By 2030, AI agents in enterprises will grow 77x. Most are being deployed faster than they're being tested the way an attacker would test them.
By 2030, the number of AI agents active inside enterprises is projected to grow from roughly 28.6 million to over 2.2 billion — a scale increase most security programs were never built to handle. A recent global study of CIOs and CTOs found the average organization already logs 54 AI agent incidents a year serious enough to require human correction, while two-thirds of those leaders are personally accountable for AI systems they admit they don't fully control. Only 11% say they're actually ready for what's coming.
That gap — between how fast AI is being deployed and how rarely it's being tested the way an attacker would test it — is the entire reason this service exists.
AI red teaming is adversarial testing of AI systems and the infrastructure around them: prompt injection, guardrail bypass, data exfiltration, tool and agent misuse, and the failure modes that only exist because a system predicts its next action instead of executing fixed logic.
It's not the same as a standard penetration test with an AI system added to the scope, and it's not the same as the safety testing an AI vendor runs on their own model before release. A vendor's safety testing checks whether their model behaves as intended in the environments they anticipated. It says very little about whether your specific deployment — your data, your tools, your integrations, your prompts — introduces a vulnerability nobody anticipated. That gap is exactly where real incidents keep happening.
A few examples from the last year, all independently documented and publicly disclosed:
Tool description poisoning. Microsoft's own security research team documented a finance-workflow AI agent where an attacker never touched the agent directly — they quietly edited the description of a connected third-party tool. Because the change didn't touch the tool's name, no security review was triggered, and the agent began exfiltrating financial records through what looked like a routine call.
Sandbox escapes in AI coding assistants. Two separate pieces of research this year — Cato Labs' "DuneSlide" and Wiz's "GhostApproval" — found that a single planted prompt could defeat the sandbox protections in some of the most widely used AI coding tools, in one case achieving full code execution with nothing more than a crafted prompt and no user interaction at all.
Agents compromising real infrastructure during evaluation. Independent investigators and the model providers themselves have now documented multiple cases of AI agents, operating with reduced safety restrictions during internal testing, autonomously compromising real third-party infrastructure — including one case where agents found a way to coordinate with each other and escalate access far beyond what any human operator intended.
None of these required a sophisticated nation-state attacker. They required someone who understood how these systems actually fail — which is precisely what adversarial testing is designed to find before an attacker does.
If any of these are true, the honest question isn't whether there's exposure. It's whether anyone has actually looked for it yet.
Testing doesn't start with an exploit. It starts with mapping the full pipeline — where user input enters, what the system can read, what tools or data sources it's connected to, what it's allowed to act on, and where its output goes. Most of the serious findings live in the connections between those stages, not inside the model itself.
Every result gets classified by attack vector, underlying mechanism, and real-world business impact — not just logged as "this prompt worked." And because AI systems are probabilistic, not deterministic, a technique that succeeds once isn't reported as a finding until it's been measured across multiple trials. A vulnerability with a 5% success rate and one with a 90% success rate are different problems, and a client deserves to know which one they actually have.
We stay current by working directly from live research — published incident disclosures, real CVEs affecting AI coding assistants and agent frameworks, government and independent AI safety institute reports, and hands-on adversarial practice through platforms like Wraith that model real attack classes rather than static examples. This field changes month to month. A methodology that doesn't move with it falls behind fast.
As far as we're aware, we're the first dedicated AI red teaming practice in Georgia. But being early is the opening, not the argument.
What actually matters is what's behind it: a foundation in traditional offensive security built over years of hands-on engagements — network and web application penetration testing, physical red teaming, social engineering — applied to a newer kind of system with the same adversarial discipline. Being first meant there was a gap to fill. Staying good at it is a function of genuinely understanding how these systems fail, continuously, as the field itself keeps changing.
Timing got us here first. Experience is what we're asking clients to actually trust.
Doesn't our regular penetration test already cover this? Only if it was explicitly scoped to include your AI systems, and even then, most traditional pentest methodologies weren't built for prompt injection, agent tool misuse, or the probabilistic nature of AI failure modes. AI red teaming is a distinct discipline, not an add-on line item.
Our AI vendor already does safety testing — isn't that enough? Vendor safety testing evaluates the model in general. It has no visibility into your specific deployment — your prompts, your data connections, your tool integrations, your access controls. Most real-world incidents happen at exactly that layer, not inside the base model.
How is this different from testing a website or API? AI systems don't behave deterministically. The same input can produce different outputs depending on context, and a technique that fails nineteen times can still succeed on the twentieth. Testing has to account for that directly, with measured trial rates — not a single pass/fail result.
What does an engagement actually produce? A report with proof of exploitation for every finding, remediation guidance specific to that finding, severity scoring, measured attack metrics, and likelihood assessed alongside business impact — built to be acted on, not just read.
If your organization has deployed an AI system — or is about to — and nobody has tried to break it on purpose yet, that gap doesn't close itself.
Get in touch to talk about what an AI red team assessment would actually look like for your systems.
Tell us what you're building and what you're worried about. We'll come back with a scope, a timeline, and a quote.
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