4 Step AI Red Team Pilot for SMEs That Turns Findings Into Tests

AI red teaming is structured, adversarial testing of an entire AI system, model, prompts, retrieval pipelines and connected tools, designed to surface prompt injection, jailbreaks, data leakage and unauthorised actions before an attacker finds them first. Frameworks such as the OWASP LLM Top 10, MITRE ATLAS and the NIST AI RMF give the practice a shared vocabulary. Some firms use it to turn findings into evidence, not anecdotes. The next step for most teams is a scoped pilot, not a full-blown programme.

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TL;DR:

• AI red teaming must target entire systems, including prompts, retrieval pipelines, and tool integrations, not just the model in isolation.
• Testing should follow a manual discovery process first, with automated tools used to reinforce known vulnerabilities and scale coverage.
• Focus on impact-driven attack surfaces like indirect prompt injection and data exfiltration, which are responsible for around 10% success rates in lightly protected apps.
• A disciplined process includes scope definition, evidence logging, iterative passes, and converting findings into regression tests for continuous monitoring.
• Building a threat-model ontology and maintaining a living test matrix are crucial for effective governance and auditability over time.

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Table of Contents

• What is AI red teaming and how does it differ from benchmarking?
• What are the main attack surfaces in an AI system?
• What testing methods and tactics do red teams actually use?
• How do you plan and run a safe red-team campaign?
• What tools help automate AI red teaming, and where do they fall short?
• What can operators learn from large-scale AI red-team programmes?
• How do you build red-team findings into governance and audit trails?
• Who is behind this guidance, and what does a small pilot look like?
• What actually matters once the testing stops
• How Pattrndata supports a first AI red-team pilot
• Where to read more on AI red teaming
• Sources

What is AI red teaming and how does it differ from benchmarking?

AI red teaming is exploratory, limit-seeking testing carried out by a team blending human creativity with automated tooling, aimed at finding where a system breaks under adversarial pressure rather than confirming it meets a fixed score. That distinction matters more than most write-ups suggest. A benchmark tells you how a model performs on a known set of questions. A red team tries to find questions nobody thought to ask, and it does that manually first, before anything gets automated.

The practice draws its name and much of its discipline from traditional cybersecurity red teaming, but the target is different in a way that changes the whole approach. A conventional penetration test probes a network, an application, or an API for known vulnerability classes: unpatched software, misconfigured access controls, weak authentication. AI red teaming has to do all of that and then go further, because the “vulnerability” can live inside the model’s own reasoning, inside a retrieved document, or inside the way a tool call gets authorised. Researchers studying the practice describe it as having five defining characteristics: it is limit-seeking, exploratory, manual by default, team-based, and approached with what one industry write-up calls an “alchemist mindset”, meaning testers hunt for novel failure combinations rather than repeating a fixed script.

That system-wide framing is the part organisations most often miss. Testing the model in isolation, asking it directly for something harmful and checking whether it refuses, tells you almost nothing about how the same model behaves once it is wired into your CRM, your document store, or a Copilot Studio agent that can send emails on someone’s behalf. A model that politely declines a harmful request in a clean chat window can still be manipulated once it is fed a poisoned document through a retrieval pipeline it trusts by default.

Four things worth holding onto before scoping any test:

• The system is the target, not just the model. Prompts, retrieval-augmented generation (RAG) pipelines, tool integrations and the downstream code that acts on outputs are all in scope.
• Repeatability is a later goal, not a starting condition. Early passes should explore broadly; only proven failure patterns get turned into repeatable regression checks.
• Human judgement leads, automation scales. Manual discovery should come first, with automated tools reinforcing what testers already know to look for.
• A red team is not a compliance tick. Its value comes from adversarial creativity, not from running through a static checklist once a quarter.

None of this replaces conventional AI penetration testing or standard application security review. It sits alongside them, closing the gap that classic tooling was never built to cover.

What are the main attack surfaces in an AI system?

