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AI is already being used to broaden coverage and speed repetitive penetration-testing tasks, but current evidence does not show it replacing skilled testers. The practical question is how to combine automated exploration with human judgment: people still matter when an application’s business logic is unusual, several weaknesses must be connected, or a finding needs to be confirmed and prioritized.
What AI can add to penetration testing
AI-assisted tools can help explore a wide attack surface, spot patterns, and pursue repetitive investigative steps. That can make it easier to cover more ground or revisit systems as they change. Edgescan describes this as a shift in how testers spend their time: automation handles more breadth and repetition, while experts focus on interpretation and validation. This is the company’s perspective and description of its own approach, not independent evidence that one service outperforms another. Edgescan’s account
That division of labor is more useful than asking whether AI or people will “win.” A tool can surface a possible weakness; a tester can determine whether it is reproducible, what access it actually permits, how it relates to the application’s intended behavior, and what deserves attention first.
Where human interpretation remains important
Application-specific business logic
Many security questions depend on what an application is supposed to do: which user may perform an action, in what order, and under what conditions. EPAM says agents in its evaluation struggled with non-standard application logic. Recognizing a technical pattern is not the same as understanding whether a workflow violates the application’s rules. EPAM’s evaluation and analysis
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Multi-step issues and unexpected behavior
A meaningful attack may require connecting several observations rather than exploiting one isolated flaw. EPAM also reports difficulties with chained exploits and unexpected behavior, including locked accounts and error messages that did not fit an agent’s expectations. A human tester can adapt the investigation when the application behaves differently from a presumed path.
Confirmation and prioritization
Before a suspected weakness becomes a remediation task, someone needs to establish that it is real and explain its significance. Edgescan emphasizes expert validation and business prioritization; these are especially important when automated output contains uncertain, duplicated, or context-dependent findings. The value of a report is not simply how many items it lists, but whether the organization can understand and act on them.
What the current numbers do—and do not—show
EPAM evaluated five open-source agents and one commercial tool against two internal web applications designed to mimic production complexity. Each application contained approximately 40 vulnerabilities. In that evaluation, the open-source tool Shannon reported 13 vulnerabilities in Test App v1 and seven in Test App v2. AWS Security Agent reported 14 in v1 and 15 in v2 in black-box mode. These are results from EPAM’s specific targets and setup, not a universal ranking or proof that an agent can independently deliver a complete professional assessment.
The figures also should not be read as a direct comparison of human and AI performance. They do not establish that one approach is more accurate overall, that the tools had equivalent access or configuration, or that the findings were equally severe. EPAM’s account is useful evidence of what happened in a bounded evaluation; it cannot settle how every tool will perform on every application.
Adoption data answers a different question. Packetlabs’ June 2026 summary of CREST research reports responses from 62 cybersecurity providers in 19 countries: nearly 70% said they used AI-enabled tools, and more than three-quarters said their use had increased significantly over the preceding year. Those are provider-reported adoption figures, not measures of test accuracy, impact, or replacement of human expertise. Packetlabs’ summary of the CREST research
How to choose an AI-assisted or human-led assessment
For a buyer or security team, the right approach depends on the target and the decision the test needs to support. Ask providers or internal teams to explain the following before comparing offerings:
- Cadence and coverage: How often are changed applications and newly exposed surfaces assessed? Which activities are continuous, and which still rely on scheduled engagements?
- Access and test mode: Is the work black-box, informed by source code, or supplied with application knowledge? Different access conditions can change what a tool or tester is able to examine.
- Human validation: Who reproduces and confirms a reported weakness before it enters a report or remediation queue?
- Business logic and exploit chains: How will the assessment test bespoke workflows and determine whether separate observations combine into a larger issue?
- Interpretation: How are technical findings translated into business risk and ordered for remediation?
- Governance: What permissions, data boundaries, and operator review apply when an AI system is used? These questions need concrete answers for the specific engagement; the cited sources do not establish a comprehensive legal or compliance framework.
These criteria help distinguish a tool that assists a tester from a service that claims to deliver a complete assessment autonomously. They also make comparisons fairer: ask what was tested, with what access, how often, and who stands behind the final conclusions.
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What to expect from the near-term model
Current practitioner and vendor accounts point toward a hybrid operating model: AI expands breadth and accelerates selected tasks, while human testers guide investigations, handle context-sensitive cases, verify findings, and explain risk. EPAM concluded that most of the open-source agents it assessed were not ready for production use and recommended human-guided assistance for repetitive work. That conclusion applies to the agents and evaluation it described, not every product or future system.
Best Value
Edgescan’s Sean Ryan, its Head of AI Development, put the company’s view this way: “The future of penetration testing is not AI or humans. It is both, applied with intent.” The statement captures a plausible direction, but it is a vendor-authored opinion rather than proof that the same balance is right for every engagement. The available evidence does not establish that AI has eliminated the need for human testers or when, if ever, that might happen.
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