Over the last year, one topic has dominated conversations across the cybersecurity industry: artificial intelligence.
Every week seems to bring another announcement, another capability, or another prediction about how AI will transform security. In offensive security, the discussion has become particularly intense. We are seeing AI-assisted vulnerability discovery, AI-generated attack simulations, AI-powered analysis tools, and increasingly bold claims about autonomous security testing.
Chartered Cyber Security Professional (ChCSP) and Vice President of Global Threat Services at Rootshell Security.
The question I am asked most often is surprisingly simple:
“Will AI replace penetration testers?”
My answer is equally simple: No.
What AI will do is change how penetration testers work.
And in many ways, it will make experienced penetration testers even more valuable.
AI is already changing security testing:
Let’s start with the obvious.
AI is genuinely impressive.
Modern AI models can process vast amounts of information, identify patterns, summarize findings, correlate data sources, and surface potential issues far faster than any individual analyst could achieve manually.
Within offensive security, AI is already helping teams:
- Identify vulnerabilities more quickly
- Analyze large datasets
- Correlate findings across environments
- Surface potential attack paths
- Generate documentation and reporting
- Reduce repetitive manual tasks
These are meaningful improvements.
Many of the activities that traditionally consumed valuable consultant time can now be accelerated significantly.
As a result, organizations are gaining greater visibility into their environments than ever before.
But visibility alone has never been the ultimate goal.
Finding vulnerabilities has never been the hard part:
One of the biggest misconceptions in cybersecurity is that finding vulnerabilities is the primary challenge.
It isn’t.
Understanding risk is.
Most organizations already have access to large amounts of security data. They run vulnerability scanners. They receive penetration testing reports. They consume threat intelligence. They deploy attack surface management tools. They monitor logs and alerts.
The problem is rarely a complete lack of information. The problem is understanding what matters.
- Which vulnerabilities are genuinely exploitable?
- Which attack paths represent realistic threats?
- Which issues require immediate remediation?
- Which findings can safely wait?
These questions are considerably harder to answer than simply identifying a vulnerability. And they are questions that require context.
Context is where human expertise matters
A vulnerability rarely exists in isolation.
The real-world risk associated with any finding depends on a range of factors, including asset criticality, business impact, compensating controls, user privileges, environmental configuration, attacker motivation, and the relationships between multiple weaknesses.
This is where experienced penetration testers provide value that AI alone cannot replicate.
When performing an assessment, we are not simply identifying vulnerabilities. We are thinking like attackers.
We are asking questions such as:
- How would I gain initial access?
- What would I target next?
- How could I chain these weaknesses together?
- What data could be accessed?
- How difficult would exploitation actually be?
- What is the likely business impact?
These decisions are rarely straightforward. They require judgement, creativity, and experience.
Two organizations may have the same vulnerability present within their environments, yet the associated risk could be dramatically different depending on the surrounding context.
Understanding that difference is where human expertise becomes critical.
The future isn’t autonomous testing:
There is currently a great deal of excitement around autonomous security testing. The idea is appealing. Feed an environment into an AI model and receive a complete understanding of risk in return.
The reality is significantly more complex.
Attackers do not operate according to predefined workflows.
- They adapt.
- They improvise.
- They exploit unexpected opportunities.
- They combine seemingly insignificant weaknesses into meaningful attack chains.
Successful offensive security assessments require the same flexibility.
While AI can assist with analysis and discovery, security testing remains fundamentally an exercise in understanding human behavior, business context, and attacker decision-making. These are areas where human expertise continues to outperform automation.
For the foreseeable future, I believe the most effective approach will be human-led, AI-assisted testing. Not human versus AI. Human plus AI.
AI should make penetration testers better:
The conversation should not be about replacing penetration testers. It should be about enabling them. When repetitive activities are automated, consultants can spend more time focusing on the areas where they create the greatest value.
Instead of manually processing information, they can spend more time:
- Investigating attack paths
- Validating exploitability
- Understanding business impact
- Identifying complex attack chains
- Advising clients on remediation priorities
- Delivering meaningful security outcomes
In many respects, AI allows skilled security professionals to operate at a higher level. It augments expertise rather than replacing it. The result is not fewer penetration testers.
It is more effective penetration testers.
The real challenge is prioritization:
As AI continues to improve vulnerability discovery and analysis, organizations will inevitably uncover more security findings.
That sounds positive, but it introduces a new challenge. More findings do not automatically reduce risk. In fact, without effective prioritization, they can create additional noise.
The organizations that succeed over the next decade will not necessarily be the ones finding the most vulnerabilities. They will be the ones that can most effectively distinguish genuine risk from background noise, understand how attackers are likely to exploit weaknesses in practice, and make informed decisions about where to focus finite resources.
As AI continues to improve vulnerability discovery and analysis, security teams will inevitably gain access to more data, more findings, and greater visibility than ever before. While that represents a significant advancement for the industry, visibility alone does not reduce risk. The real value lies in understanding what matters, what is exploitable, and what action should be taken next.
That is why I believe the future of security testing is not autonomous. It is human-led and AI-assisted. AI tools will continue to accelerate analysis, improve visibility, and help uncover opportunities that may previously have been missed. However, understanding business context, assessing real-world risk, and making sound security decisions will remain fundamentally human responsibilities.
The cybersecurity industry has spent years trying to solve the visibility problem. AI is helping us make enormous progress. The next challenge is prioritization, and that is where experienced security professionals will continue to play their most important role.
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