Start from the impact you are trying to prevent, then work backwards to the attack path that could cause it. Microsoft’s internal red-teaming programme, drawn from testing over 100 generative AI products, found this “impact-first” approach far more useful than starting with a list of exotic prompts and hoping something sticks. If the worst outcome for your business is a support agent approving a refund it should never have approved, that’s your starting point, not a generic jailbreak library.

Once the impact is clear, map it to the routes an attacker could realistically use to reach it. In most AI applications, that means working through a handful of recurring attack surfaces.

• Direct prompt injection, where an attacker types instructions straight into a chat interface to override the system prompt or safety guidance.
• Indirect prompt injection, where malicious instructions arrive hidden inside a document, webpage, or email that the model retrieves and treats as trusted content, arguably the highest-impact surface for any RAG-based assistant.
• Data exfiltration, where careful questioning or encoding tricks the model into revealing system prompts, other users’ data, or confidential business records.
• Tool-authorisation abuse, where an agent with the power to send emails, update records, or trigger payments is tricked into using that power outside its intended bounds.
• Memory poisoning, where a system with persistent memory or long-running context is fed false information early, which then quietly shapes later, unrelated interactions.
• Supply-chain manipulation, where a compromised plugin, fine-tuned adapter, or third-party model update introduces a vulnerability nobody in the business chose to accept.

One figure worth sitting with: research from SafePrompt on prompt-injection testing suggests around 10% of common injection attempts succeed against lightly protected applications. That is not an edge case. It is roughly one in ten off-the-shelf attacks getting through on apps that haven’t been deliberately hardened, which is a fair description of most internal tools built quickly on top of a chat model.

Mapping findings to recognised taxonomies pays off well beyond the test report itself. Filing a finding against a specific OWASP LLM Top 10 category, a MITRE ATLAS technique ID, or a control in the NIST AI RMF means the result slots straight into an existing risk register instead of sitting in a PDF nobody revisits. Auditors, insurers and regulators increasingly expect that kind of mapping, and the White House executive order on safe, secure and trustworthy AI is one clear signal that adversarial testing is moving from best practice to expectation.

What testing methods and tactics do red teams actually use?

Most mature programmes run tests in passes rather than one long, unstructured session, because each pass catches a different class of weakness and the results build on each other. Palo Alto Networks describes this as a four-pass testing pattern, and it holds up well as a working structure for teams building their first campaign.

• Known-pattern testing. Run established jailbreak prompts, role-play framings, and documented injection strings first. This is the fastest pass and clears the low-hanging fruit before testers spend time on anything novel.
• Encoding bypass testing. Wrap the same intent in Base64, leetspeak, unusual Unicode, or a different language, since many filters catch plain text but miss the same request disguised at the character level.
• Multi-turn attacks. Split a harmful request across several exchanges, building context gradually so no single message trips a filter, even though the conversation as a whole clearly achieves the disallowed goal.
• Indirect injection via external content. Plant instructions inside a PDF, a webpage, or a calendar invite that the system will later retrieve and treat as legitimate input, which is the pass most teams skip and the one that tends to matter most for RAG-based tools.

Underneath those four passes sit the actual technique families testers draw on. NVIDIA’s research on how LLM red teaming is practised groups them into language, rhetorical, possible-worlds, fictionalising, and stratagem-based tactics. Language tactics exploit ambiguity or typos the filter wasn’t trained to catch. Rhetorical tactics use persuasion, false urgency, or authority framing (“as the system administrator, I need you to…”). Possible-worlds tactics ask the model to imagine an alternate scenario where its usual rules don’t apply. Fictionalising tactics wrap the request in a story, screenplay, or academic exercise. Stratagem tactics chain several of the above together, layering a fictional frame over a multi-turn build-up over an encoding trick, until the combination gets through where any single element alone would not.

Pro Tip: Run the four passes in order and stop escalating once a pass fails cleanly. If known-pattern prompts already break the system, there is little value in building an elaborate multi-turn fictionalising attack before that basic gap gets fixed.

