AI Red Teaming Services

Your AI Went to Production Faster Than Your Controls Did

Most organizations shipped an AI feature in months. The security review, the data classification exercise, and the access model that should have preceded it are still in progress. Adversarial testing is how you find out what that gap costs before someone else does.

The pattern repeats across nearly every environment we assess. A team stands up an internal assistant so employees can ask questions of company documents. To make it useful, they point it at a shared drive. Nobody audits that drive first, because for twenty years it was protected by the fact that no human had time to read all of it. The assistant does have time. Within a week, an employee in one department can ask a plain-English question and receive salary data, an unredacted contract, or a spreadsheet of credentials that was technically readable all along. No exploit was involved. The system worked exactly as designed, and the design was the problem.

Customer-facing systems fail differently. A support chatbot is given a system prompt describing its rules, a knowledge base, and often an integration that can look up an order or open a ticket. An attacker types a message crafted to make the model treat the attacker's text as a higher priority than its original instructions, and the guardrails written in English come apart, because they were only ever a suggestion to a statistical model. The interesting question is never whether the model can be talked out of its persona. It is what happens next: whether the integration behind it will honor a lookup for an account the user does not own, whether the model will disclose the contents of another customer's ticket, whether the refund tool has a spending limit.

Retrieval and agent architectures raise the stakes again. When a model reads a web page, an inbound email, a support ticket, or a vendor PDF, that content enters the same context window as your instructions. An attacker who can place text where the model will read it can issue commands to your system without ever authenticating to it. This is indirect prompt injection, and it is the reason an AI agent with permission to send email, write to a database, or execute code is a security boundary rather than a productivity feature. The agent will follow the most persuasive instruction in its context, and that instruction may have been planted by a stranger.

Petronella Technology Group approaches this work from both sides. We build and host AI systems for regulated clients, including private AI deployments and retrieval-augmented generation pipelines, and we run offensive security engagements against networks and applications. That combination matters, because an AI red team report is only useful if the people writing it understand how the pipeline was assembled and what it would take to change it. Findings that end at the word "jailbreak" are entertainment. Findings that name the retrieval filter, the tool permission, and the log you were missing are a remediation plan.

Where the AI feature is embedded in a larger web platform, we recommend pairing this engagement with a web application penetration test, which covers authentication, session handling, and business logic in a depth an AI-focused engagement does not attempt. Where the concern is the network the models run on, an internal penetration test is the better companion.

These are complements, not competitors. A healthy program runs scanners continuously, red teams periodically, and governs the whole thing under a documented framework. If you are building that framework now, start with our AI governance framework guidance and the ISO 42001 certification path, then use red team results as the evidence that your controls do something.

The fourth position deserves emphasis, because it is the one organizations consistently underestimate. An adversary who can submit a support ticket, publish a page your crawler indexes, or send an email your assistant summarizes has an instruction channel into your AI system without any account, any credential, and any alert. If your architecture assumes that only authenticated users can influence the model, that assumption is worth testing before it is tested for you.

Step five is where most AI security reports stop short, and it is the step that determines whether anyone acts on the results. Showing that a model can be persuaded to write something off-policy is a curiosity. Showing that the same technique causes the assistant to return a different customer's invoice, or to call a refund API without an approval, converts a research finding into a business risk with an owner, a deadline, and a budget. We write for the engineer who has to fix it and the executive who has to fund it.

Where AI Red Teaming Shows Up in Your Obligations

Adversarial testing of AI systems has moved from a research practice to an expectation written into frameworks, contracts, and customer questionnaires.

ISO/IEC 42001, the management system standard for artificial intelligence, expects organizations to identify AI-specific risks and demonstrate that controls are operating. Adversarial testing is the most direct evidence available that a control does more than exist in a policy document. The NIST AI Risk Management Framework treats measurement as a core function, and red team results populate the measure and manage activities with something concrete. Organizations pursuing ISO 42001 certification generally find that testing evidence is the fastest way to close the gap between a written framework and an operating one.

