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AI does not currently favor attackers or defenders in every situation. It gives both sides task-specific advantages, and the outcome depends on the model’s capability, the safeguards around it, access to data and infrastructure, and how prepared an organization is to deploy and supervise controls. AI can speed detection, triage and response, while also lowering the cost or improving the quality of reconnaissance, social engineering and other offensive work. Current evidence supports a growing, uneven capability—not a claim that general-purpose systems autonomously conduct sophisticated intrusions from end to end.
How is AI changing cybersecurity?
AI is changing cybersecurity by compressing work that once required more time, specialist knowledge or manual scale. On the defensive side, systems can help analysts sort alerts, identify patterns, summarize incidents, draft detection rules and support response decisions. On the offensive side, the same broad abilities can assist with target research, convincing messages, content generation and parts of cyber operations.
The important qualification is that these are task-level effects. A model that writes a plausible phishing message or groups similar alerts has not demonstrated that it can independently compromise a well-defended network, maintain access and achieve an operational objective. The International Scientific Report on the Safety of Advanced AI, interim report updated 22 October 2025, said general-purpose AI could lower barriers to cyberattacks and scale activities such as social engineering, but found no substantial evidence at that time that it could automate sophisticated cybersecurity tasks.
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Can AI help hackers?
Yes. Government assessments describe AI as improving some malicious activity’s quality, scale, precision or efficiency, while stressing that the effect is uneven across tasks and threat actors.
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What Canadian assessments say
Canada’s National Cyber Threat Assessment 2025–2026 says AI is almost certainly lowering barriers and enhancing the quality, scale and precision of malicious cyber activity. It describes use by cybercriminals and state-sponsored actors. This is a Canadian national assessment of trends, not a measurement that every attacker in every country has advanced AI capability.
What the UK assessment forecasts
The UK National Cyber Security Centre’s report on the near-term impact of AI on the cyber threat says AI will almost certainly increase the volume and impact of cyberattacks over the next two years. It identifies reconnaissance and social engineering as areas where AI can provide uplift, while emphasizing that gains will not be uniform.
What “help” means in practice
- Reconnaissance: AI can organize publicly available information, identify likely people or systems of interest and help an operator prioritize research.
- Social engineering: It can produce fluent, personalized text and adapt messages to a target’s context, reducing the effort needed to create lures.
- Operational support: Models can assist with translation, scripting, documentation and repetitive analysis. Assistance still depends on the operator’s access, instructions, tools and ability to validate results.
These uses can make existing campaigns faster or more credible without turning the model into an autonomous attacker. Human decisions, infrastructure, credentials and target-specific knowledge remain material constraints.
Can AI be used to defend against cyberattacks?
Yes, but defensive value appears only when an organization integrates the system into sound security processes and keeps people accountable for decisions. AI can augment detection, triage and response; it does not compensate automatically for weak identity controls, unpatched systems, poor logging or an untested incident plan.
Where defensive assistance is useful
- Detection: Models can help correlate events, surface unusual behavior and turn large volumes of telemetry into analyst-ready explanations.
- Triage: They can summarize alerts, link related indicators and suggest which cases deserve immediate investigation.
- Response support: AI can draft queries, playbook steps or communications for a human to review before action.
- Knowledge access: Natural-language interfaces can help staff find procedures and interpret technical evidence, provided the underlying data is current and access is controlled.
Every one of these uses has failure modes: missed signals, confident but wrong explanations, unsafe automated actions, data leakage and overreliance by tired analysts. A deployment needs measurable objectives, logging, approval points and a way to disable or roll back an action.
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Does current evidence show that AI favors attackers?
No single reliable measure establishes AI’s net effect on the overall attacker–defender balance. The evidence differs by the question being asked, so broad verdicts hide important distinctions.
| Comparison | What the evidence supports | Why the distinction matters |
|---|---|---|
| Attack assistance vs. defense assistance | AI can aid reconnaissance, social engineering and other offensive tasks, while also supporting detection, triage and response. | The same capability can have different value depending on access, workflow and oversight. |
| Demonstrated capability vs. forecast | Evaluations show what tested systems did; Canadian and UK assessments describe likely trends and near-term effects. | A forecast is not proof that every threat actor already has the predicted capability. |
| Task performance vs. real-world operation | Benchmarks measure bounded tasks under test conditions. | Real intrusions require sustained access, infrastructure, adaptation and operational judgment. |
| Model capability vs. safeguard robustness | Capability and resistance to misuse are separate properties. | A powerful model with weak safeguards can create a different risk from a similarly capable, well-controlled system. |
| Potential controls vs. organizational adoption | Guidance identifies controls, but outcomes depend on whether an organization implements and monitors them. | Buying an AI-enabled product is not the same as reducing exposure. |
The National Academies’ Implications of AI for Cybersecurity: A Rapid Expert Consultation (2026) captures the strategic asymmetry: “The adversarial, asymmetric nature of cybersecurity makes these advances especially consequential: defenders must protect every asset at all times, while attackers need only succeed once.” The statement explains why even modest offensive improvements can matter, but it does not establish a numerical net advantage for either side.
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What do the latest AI cyber evaluations actually demonstrate?
The UK AI Security Institute’s Frontier AI Trends Report, which reports two years of evaluations, found that models completed apprentice-level tasks 50% of the time on average, compared with just over 10% in early 2024. That is a capability trend in AISI’s evaluations—not the percentage of cyberattacks an AI can perform.