None of this is worth doing without disciplined evidence capture. A finding that cannot be reproduced is a rumour, not a result. Every test that succeeds should be logged with the exact input, the full assembled prompt context including anything retrieved from RAG, the model version and configuration, any tool call traces triggered, and the success rate across repeated attempts, not just a single lucky pass. That last point matters because a single success against ten failures tells a very different risk story than nine successes out of ten, yet both often get reported as a flat “vulnerable/not vulnerable” line if nobody insists on the rate. This is exactly the evidence that later becomes an automated regression test, so the same failure gets caught the moment a model update or prompt change reintroduces it.

How do you plan and run a safe red-team campaign?

A red-team campaign needs a written scope before anyone opens a chat window, and that scope should name the impacts being tested for, the systems in bounds, the systems explicitly out of bounds, and who signs off on findings before anything gets escalated. Skipping this step is the single most common reason a first attempt at AI red teaming produces a pile of interesting screenshots and no clear action.

Who needs to be in the room depends on the system, but most credible campaigns pull together a mix of roles rather than leaving testing to one person working alone.

• A security lead or AI red-team owner who scopes the campaign and signs off on severity ratings.
• A model or product owner who understands the intended behaviour well enough to know what “broken” actually looks like.
• One or two testers with genuine adversarial creativity, drawn from security, or from anyone in the business known for finding the edge cases nobody planned for.
• A compliance or risk contact who translates findings into the language a risk register or auditor expects.

Testing has to happen somewhere safe, and “safe” here means specific things, not a vague promise. Use an isolated copy of the model and its supporting infrastructure, never the live production environment where a successful data-exfiltration attempt could actually leak a real client’s records. Turn on full logging before the first prompt goes in, because evidence captured after the fact is far weaker than evidence captured live. Set rate limits so automated fuzzing doesn’t accidentally trigger cost overruns or downstream API bans. And agree in advance what counts as an automatic stop condition, such as a test that appears to be working against a real customer record rather than test data.

Once testing produces findings, the workflow from there follows a familiar security shape: triage each finding for severity based on business impact rather than technical novelty, assign an owner for remediation, fix the underlying gap (a system prompt change, a stricter tool-authorisation rule, a retrieval filter), and then write a regression test that reruns the exact successful attack against the patched system. If the regression test passes, the fix holds. If a future model update or prompt change makes it fail again, the team knows before a client does. Firms uncertain about what should happen when an AI tool misbehaves in production can work through Pattrndata’s guidance on handling AI tool failures as a starting template for that escalation path.

What tools help automate AI red teaming, and where do they fall short?

Automation earns its place once manual testing has already told you what to look for, not before; this sequence aligns with best practices for building reliable AI apps detailed in the Adrian blog. Microsoft’s own guidance, built from the programme behind PyRIT, its open-source Python Risk Identification Toolkit, is explicit on this point: automated tools accelerate testing at scale but cannot replace human judgement for novel or subjective harms, particularly anything touching responsible-AI concerns that don’t reduce neatly to a pass/fail rule. PyRIT is best understood as a framework for running known attack patterns and encoding variations against a target model quickly and repeatedly, freeing testers to spend their time on the genuinely creative work instead of retyping the same jailbreak forty times with minor variations.

Beyond PyRIT, the tooling landscape splits roughly into three categories worth knowing apart. Open-source scanners focus on automatically probing a model or endpoint against a library of known attack strings, useful for quick coverage but only as good as the library behind them. Interactive playgrounds, of the kind SafePrompt describes, let a tester run curated attacks manually or in batch against a live endpoint, which suits both exploratory sessions and repeatable CI checks. Runtime monitoring tools sit in production, watching for injection-style patterns in real traffic rather than testing pre-release, which catches drift that a one-off test campaign never will.

Integrating these into CI/CD is where automation genuinely pays for itself. Once a red team has confirmed a specific failure and written a regression test for it, that test can gate a pipeline: no model or prompt change ships if it reopens a previously closed vulnerability. Batch-testing endpoints let that same regression suite run against every candidate release automatically, turning a one-off finding into a permanent guardrail rather than a single line in an old report.

The three pitfalls that undo most automated programmes are consistent: a threat model that doesn’t match how the system is actually used, over-reliance on one tool’s built-in attack library as if it were exhaustive, and thin or non-existent coverage for RAG and agentic flows, which is precisely where the highest-impact indirect injection and tool-abuse failures tend to live.

