Red-teaming the AI your org is already deploying: LLM guardrail testing, prompt-injection hardening, data-leak assessment, and governance that won't block the people shipping features.
Nearly every mid-market org we talk to has at least one internal-facing LLM running against production data: an HR assistant, a support copilot, a RAG pipeline over customer tickets, a code assistant with repo access. None of them went through the security review your normal apps do. The prompt IS the API — and nobody is pen-testing the prompt. LLM red-teaming isn't jailbreak theater; it's systematic prompt-injection testing against your actual tool definitions, data sources, and guardrails.
Not a policy document. A working program with testing, governance, and training that makes AI adoption safer without killing the velocity you signed up for.
// AI adoption coverage — every scope
// the policy is the output, not the starting point.
Inventory what's running, red-team what's risky, build governance around what's working. No paperwork for its own sake.
We catalog what AI you're actually running — sanctioned and shadow — and rank systems by risk: data sensitivity, user-facing exposure, tool authority, regulatory scope. Output is a prioritized target list, not a spreadsheet that sits in a drive.
Systematic prompt injection, tool abuse, data exfiltration, and prompt chaining tests against each prioritized system. Findings with verified reproduction, severity rationale, and remediation guidance — mapped to the EU AI Act and ISO/IEC 42001 where applicable. Real-time critical-finding escalation.
Usage policy and acceptable-use framework that reflects what your teams actually do, not what a template says. Escalation paths for new AI systems. Quarterly re-review cadence. Team training tailored to your AI surface. Optional: SOC coverage for the AI runtime.
Free initial scoping — 30 minutes to inventory what AI is actually running in your org and where the highest-risk surfaces are.