According to TheRegister.com, the security landscape for artificial intelligence is undergoing a seismic shift as agentic AI systems—complex, multi-LLM setups that make autonomous decisions—become central to critical operations. This new era brings unique vulnerabilities like prompt injection and data poisoning, forcing organizations to adopt advanced red teaming practices. The stakes are incredibly high, with regulatory frameworks like the EU AI Act mandating clear documentation and traceability, with non-compliance penalties reaching up to €35 million or 7% of a company’s global revenue. In response, security practices must evolve from traditional black-box testing to a more transparent, gray-box approach that understands internal AI workflows. The article positions continuous, automated red teaming platforms as essential for future-proof AI assurance, moving beyond infrequent audits to embedded security throughout the AI lifecycle.
Why Old Security Methods Fail
Here’s the thing: traditional security testing is built for static systems. You probe the perimeter, you test the code, you look for known vulnerabilities. But modern AI, especially these new “agentic” systems, is a living, breathing, and incredibly opaque beast. It’s not just one model sitting in a box; it’s a whole team of specialized AI agents passing tasks and decisions between each other. Think of a financial system where one agent handles login, another checks the transaction, and a third looks for fraud. How do you even begin to test that with old tools? You can’t. A single compromised agent—maybe tricked by a clever prompt injection—can poison the entire workflow. The attack surface isn’t a wall anymore; it’s a web.
The Gray-Box Imperative
So what’s the answer? The article argues it’s a shift from black-box to “gray-box” testing. Black-box means you’re an external attacker with zero internal knowledge. That’s still useful, but it’s not enough for these complex systems. Gray-box means your red team has some visibility into the architecture—they can see how the agents are connected, what data they share, where the trust boundaries are. This is where transparency becomes non-negotiable. It’s the only way to map those critical dependencies and simulate the kind of cascading, multi-step attacks that are the real threat. Basically, you need to understand the playbook to find the flaws in the plays.
More Than Security, It’s Compliance
And this isn’t just a nice-to-have for elite tech firms anymore. It’s quickly becoming a legal requirement. Look at the EU AI Act, the NIST framework, OWASP’s lists—they all demand this level of transparency and documentation. You need an audit trail for your AI’s decisions. You need to prove you can detect and mitigate bias. If you can’t show how your AI works and how you’re stress-testing it, you’re not just vulnerable to hackers; you’re vulnerable to massive fines. The regulatory hammer is coming down, and it’s making advanced red teaming a core business function, not a niche security exercise.
The Platform Future of Testing
Now, manually red teaming these systems is a monumental, maybe impossible, task. The scale and complexity are too high. That’s why the piece points toward automated, platform-based solutions—like the one mentioned from Zscaler—that can provide continuous testing. The idea is to bake this scrutiny into the entire AI lifecycle, from development to deployment. It’s a recognition that security can’t be a periodic checkup. It has to be the constant immune system for an autonomous AI. The companies that figure this out now won’t just be more secure. They’ll be the only ones operating legally at scale. Everyone else is building a compliance time bomb.