Firms building agentic workflows, particularly on Microsoft Copilot Studio, should treat tool-calling permissions as a first-class test target rather than an afterthought. Pattrndata’s work on secure agent development exists precisely because that permission boundary is where automated coverage runs thinnest and manual review still earns its keep.

What can operators learn from large-scale AI red-team programmes?

Microsoft’s account of red teaming more than 100 generative AI products offers the clearest public record of what actually works at scale, and the eight lessons it published reward a careful read rather than a skim. The headline lesson is the one already threaded through this article: manual testing has to define the risk surface before anything gets automated, because you cannot build a meaningful regression suite for a failure mode you haven’t yet discovered by hand. A second lesson, easy to underrate, is that red-team findings should measure the effect of mitigations, not just confirm a vulnerability exists. Finding a jailbreak is the easy part; proving a fix actually closes it, at scale, across repeated attempts, is where the real engineering effort sits.

A few operator practices show up repeatedly across serious programmes, worth adopting even at small scale.

• A threat-model ontology, a shared, written taxonomy of harm categories and attack techniques so different testers describe the same failure the same way.
• A living test matrix, mapping attack surfaces to specific test cases, expected evidence, and pass/fail criteria, updated as new failure modes get discovered.
• Consistent severity scoring, so a data-exfiltration finding from one sprint and one from six months later get compared on the same scale.
• Long-term evidence retention, keeping inputs, outputs and tool traces long enough to support an audit or a regulatory query well after the original test window closes.

Two short, anonymised vignettes illustrate how this plays out in practice. In one case, a customer-service assistant built on a retrieval pipeline was found, during an indirect-injection pass, to follow instructions buried inside a scanned PDF a customer had uploaded, instructions that told it to escalate the ticket and quote a discount policy that didn’t exist. The fix wasn’t a blanket ban on PDF uploads; it was a stricter instruction-hierarchy rule that treats retrieved document content as data, never as commands, plus a regression test that resubmits the same poisoned PDF after every model update. In a second case, a multi-turn attack gradually walked an internal HR chatbot into revealing salary bands for named employees, something a single blunt question never managed. The remediation added a context-aware data-loss check that runs continuously across a conversation rather than per message, closing the specific gap multi-turn testing had exposed.

How do you build red-team findings into governance and audit trails?

A red-team finding that never reaches the risk register has done half a job. Convert each confirmed vulnerability into a formal risk entry with a severity rating, an owner, and a target remediation date, exactly as you would for any other security finding, then define the pass/fail criteria that turn it into an automated regression check inside your CI pipeline. That single step, moving from a one-off report to a standing test, is what separates a red-team exercise that gets forgotten from one that keeps paying off.

Re-testing needs a schedule, not a one-off promise. Version gating, blocking a model or prompt update from shipping until its regression suite passes clean, gives that schedule teeth rather than leaving it to goodwill. Auditors and regulators increasingly expect to see exactly this kind of evidence trail: the original finding, the fix, the regression test, and the date each version last passed it. Firms working through what an AI-assisted audit trail should actually contain can use Pattrndata’s guidance on building audit trails without spreadsheets as a practical checklist for that documentation layer.

Ownership matters more than most organisations initially assume. Someone specific has to own the red-team programme, whether that sits inside a dedicated security function, a responsible-AI team, or, in a smaller firm, whoever already owns operational risk. Diffuse ownership is how programmes quietly stop running after the first pilot.

Governance element	What it needs to contain	Who typically owns it
Risk register entry	Finding, severity, impact, owner, remediation date	Security lead or risk owner
Regression test	Exact input, expected pass criteria, CI gate	AI/ML engineering
Re-test schedule	Trigger events (model update, prompt change), cadence	Security or governance function
Audit evidence	Inputs, outputs, tool traces, version history	Compliance or responsible-AI lead

Who is behind this guidance, and what does a small pilot look like?

