It’s become pretty standard to expect the help of AI with automating tasks, with penetration testing being no exception. As AI-driven tools grow more sophisticated, some have posed the question: could these systems render the traditional human pen tester obsolete entirely?

We’ll explore the strengths and limitations of AI when it comes to offensive security and predict the role human red team expertise still has to play in an increasingly automated world.

Where does AI already play a role in pen testing?

AI already plays a supporting (but influential) role throughout the lifecycle of a pen test:

1. Triage and prioritization: Modern tools use machine learning to prioritize high-risk assets and pinpoint likely vulnerability clusters. AI-driven discovery platforms draw from threat intelligence feeds and past assessments to recommend focus on what matters most. This can be helpful for reducing manual overhead.
2. Validation assistance: Language models are already being experimented with to draft proof of concept exploits or fuzzing harnesses. In practice, testers combine AI-suggested payloads with their own domain expertise. This accelerates the exploit development phase by providing a first draft that the human then refines, rather than replacing it outright.
3. Phishing and social engineering simulations: AI-powered content generators can craft highly personalized spear phishing emails and landing pages at scale, drawing on publicly available data for context. While a skilled social engineer still tailors messaging by hand, AI dramatically boosts efficiency in largescale red team exercises.
4. Report writing and recommendations: After the technical work concludes, many teams use AI to assemble initial draft reports: summarizing findings, generating remediation advice, and formatting results into polished deliverables. This frees senior testers to focus on strategic context and ensure guidance aligns with the client’s risk appetite.

Why is human expertise still essential?

Even the most advanced AI tools today can’t replicate the uniquely human skills that make a penetration test truly effective. Human expertise remains essential in areas such as:

1. Threat modeling and scoping: Defining what matters most to a business, such as: identifying critical assets, understanding trust boundaries, and mapping likely attacker motivations. This work relies on nuanced judgments about risk appetite, regulatory constraints, and corporate priorities.
2. Creative attack design: Crafting novel exploits against complex business logic, chaining together disparate vulnerabilities, and improvising when a target behaves unexpectedly all demand “out of the box” thinking and domain knowledge that AI simply can’t reliably emulate.
3. Contextual interpretation: Determining which findings truly pose a material risk (versus low impact issues) requires an appreciation for how an organization’s systems are used in practice. For example, how compensating controls mitigate exposure, and what the real-world fallout of an exploit would be.
4. Ethical and legal judgment: Deciding where to draw the line isn’t something many would want to leave to AI. For example, what tactics are permissible under a given engagement, how much collateral disruption is acceptable, and how to coordinate with internal legal, compliance, and privacy teams.
5. Client communication and advice: Working with clients requires empathy, persuasion, and trust-building. Human pen testers need to translate raw technical results into clear, actionable guidance and manage stakeholder expectations. This is far more easily done through face-to-face debriefs and workshops than an AI interface.
6. Zero-day discovery and research: While AI is very good at scanning for known patterns, uncovering entirely new classes of vulnerability often involves creative experimentation, deep protocol reverse engineering, and the kind of “a-ha” moments that come from years of hands-on experience.

So to sum it up, AI excels at volume tasks like scanning, triaging, and drafting reports. Human testers remain indispensable for strategy, creativity, ethics, and the nuanced judgments that turn a list of findings into a prioritized, context aware security roadmap.

How likely is it that we’ll see AI-only pen testing in the future?