How to interpret the 50% figure
An apprentice-level task result indicates that models are becoming more useful on bounded cyber problems. It does not show that they can independently plan and execute a sophisticated intrusion, evade a capable security team or operate reliably without supervision. Test design, available tools, prompts and scoring conditions all shape the result.
Why safeguards must be measured separately
AISI reports vulnerabilities in every system it tested. That finding applies to its sample and methods; it should not be generalized to every AI product. The report also describes a 40-fold difference in the expert effort required to jailbreak two models released six months apart for certain malicious-request categories. The result is limited to those tested models and categories, but it illustrates why capability progress and safeguard robustness must be tracked as separate dimensions.
What kinds of AI security risk should an organization manage?
A practical program should cover three connected problems: malicious use of AI, attacks on AI systems themselves and harm to users or business processes that rely on AI.
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- Strengthen identity, multifactor authentication, patching and least-privilege access so faster reconnaissance does not become easy compromise.
- Train staff to verify unusual requests, payment changes and urgent messages even when language and personal details look convincing.
- Monitor for abnormal account behavior, data access and automated activity, and rehearse how to investigate it.
2. Protect AI systems
- Inventory models, plugins, agents, data sources and the permissions they receive.
- Separate untrusted content from instructions, restrict tool execution and log prompts, outputs and consequential actions.
- Test for prompt injection, data poisoning, sensitive-information disclosure, model misuse and unsafe tool calls before and after updates.
3. Protect users and business processes
- Define which decisions require a named human approver and which can be automated.
- Validate outputs against authoritative records rather than treating fluent text as evidence.
- Provide an escalation path when the model is uncertain, unavailable or producing inconsistent results.
This three-pillar structure comes from the Canadian Centre for Cyber Security’s Top 10 artificial intelligence security actions: A primer. It prevents a common blind spot: defending a model while neglecting the people and workflows that can be manipulated through it.
How are attacks on AI systems different from AI-assisted attacks?
An AI-assisted attack uses a model to help target an ordinary system or organization. An attack on an AI system targets the model, its data, its interface or the tools connected to it. The distinction matters because the controls and terminology differ.
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NIST’s Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (2025) provides a shared vocabulary for adversarial machine-learning attacks and mitigations. Use that taxonomy when documenting threats such as manipulated inputs, poisoned training data or attempts to extract sensitive information. Separately, assess how a model might help someone attack email, identity, endpoints or cloud services.
A single incident can involve both categories. For example, an attacker might use a language model to craft a message, then exploit a vulnerable AI support agent through prompt injection. Incident response should record the ordinary cyber objective, the AI component and the permissions or data that made the chain possible.
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Start with a bounded problem and measurable controls, not with a general promise that AI will “transform” security.
- Choose a narrow use case. Define whether the goal is alert summarization, investigation support, detection engineering or another specific task.
- Map data and permissions. Identify what the system can read, where prompts and outputs are stored, and which actions it can trigger.
- Set human approval gates. Require review for account changes, blocking actions, code deployment, external communications and other high-impact decisions.
- Test with representative cases. Measure false positives, false negatives, unsafe recommendations, latency and analyst time under normal and adversarial inputs.
- Monitor after launch. Log use, sample outputs, test abuse resistance and review performance after model, data or workflow changes.
- Keep a fallback. Maintain a manual procedure and a rapid shutdown path for model failure, compromise or provider outage.
These steps sit on top of foundational controls: strong authentication, timely patching, network and endpoint visibility, tested backups, clear ownership and practiced incident response. AI can improve a mature process; it can also make a weak process fail faster and at greater scale.
What is the most defensible conclusion about AI’s security impact?
AI is best understood as an uneven force multiplier. It is already improving performance on some bounded cyber tasks, and official assessments expect it to lower barriers or increase the scale of selected malicious activities. At the same time, no reviewed evidence provides a reliable overall score showing that attackers have won—or that defenders have.
The practical question is therefore conditional: for a particular task, who has the better model, data, access, safeguards, monitoring and operational discipline? Organizations that improve those foundations and govern AI use can capture defensive gains while reducing the opportunities created by faster, more persuasive or more scalable attacks.
Where can I read a technical overview of AI and cybersecurity?
The National Academies Press book Implications of AI for Cybersecurity: A Rapid Expert Consultation (2026) is available in print and as a free PDF, according to its publisher. It provides an expert-consultation perspective rather than a product evaluation or a guarantee about future capabilities.
Frequently Asked Questions
Will AI replace cybersecurity analysts?
The evidence supports augmentation of specific analyst tasks, not a complete replacement. Human judgment remains important for validating ambiguous evidence, setting risk tolerance, approving disruptive actions and understanding an organization’s business context.
Can small organizations use AI safely for security?
They can begin with low-impact assistance such as summarizing alerts or drafting queries, provided sensitive data, permissions and retention are controlled. A small team should also confirm who reviews outputs and what happens when the service is unavailable.
What should employees do about AI-generated phishing?
Treat polished language, familiar branding and personal details as weak evidence. Verify payment requests, credential prompts and urgent changes through a separate known channel, and report suspicious messages so security teams can correlate them with other activity.
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How often should an AI security system be retested?
Retest after model or provider updates, permission changes, new data sources, major workflow changes and any security incident. Periodic adversarial exercises between releases can reveal drift before it affects production decisions.
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