Beyond the pentest
AI red teaming: adversarial testing beyond the pentest
A penetration test tells you whether your AI system can be broken. Red teaming tells you what it can be made to do: the harms, misuse cases and behavioural failures that emerge when a motivated adversary works the system patiently. Structured scenarios, mapped to MITRE ATLAS and the NIST AI RMF.
The definition
What AI red teaming is
AI red teaming is the structured adversarial testing of an AI system's behaviour: specialists play motivated attackers and misusers, probing the model, its guardrails and its surrounding processes with realistic scenarios to surface harms, misuse routes and failure modes that conventional security testing does not look for, then documenting what happened and what to change.
The practice comes from military exercises via cyber security, but applied to AI it widens: the question is not only whether the system can be breached, but whether it can be bent, into deception, discrimination, dangerous advice or quiet data exposure, while every component works exactly as built.
Red teaming vs pentest
How it differs from a penetration test
The two answer different questions and neither substitutes for the other. If nothing has attacked your AI system yet, the AI penetration test is usually the right first engagement.
Scenario-driven, not checklist-driven
A pentest works through known weakness classes. Red teaming starts from questions: could this system be used to defraud a customer, leak a diagnosis, generate instructions it must never give? Each scenario is played out end to end.
Harms and misuse, not just vulnerabilities
The exercise targets outcomes that hurt people and the organisation, discrimination, dangerous content, manipulation, privacy harms, whether or not a technical flaw is involved. A system can be fully patched and still cause harm.
Model behaviour under adversarial pressure
Sustained, adaptive campaigns rather than single probes: persona building, multi-turn coercion, context flooding and escalation over long conversations, because real misuse is patient.
Sociotechnical scope
The system includes the humans around it. Scenarios cover how staff act on model output, where oversight breaks down and what the escalation path does under pressure, not just what the model returns.
The frameworks
Mapped to MITRE ATLAS and the NIST AI RMF
MITRE ATLAS is the adversarial playbook for AI: a curated matrix of tactics and techniques observed against real machine learning systems, from reconnaissance and model access through to exfiltration and impact. Our scenarios draw on it and every outcome cites the tactics involved, so your findings speak the vocabulary threat intelligence already uses.
The NIST AI Risk Management Framework supplies the governance half. Results are organised against its four functions, Govern, Map, Measure and Manage, which is precisely where red teaming earns its place: it is the strongest Measure activity available for behavioural risk, and its output feeds Manage directly. If your organisation aligns to the AI RMF or ISO 42001, the report files itself.
The engagement
What an AI red teaming engagement looks like
Four phases, scoped around your deployment rather than a standard test plan, priced per system from £5,600 by complexity, fixed once scope is agreed. Full details on the pricing page.
Scoping and harms hypothesis
We work with you to define the deployment context, the users, the worst plausible outcomes and the scenarios worth spending adversarial effort on. Scope is agreed in writing.
Adversarial campaign
Specialists run the scenarios against the system in a controlled setting, adapting as it responds, recording every exchange and outcome as evidence.
Analysis against frameworks
Outcomes are classified against MITRE ATLAS tactics and the NIST AI RMF functions, so results plug into risk registers and governance reporting rather than sitting in a silo.
Readout and mitigation planning
Findings are walked through with your technical and governance owners together, ending in an agreed mitigation plan with owners against each item.
Harms register
Every harm identified, with severity, likelihood evidence and the scenario that produced it, in a format that drops into your risk management process.
Scenario outcomes
What was attempted, what the system did, full transcripts as evidence, and a clear verdict per scenario: resisted, partially resisted or failed.
Mitigations and mapping
Specific, prioritised mitigations for each finding, cross-referenced to ATLAS tactics and AI RMF functions so remediation and reporting speak the same language.
Agentic systems raise the stakes of every scenario, because a bent agent does not just answer badly, it acts. If agents are in scope, read the agentic AI security page alongside this one.
Who commissions it
Who needs AI red teaming
Foundation-model deployers
If a general-purpose model sits inside your product, its misuse becomes your incident. Red teaming establishes what your guardrails withstand before your users establish it for you.
Regulated sectors
Financial services, health and legal deployments face regulators who expect evidence of adversarial evaluation, and the EU AI Act pushes the same direction for higher-risk systems.
Board assurance
A structured, framework-mapped exercise gives the board a defensible answer to the question they are starting to ask: how do we know our AI cannot be turned against us or our customers?
Quick answers
AI red teaming questions, answered
What is AI red teaming?
AI red teaming is the structured adversarial testing of an AI system's behaviour: specialists play motivated attackers and misusers, probing the model, its guardrails and its surrounding processes with realistic scenarios to surface harms, misuse routes and failure modes that conventional security testing does not look for. The phrase 'red teaming in AI' means the same exercise.
Where does red teaming end and a penetration test begin?
A penetration test hunts technical vulnerabilities in a defined system; red teaming asks what a motivated adversary could make the system do, including harmful outcomes that involve no vulnerability at all. Different questions, different methods, complementary results. Most AI estates warrant the pentest first.
How often should AI systems be red teamed?
On material change, not on a calendar: a new model version, new tools or data sources, a new user population or a shift in deployment context each reset the risk picture. Between exercises, monitoring should watch for the failure modes the last exercise found.
Scenario zero
Scope an AI red teaming exercise
A free scoping call defines the deployment, the plausible harms and the scenarios worth adversarial effort, then confirms the engagement price before anything is signed.