There are a few reasons why true “AI only” pen testing (with no human expertise involved) remains highly unlikely in the foreseeable future:

1. Complexity of real-world environments: Enterprise networks are complex: legacy systems, bespoke applications, and ever shifting cloud infrastructures all conspire to foil one-size-fits-all automation. AI tools excel at pattern recognition, but they struggle when infrastructure deviates from textbook configurations or when business critical workflows hinge on bespoke logic.
2. Tacit knowledge and domain intuition: Seasoned testers draw on an enormous reservoir of tacit knowledge – things like subtle indicators of misconfiguration, quirk behaviors in obscure protocols, even the telltale fingerprints of bespoke code. Those instinct driven leaps (“I’ve seen that error before; it probably means…”) are hard to translate into deterministic algorithms.
3. Ethical, legal, and engagement specific judgments: Every client engagement comes with its own risk tolerances, legal constraints, and internal politics. An AI agent can’t negotiate rules of engagement with a CFO, decide on the fly how much collateral disruption is acceptable, or deftly defuse a situation when a simulated attack goes sideways in production.
4. Adaptive resistance and countermeasures: As defenders adopt AI-driven detection and response, attackers (and by extension, red team tools) will need to innovate continuously too. They’ll need to tweak payloads, obfuscation techniques, and multistage exploits. Human creativity (for now) remains the ultimate wildcard in this cat and mouse game, one that static models can’t replicate without ongoing, expert driven re-training.
5. Trust-based relationships: Clients pay not just for vulnerability lists, but for a trusted partner who can contextualize findings, advocate for budget, and shepherd remediation. An AI “report” may list the risks, but it can’t sit across the table and build the consensus needed to prioritize fixes or drive cultural change.

What’s new with AI pen testing in 2025?

• Agentic AI platforms securing funding: In April 2025, Terra Security closed a $7.5 million seed round led by SYN Ventures and FXP Ventures for its “agentic” AI-native penetration testing platform. Terra’s solution orchestrates dozens of finetuned AI agents under human supervision to deliver continuous, deep testing at scale—underscoring that even the newest AI tools are being built to augment, not replace, skilled testers (GlobeNewswire).
• Academic breakthroughs in full automation: A February 2025 arXiv paper introduced “RapidPen,” a prototype LLM-based framework that autonomously achieves an IP-to-shell compromise with a 60 percent success rate in under seven minutes per run. While impressive, the authors stress this remains an experimental proof of concept. They say it’s far from production ready and still dependent on curated exploit databases and human oversight
• Industry discussion at RSAC 2025: At RSA Conference 2025, Cisco unveiled an opensource, 8billionparameter “Foundation AI Security Model” for defensive tasks, and Google Cloud shared that APT groups are leveraging AI (e.g. Gemini) for research and phishing. However, no truly novel “AI native” attack vectors have emerged so far. Speakers agreed the next frontier is responsible coevolution of AI in both red and blue team operations.

What to expect from AI pen testing in the next 3 to 5 years?

Over the next 3–5 years, we’ll see increasingly autonomous tools handling the ‘grunt work’, such as automated discovery, initial exploitation attempts, even draft reporting. However, every major vendor and consultancy still layers human review atop these outputs. A milestone shift to “AI only” would require breakthroughs in context awareness, continuous self supervised learning, and (perhaps most critically) new frameworks for assigning liability when automated attacks cause unintended damage.

AI has undeniably transformed the penetration testing landscape, supercharging reconnaissance, triage, and reporting. Yet as we’ve explored, it’s the blend of machine speed and human ingenuity that delivers the deepest insights, the most creative attack paths, and the nuanced guidance your organization demands. Rather than asking “Will AI replace us?”, the smarter question is “How can we harness AI to amplify our expertise?”

In other words, while AI will keep redefining the pen test toolkit, it’s far more likely to cement its role as an indispensable copilot than to replace the human pilot altogether.

Get the benefits of human-led pen testing combined with automation

Outpost24’s Pen Testing as a Service (PTaaS) platform combines best in class automated scanning with on demand access to seasoned security consultants. This means you get the speed of cutting-edge tooling and the strategic counsel of veteran pen testers. From continuous vulnerability discovery to rapid proof-of-concept development and comprehensive reporting, PTaaS lets your team focus on high value activities while we handle the heavy lifting.

Give Outpost24’s PTaaS a try to experience continuous, scalable pen testing powered by AI, supervised by experts, and tailored to your risk profile. Book a live demo.