or unavailability. Treat model and data changes as controlled releases with an audit trail.
- Exercise realistic failure modes. Test prompt injection, malicious or poisoned data, unsafe tool calls, misleading outputs, access-control bypasses and denial-of-service conditions. Include the people and approval paths that would be involved during a real incident; a laboratory result that ignores those dependencies overstates readiness.
- Share threat intelligence and coordinate responsibility. Exchange indicators, tactics, vulnerability information and lessons learned with trusted partners and relevant public-sector channels. Agree in advance who can isolate systems, notify affected parties, release patches and communicate during a cross-organization incident.
- Sustain investment. Continuous defense in depth requires funding for telemetry, skilled staff, secure engineering, testing and maintenance. Short pilots can demonstrate a task; they do not establish durable protection if the surrounding process is not maintained.
Can AI help defenders catch up over time?
Yes, but only conditionally. The National Academies says continuous AI-driven defense in depth could shift the balance toward defenders if sustained investment and coordination occur. In that model, AI operates across prevention, detection, response and recovery: it helps find weaknesses before exploitation, watches for changes across many systems, accelerates validated containment and supports rapid, tested repair. The advantage would come from a maintained defensive system, not from a single autonomous tool.
Several conditions determine whether that possibility becomes practical:
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- Coverage: the system must see the assets, identities, code and network activity that matter.
- Trustworthy actions: recommendations need validation, and automated actions need narrow permissions and rollback.
- Fast remediation: findings must reach owners who can make and verify changes.
- Resilient AI: models and data must be defended against manipulation, extraction and availability attacks.
- Coordination: teams and organizations must share information and maintain compatible response procedures.
- Evidence: leaders need outcome measurements that reveal whether the deployment reduces real exposure rather than merely producing impressive demonstrations.
Without those conditions, AI may increase the volume of alerts, recommendations or attack attempts faster than an organization can validate and repair them. With them, the same ability to compress repetitive work can be applied continuously on the defensive side. The present evidence supports that as a credible path, not as a guaranteed forecast.
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