AI red-team, mapped to the frameworks regulators read.
Every engagement is scoped against a specific framework so that the findings are usable by the customer's compliance, security, and audit functions.
- NIST AI Risk Management Framework. Control mappings and gap analysis on production AI systems. Coverage matrices produced as engineering artifacts.
- ISO 42001. AI management system implementation against the control set, with audit-ready evidence.
- OWASP LLM Top 10. Adversarial testing of prompt injection, data poisoning, supply chain vulnerabilities, model denial of service, sensitive information disclosure, and the rest of the catalog.
- MITRE ATLAS. Adversarial techniques against AI systems mapped to the ATLAS taxonomy.
- EU AI Act. Risk classification, conformity assessment, and obligations for high-risk and general-purpose AI systems.
The deliverable is not a report. The deliverable is the set of fixes deployed into the customer's codebase and the regression test suite that proves the fixes hold.
What we deploy with autonomous agents.
Prompt-injection sanitization. External input passes through detection of instruction-override patterns, system-prompt-leak attempts, role-switch attempts, and delimiter-based attacks before it reaches the model. Stripped content is logged as a potential adversarial event.
Instruction guards at the top of every prompt. Explicit constraints stated before the data, not buried after it. Length caps on external data to prevent prompt expansion. Type validation of model outputs before they are acted on.
Audit-grade event logging for adversarial activity. Every detection event is logged immutably with full context. The log is queryable by the customer's security operations center and by incident response.
AI model security scanner integration. Automated detection of model lineage issues, dependency vulnerabilities, and AI-specific attack paths integrated as a required gate in the customer's machine learning pipeline. Reference coverage exceeding ninety-five percent of production models in one deployment.
Where the practice was forged.
The firm's principal led security operations in classified federal environments, including the Nuclear Regulatory Commission and the Department of Energy. The work spanned nation-state threat defense, spearphishing campaigns, intrusions using professional command-and-control frameworks, and digital forensics in air-gapped facilities. Specific tradecraft, indicators, and attribution remain classified.
Federal experience translates to commercial AI security work in a specific way. The pattern of operating against a determined, well-resourced adversary in an environment with no margin for error is the same pattern that an autonomous agent in a regulated production environment must withstand.
Who engages the practice.
Software companies and AI-native firms preparing model launches, enterprise SKUs, or federal market access. Sovereign AI operators commissioning external red-team and evaluation work on deployed foundation models. Regulated industries with AI deployments under sector supervision, including banking, healthcare, and defense. Hyperscaler tenants subcontracting specialist work where local nationals or cleared personnel are required.
The honest constraint.
We do not run generic vulnerability assessment programs. We do not staff security operations centers as a managed service. We do not write security policy as a deliverable. The practice is technical, narrow, and tied to AI systems specifically. Adjacent work outside that scope is referred.