In the rapidly evolving landscape of cybersecurity, the old methods of periodic, manual penetration tests are proving insufficient. Today’s threats are persistent, automated, and adept at exploiting the complex interplay between cloud services, APIs, and identity platforms. To discuss this paradigm shift, we are joined by Laurent Giraid, a leading technologist and expert in AI-driven offensive security. He will shed light on how autonomous testing is moving from a periodic reporting function to a continuous validation mechanism, exploring how AI uncovers systemic risks, the irreplaceable role of human creativity in a hybrid model, and what it takes to secure the very AI systems now embedded in our core business processes.
Traditional penetration tests often provide a point-in-time snapshot of security posture. How does the shift to AI-driven, persistent validation change a security team’s day-to-day risk management? Please share a specific example of how this continuous model catches exposure that a quarterly test would miss.
It’s a fundamental shift from a reporting function to a real-time validation mechanism. Instead of getting a big, static PDF once a quarter that tells you what was broken three weeks ago, you have a living, breathing system that acts as a persistent control. Think of it this way: your environment is in constant flux—a developer pushes a new configuration, an identity permission drifts, a new SaaS tool is integrated. An AI platform is always watching, continuously reassessing the attack surface as these changes happen. This means security teams aren’t just reacting to old news; they’re managing risk as it emerges. For example, imagine a cloud engineer accidentally makes an S3 bucket publicly readable on a Tuesday afternoon. A quarterly pen test might not happen for another two months, leaving that data exposed the entire time. An AI-driven platform, however, would see that change, validate that it creates a viable attack path for data exfiltration, and flag it as a critical, verifiable risk within hours, if not minutes.
Platforms like Harmony Intelligence focus on vulnerabilities emerging from interactions between system components, not just isolated flaws. Can you describe a scenario where this approach uncovers a critical risk that traditional vulnerability scanning would overlook, and how does this change how developers remediate issues?
This is where AI truly shines, by understanding context and consequence. A traditional scanner is great at looking for a known CVE in a single library or a simple misconfiguration on one server. It might flag ten separate, low-severity issues across your microservices architecture. But it has no idea how those ten things connect. Imagine a scenario where a scanner finds a minor information disclosure flaw in one API, an overly permissive IAM role on a serverless function, and a weak default setting in a message queue service. Individually, they’re just noise. An AI platform focused on systemic risk would model how an attacker could chain them together: use the API leak to get an internal service name, then exploit the weak queue setting to send a malicious message to the serverless function, and finally leverage its overly permissive role to escalate privileges and access a production database. The platform presents a validated attack path, not just a list of flaws. For developers, this is a game-changer. Instead of getting a ticket that says “Fix CVE-2023-XXXX,” they get a report that says, “Here is the exact, three-step path an attacker can take to steal customer data.” Remediation is no longer about patching a single bug; it’s about rethinking the trust relationships and logic between those services.
Some platforms blend AI with human expertise, like Synack’s trusted researcher network. In an era of autonomous testing, where does human creativity remain most critical in offensive security? Could you walk me through how a hybrid model might tackle a complex, novel attack path?
AI is brilliant at scale, speed, and persistence. It can run thousands of reconnaissance checks, validate exploit chains, and tirelessly poke at an environment 24/7. But it operates based on the models and data it has been trained on. Human creativity, on the other hand, is unmatched at understanding business context, identifying novel logic flaws, and thinking with the kind of lateral, out-of-the-box malice that leads to true zero-day discoveries. In a hybrid model, the AI acts as a force multiplier for the human expert. For instance, an autonomous platform could spend a week mapping a complex cloud environment and discover a subtle permission misconfiguration that allows read access to a seemingly unimportant metadata service. The AI might flag this as a low-priority finding because it doesn’t lead to a known exploit path. A human researcher from a vetted network sees that same finding and immediately recognizes that the metadata contains temporary credentials for a different system, a detail specific to that company’s unique architecture. The researcher then uses that non-obvious pivot point to craft a completely novel, multi-stage attack that the AI never could have conceived. The AI did the exhaustive legwork, and the human provided the creative spark to turn a weak signal into a critical breach scenario.
