The AI security program built by offensive operators who attack AI systems for a living.

AI red teaming is adversarial security testing of machine learning systems, large language models, agentic workflows, and RAG pipelines. Unlike traditional penetration testing, AI red teaming targets model-layer vulnerabilities — prompt injection, jailbreaks, model abuse, training data extraction, agent tool-use compromise — that cannot be discovered with conventional application security tools. It requires offensive operators with both AppSec depth and hands-on ML adversarial experience.

Yes. The program is mapped to the EU AI Act's risk-management, transparency, accuracy, robustness, and cybersecurity requirements for high-risk AI systems (Articles 9, 13, 14, 15, and 17). Deliverables include the technical documentation and post-market monitoring evidence required for conformity assessment under Article 43 and ongoing obligations under Article 72.

We test agentic systems for tool-use compromise, prompt injection through tool output, MCP server authentication and authorization gaps, cross-tenant context leakage, agent identity confusion, multi-agent trust boundary failures, and excessive autonomy. Engagements include both static review of the agent architecture and dynamic adversarial testing of the running system. Where appropriate, we deploy custom tooling against your MCP servers to validate authentication, authorization, and isolation guarantees.

The AIBOM is a structured inventory of every foundation model, fine-tune, training and fine-tuning dataset, embedding model, vector store, third-party AI vendor, and integration point in your AI stack. Each component is mapped to its provenance, license, security posture, data residency, and regulatory implications — providing the supply chain visibility increasingly required by NIST AI RMF (Map function), EU AI Act (Article 11 technical documentation), and ISO 42001 (Annex A controls on AI system lifecycle).

Yes. All adversarial testing of production AI systems follows a controlled engagement protocol with rate limits, scoped service accounts, real-time monitoring of test traffic, and pre-agreed rollback procedures. For higher-risk testing — particularly model abuse, denial of service, and supply chain validation — we replicate the model and pipeline in an isolated environment provided by your team. Production-vs-isolated decisions are made jointly during the scoping phase and documented in the rules of engagement.

The standard program is 12 months with quarterly assessment cycles, continuous monitoring between cycles, and an executive review at the end of each quarter. Initial scoping and baseline assessment typically complete in the first 6–8 weeks. Shorter point-in-time AI penetration tests are available outside the program structure but are not the recommended engagement model — AI systems evolve too rapidly for point-in-time assurance to remain meaningful.

Every engagement is led by a named senior AI red team operator with both offensive security and applied ML backgrounds, supported by a delivery team across our US, India, and Kenya offices. You will know the names of the people testing your systems before the engagement starts, and they remain the same individuals across quarterly cycles to preserve continuity and context.

The AI Assurance Program is designed to integrate with, not replace, your existing AppSec function. Findings flow into the same triage workflow you already use (Jira, ServiceNow, GitHub Issues), via the Bluefire platform's integrations. Where your existing AppSec controls cover the AI surface adequately, we say so; where they don't, we identify specific tooling, process, and skill gaps to close.