Sector rules apply on top of that. A healthcare organization deploying an AI assistant that touches protected health information inherits the full weight of the HIPAA Security Rule, including risk analysis and access control obligations that do not soften because the interface is conversational. Our HIPAA compliance team scopes AI engagements with those requirements in view. Defense contractors handling controlled unclassified information face the same reality under NIST SP 800-171 and CMMC: an AI tool that can retrieve controlled information is in scope, and a model that will disclose it to the wrong user is a control failure regardless of how the disclosure was phrased.

Commercial pressure is arriving faster than regulation. SOC 2 auditors now ask how AI features are governed, enterprise procurement teams send AI-specific security questionnaires, and cyber insurance applications have begun asking whether AI systems have been independently tested. A dated red team report answers all three at once. If you need ongoing ownership of that program rather than a point-in-time test, our vCISO services provide the security leadership to run it.

What Drives the Cost of an AI Red Team Engagement

Pricing depends on what we are testing and how deep the system goes. These are the factors that move an estimate, and the ones we confirm during scoping.

The largest driver is the number of distinct AI surfaces in scope. A single customer-facing chatbot with a fixed knowledge base and no write access is a contained engagement. An internal copilot connected to four business systems, a customer assistant, and an autonomous agent with permission to act are three separate systems that happen to share a model provider, and each needs its own threat model. The second driver is tool and integration depth: every function the model can call expands the attack surface disproportionately, because the interesting findings live in the gap between what a tool permits and what the business intended.

Data sensitivity affects effort as well. Testing a system that touches protected health information, controlled unclassified information, or financial records requires handling controls, evidence discipline, and reporting rigor that a marketing chatbot does not. Retrieval architecture matters too: a pipeline drawing from a curated, permission-aware index is faster to assess than one pointed at a decade of unclassified shared drives, where the primary work becomes determining what the index should never have contained.

Finally, engagement style changes the number. A focused pre-launch assessment on one application is the most common starting point. A purple team exercise run alongside your defenders costs more and returns detection improvements a covert test cannot. Adding retesting after remediation, or moving to a recurring cadence tied to your release schedule, changes the shape of the investment rather than simply its size. We scope every engagement against your actual architecture and provide a written estimate before any work begins, because a number quoted without seeing the system is a guess wearing a suit.

Offensive Security Experience Applied to AI

AI red teaming is a young discipline built on an old one. The organizations doing it well are the ones that already knew how to think like an attacker.

Petronella Technology Group has been securing regulated businesses from Raleigh since 2002 and has held a BBB A+ rating since 2003. We are a CyberAB Registered Provider Organization (RPO #1449) with CMMC Registered Practitioners on staff, and our founder is an NC Licensed Digital Forensics Examiner (License #604180-DFE) and cybersecurity expert witness. That forensic work matters more than it might appear: investigating real intrusions teaches you which theoretical weaknesses adversaries actually convert into incidents, and that judgment is what separates a prioritized report from an undifferentiated list.

Craig Petronella has published 15 books on cybersecurity and business technology. As he details in How Hackers Can Crush Your Business, attackers rarely need a novel exploit when an ordinary configuration decision will do, and AI systems have created an enormous new supply of ordinary configuration decisions. His book Beautifully Inefficient examines where AI genuinely improves on human judgment and where it merely accelerates a bad one, which is the same question a red team asks about a tool permission. He holds MIT Sloan Executive Education certification in Cybersecurity for Managers alongside MIT-certified AI and blockchain credentials, and has appeared as a cybersecurity commentator on NBC, ABC, CBS, FOX, and WRAL. Read more about his background on his team profile or browse the full book library.

We also build what we test. Our team deploys secure AI infrastructure, runs model fine-tuning for regulated clients, and delivers the broader AI services that put these systems into production in the first place. When a red team finding requires re-architecting a retrieval pipeline or rewriting a tool permission model, we can explain the fix in terms of the system you have rather than the one a template assumed.

The Triangle concentration of healthcare systems, biotechnology firms, law practices, universities, and defense suppliers means our engineers spend their days inside exactly the environments where AI adoption collides with strict data obligations. Clients elsewhere in the country receive the same engagement, delivered remotely, with the same reporting and the same option to move findings into a managed cybersecurity services program that tracks remediation rather than filing it.

Last Updated: July 27, 2026