This article was written by Rohit for Pattrndata, drawing on Pattrn Data’s work helping professional services firms and operations-heavy SMEs bring AI into their workflows without losing control of judgement, data boundaries, or client trust. Pattrndata’s approach centres on the Pattrn Protocol, a framework for mapping how work actually happens before any tool gets introduced, which is the same discipline good red teaming demands: understand the real attack surface before you start firing test prompts at it.

A workable first pilot doesn’t need a security team of ten or a six-month budget. It needs four steps, run in order, on one workflow.

First, map a single workflow end to end, including every system it touches: the model, any retrieval source, any tool it can call. Second, define the trust boundaries explicitly, what the system is allowed to do unsupervised and what always needs a human sign-off. Third, run a focused set of red-team passes against that one workflow, starting with known patterns and working through indirect injection if retrieval is involved. Fourth, fix whatever breaks and write a regression test for it before calling the pilot done.

Firms unsure where their own AI risk actually sits, which tools staff have quietly adopted without sign-off, or how exposed a particular Copilot workflow is, tend to benefit from a structured AI risk and readiness assessment before committing to a full red-team programme. It is a smaller, cheaper way to find out whether the risk surface is worth the exercise at all.

What actually matters once the testing stops

The conventional advice on AI red teaming leans hard on tooling: buy a scanner, run PyRIT, tick the OWASP box. That advice isn’t wrong, but it answers the easy 80% of the problem and skips the hard 20% that actually determines whether testing changes anything. The hard part is governance discipline: does a finding get a severity rating, an owner, and a regression test, or does it sit in a slide deck until the next incident brings it back up?

Most firms overrate the sophistication of the attacks and underrate the sophistication of their own follow-through. A single successful indirect-injection attempt against an unpatched RAG pipeline matters more than a dozen exotic jailbreaks nobody will ever encounter in production, because business impact, not technical novelty, is what a risk register actually needs to price. Prioritise the boring discipline: map the workflow, define the trust boundary, run one focused pass, fix it, write the regression test. Do that four times before you buy a second tool.

— Rohit

How Pattrndata supports a first AI red-team pilot

Running a first AI red-team pass without outside support usually means guessing at scope, missing the indirect-injection surface entirely, and having no clear place to put the findings once they’re found. Pattrndata’s AI readiness assessment exists to close that gap: a fixed-scope review that maps where AI already touches your workflows, which tools staff have adopted without sign-off, and where a focused red-team pass would find the most business-critical risk first.

From there, the path depends on what the assessment finds. Some firms move straight into a secure AI implementation project to close gaps around tool authorisation and data boundaries. Others need an ongoing AI governance retainer to keep regression tests, risk registers and audit trails current as models and prompts change. If your firm is already running Copilot or an agentic workflow and isn’t sure what a red team would even find, book an AI clarity session through the AI readiness assessment page and start with the smallest controlled pilot, not the biggest possible programme.

Where to read more on AI red teaming

The sources behind this guide are worth reading directly if you’re building a programme rather than just scoping one pilot.

• Lessons From Red Teaming 100 Generative AI Products (Microsoft): the clearest operator-level account of scaling red teaming, including the threat-model ontology behind PyRIT.
• What Is AI Red Teaming? (Palo Alto Networks): a practical primer covering the four-pass testing pattern used throughout this article.
• Defining LLM Red Teaming (NVIDIA): the research behind the technique-family taxonomy, language, rhetorical, possible-worlds, fictionalising, stratagems.
• LLM Red Teaming: Test Plan, Examples & OWASP Map (Phixe): concrete test-matrix examples mapped to OWASP LLM Top 10 categories.
• How to Test Your AI App for Prompt Injection Vulnerabilities (SafePrompt): grounding for automated batch testing and CI gating.
• What Does AI Red-Teaming Actually Mean? (CSET Georgetown): balanced framing on the human-versus-automation question.

Sources

• Lessons From Red Teaming 100 Generative AI Products (Microsoft)
• Defining LLM red teaming (NVIDIA blog)
• LLM Red Teaming: Test Plan, Examples & OWASP Map (Phixe)
• How to test your AI app for prompt injection vulnerabilities (SafePrompt)

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