With AI being embedded into core business workflows, companies like Mindgard are focusing on adversarial testing of the AI models themselves. What unique risks do these AI systems introduce, and how does testing their logic and behavior differ from conventional infrastructure pentesting?
This is a whole new frontier of security. When we pentest a traditional application, we’re looking for things like SQL injection or buffer overflows—flaws in the code. When you test an AI model, you’re not just testing the code; you’re testing its logic, its data, and its decision-making process under adversarial conditions. The risks are completely different. Can I feed the model manipulated input to make it produce an unsafe or biased decision? Can I query it in a specific way to make it leak the private training data it was built on? Can I reverse-engineer the model to steal intellectual property? Testing this requires a completely different mindset and toolset. Instead of sending malformed packets, you’re crafting adversarial inputs designed to exploit the statistical and logical weaknesses of the model itself. It’s less about breaking the infrastructure and more about tricking the AI’s “brain,” which is a far more abstract and challenging security surface to validate.
Integrating AI-assisted validation directly into development workflows, as seen with platforms like Mend, is becoming more common. What are the key steps for an organization to successfully embed this kind of continuous validation into their existing AppSec program without slowing down development velocity?
The absolute key is to make it seamless and actionable for developers. The first step is tight integration. The tool must live where the developers live—inside their IDE, their code repositories, and their CI/CD pipelines. It cannot be a separate, clunky portal they have to log into. Second, the findings must be correlated and prioritized. Developers are already overwhelmed with alerts. A platform that can connect a vulnerability in a third-party library to a specific misconfiguration in their own code and show how they combine to create a real, exploitable risk is invaluable. This turns abstract warnings into concrete work items. Third, the focus must be on efficient remediation. The tool should not only find problems but also suggest the fix, perhaps even generating a pull request automatically. By embedding this AI-assisted validation directly into the software lifecycle, security becomes a natural part of the development process, not a bottleneck at the end. This approach respects development velocity while ensuring that security is built in, not bolted on.
It is suggested that AI penetration testing often serves as a “connective tissue” between vulnerability scanning and manual deep dives. Can you elaborate on this role? Please provide a step-by-step example of how it helps teams prioritize which of the thousands of scanner findings actually matter.
That “connective tissue” analogy is perfect because it highlights the validation gap that these platforms fill. A vulnerability scanner is your first layer; it casts a wide net and tells you about every potential crack in your foundation, generating thousands of findings. A manual pen test is your last layer; it’s a deep, creative, and expensive exploration of a specific target. The AI platform sits right in the middle, connecting the two. Here’s a typical workflow: Step one, a vulnerability scanner runs and identifies, say, 5,000 potential issues, from critical CVEs to low-risk informational findings. Most of these are just theoretical risks. Step two, the AI penetration testing platform ingests these findings. It doesn’t just report them; it actively tries to exploit them. It looks at a “critical” CVE on an internal server and determines that, because of network segmentation, it’s completely unreachable and therefore not a real-world risk. Conversely, it might take three “low” rated misconfigurations, chain them together, and prove they create a path to escalate privileges. Step three, the platform produces a much smaller, high-confidence list of validated attack paths—maybe only a dozen issues out of the original 5,000—that represent genuine, demonstrable exposure. Now, instead of asking your manual pen testers to sift through thousands of alerts, you can point them directly at these validated, high-impact paths and say, “Dig deeper here.” The AI filters the noise, validates the threat, and makes sure human expertise is spent on what truly matters.
What is your forecast for the future of AI penetration testing?
My forecast is that it will become an absolutely non-negotiable component of any mature security program, as fundamental as a firewall or endpoint protection. We’ll see these platforms become even more autonomous and adaptive, capable of not just discovering but also predicting attack paths based on subtle environmental changes. The real transformation, however, will be in how it reshapes security teams. The days of security professionals spending 80% of their time on repetitive vulnerability validation and retesting are numbered. This new wave of AI-powered offense frees them to focus on higher-value work: architecting resilient systems, proactive threat hunting, and advising the business on strategic risk. Ultimately, by providing real-time, continuous assurance, AI penetration testing won’t just make us better at finding flaws; it will fundamentally improve business agility, reduce breach risk, and embed security into the very fabric of how modern organizations operate.
