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AI Red Teaming for Enterprise LLM Deployments

Enterprise LLM applications create attack surfaces that traditional penetration testing never touches: prompt injection, jailbreaks, data exfiltration through model outputs, and agents tricked into misusing their tool access. Trussed AI red teaming stress-tests your LLM apps, copilots, and agents the way adversaries will, then closes the gaps with runtime controls, so findings become enforced policy instead of a PDF of recommendations.

What is AI red teaming?

AI red teaming is the adversarial testing of AI systems, simulating prompt injection, jailbreaks, unsafe outputs, data leakage, and agent abuse, to find vulnerabilities before attackers, auditors, or users do. Unlike traditional pentesting, AI red teaming targets model behavior and the policies governing it, not just infrastructure.

What does AI red teaming uncover?

Prompt injection paths, direct and indirect injection through documents, web content, and tool outputs
Jailbreaks and policy bypasses, adversarial prompts that defeat system instructions and guardrails
Data leakage, PII, secrets, and confidential context exposed through model outputs
Agent abuse, tool calls, API requests, and workflow triggers manipulated beyond intended boundaries
Governance blind spots, interactions your monitoring and audit trail never capture

How does the Trussed AI red team process work?

Scope models, agents, and risks, define systems under test, threat scenarios, sensitive data paths, and policy requirements.
Run adversarial attack simulations, execute injection, jailbreak, leakage, and misuse scenarios against live applications and agents.
Validate runtime guardrails, test whether existing controls actually block attacks at execution time.
Document findings and evidence, deliver reproducible findings with severity, business impact, and audit-ready records.
Implement controls and retest, convert findings into enforced runtime policies in the Trussed control plane, then verify the fix.

Why pair red teaming with a runtime control plane?

Most red team engagements end with a report; remediation is left to engineering backlogs. Because Trussed operates the control plane your AI traffic flows through, findings become enforceable policy immediately: injection patterns blocked in-line, agent permissions tightened at the execution layer, leakage prevented before outputs leave the system, with audit evidence proving each control works.

Frequently Asked Questions

How is AI red teaming different from a penetration test? Pentests target infrastructure and code. AI red teaming targets model and agent behavior, the prompts, outputs, and autonomous actions where LLM risk lives, and tests the policies meant to constrain them.

How often should we red team our AI systems? At every major release and on a recurring cadence. Model updates, new tools, and new data sources all change the attack surface; continuous runtime monitoring covers the gaps between engagements.

Do findings require code changes to fix? Usually not. Most findings map to runtime policies, input filtering, output controls, agent authorization, enforced by the control plane without touching application code.

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