Hi. I’m Francois, known as P3t3r_R4bb1t. I’m a cybersecurity leader with over 15 years of experience in information security, risk management, and ethical hacking. I’ve served as the Senior Manager of Security and Enterprise Engineering at Wayfair, and I previously held key security roles at National Bank of Canada, Videotron, and GoSecure, where I led teams, managed multimillion-dollar budgets, and developed comprehensive security programs. As a top-ranked ethical hacker on Bugcrowd (#4 out of 100,000+ active hackers), I have identified over 1,700 valid vulnerabilities across public and private programs, including US Federal Government systems, while also bringing my technical expertise and leadership skills to help organizations strengthen their cybersecurity posture through strategic risk management and offensive security initiatives.
Now that you know a little bit about me, let’s talk about artificial intelligence (AI) and where it stands in the cybersecurity space.
The rise of AI agents and automated validators has gained traction recently in the hacking and cybersecurity space. Some self-proclaimed enterprise solutions are starting to leverage vulnerability disclosure programs (VDPs) or even private bug bounty programs to train and demonstrate full automation capabilities in AI agents. What does this mean for researchers? What does this mean for the industry in general? Are these agents even ethical? Let’s explore these questions.
The concept of leveraging scripts, workflows, and automation is not new in the bug bounty world. These approaches are likely as old as the concept of bug bounty itself. Of course, the landscape has evolved quite a bit over the last 5 years, now requiring less and less human interaction. Bugs are captured by the continuous scanning of assets and pushed to queues using webhooks. Findings are validated either manually or automatically and even pushed to platforms using prewritten and heavily templated reports.
So, what does AI automation bring to the table? I would say it’s simply the following:
In its current state, I do not believe AI has the ability to provide additional depth (i.e., critical findings related directly to business-specific contexts) or the capability to efficiently circumvent proactive controls like a web application firewall (WAF) or bot detection technologies. For instance, how would an AI react if companies were to start implementing bot prevention at scale (or more simply, just denying traffic based on the AI traffic signature) to reduce the AI’s reconnaissance capabilities? A human researcher can move around this limitation rather quickly.
The other key reason why I believe AI will not replace human hunters in the short term, or perhaps even the longer term, is the need for AI to be trained. Currently, that training has to come from humans proficient in prompt engineering. Today, AI systems train on public data and complementary datasets. You don’t know what you don’t know, and the same applies to AI. In other words, an AI agent doesn’t know what humans don’t tell it. Thus, I strongly believe humans will continue to have an edge and maintain some control on that front.
Such training requirements may also trigger unwanted opacity in the future of vulnerability disclosure and research. Nobody wants their job to be replaced by AI. Therefore, in an AI-dominated world where companies fight for competitive advantage, will ethical security researchers continue to disclose their vulnerabilities publicly, or will they keep these techniques or findings to themselves for an extended amount of time? If we push this thinking slightly further, will researchers sell their research to AI companies instead? Similarly, will product manufacturers or companies disclose the vulnerabilities in their assets, or will they use incredibly vague statements (some businesses are already experts at this!) in their disclosures?
These are crucial questions to ask ourselves, and I myself am puzzled. On my end, I do see a potential case where an AI-dominated market may encourage additional secrecy, persuading bug bounty researchers or even AI companies themselves to keep their edge in a highly competitive space.
Another interesting angle that could generate additional discussion and research is AI automation costs and architecture. I discussed this before, but I personally tend to hunt manually. I limit myself to the bare-minimum tooling and automation. This strategy obviously can’t scale to a larger scope and to multiple programs at the same time. This is an area where AI agents may drastically outpace researchers. But at what cost? And what does the architecture of these solutions look like?
While AI automation may revolutionize bug bounty research at scale, the economic reality reveals hidden costs that extend far beyond simple model usage fees. An AI system capable of meaningful vulnerability discovery across multiple programs requires sophisticated infrastructure orchestrating reconnaissance engines, specialized AI models, validation pipelines, evasion mechanisms, and continuous monitoring systems. Each component demands significant computational resources, storage capacity, and operational expertise to maintain effectiveness while avoiding detection by increasingly sophisticated bot-prevention systems. Architectural complexity grows exponentially when you take into account the need for distributed scanning, real-time data processing, model retraining, and compliance monitoring across diverse program requirements.
We cannot talk about AI without discussing its ethical implications. Platforms do have terms and conditions; they have rules, and each VDP or bug bounty program implements its own set of additional limitations. Legally speaking, as a human researcher, I would obviously be brought to court if I breached agreements or negatively impacted a program. But what would the legal process look like for AI agents? How will an operator be able to fully ensure that a “robot” didn’t capture personal data or any sensitive data? If we push this even further, is an AI actually learning from the sensitive data it captured? I’m not a lawyer, but it seems the current legal frameworks or even basic safe harbors are not well suited to these situations. This is an area where technology is advancing more quickly than legal and ethical frameworks can adapt.
From a leading bug bounty researcher’s point of view, AI-based automation should be able to drastically speed up bug hunting processes, help with reconnaissance on large scopes, provide interesting aspects of a target, help pinpoint low-hanging fruit, and even submit issues to programs automatically. AI excels at processing vast amounts of data quickly, identifying patterns across extensive attack surfaces, and performing repetitive tasks that would consume significant human effort and time.
However, I personally see AI automation as far more relevant to enterprise attack surface monitoring solutions. These organizations have complex digital footprints that can benefit from AI systems that continuously scan, catalog, and assess their assets for potential vulnerabilities in real time.
Using today’s technology, I do not see this level of automation going as deep into a system as a human researcher would, and it won’t likely be able to find unique business-context vulnerabilities. Human researchers bring critical thinking, creativity, and contextual understanding that AI currently lacks. Researchers can identify logic flaws specific to business workflows, understand the nuanced implications of seemingly minor issues, and chain together multiple small vulnerabilities into significant security impacts. The most sophisticated vulnerabilities often require understanding not just the technical implementation but also the business logic, user behavior patterns, and organizational context. Only human intuition and experience can provide this level of understanding.
However, no one can really predict if a breakthrough will be made to significantly boost AI’s capabilities. As AI becomes more and more sophisticated at contextual reasoning, this gap might narrow.
With all that said, I remain confident that humans will continue to have a place of choice in the bug bounty (or even the cybersecurity) ecosystem, with the future likely showing a complementary relationship; AI will handle the breadth while humans will provide the depth and creative problem-solving that high-value, complex vulnerabilities demand. One thing is for sure: no one really knows how AI will effectively change the paradigm in the cybersecurity space. Only time will tell.