Know Your AI Attack Surface.

AI security intelligence focuses on threats specific to AI systems - prompt injection attacks on LLMs, adversarial inputs that fool ML models, model inversion attacks that extract training data, and AI supply chain risks from third-party models and datasets. Traditional cybersecurity tools and methodologies were not designed for AI-specific attack surfaces. Our practice combines offensive security research with deep AI/ML expertise to address threats that conventional security teams are not equipped to handle.

Our AI Attack Surface Assessment maps every AI component in your environment - LLM APIs, fine-tuned models, training pipelines, inference infrastructure, agent tool connections, and third-party AI integrations. We identify exposure points, enumerate attack vectors aligned to OWASP LLM Top 10 and MITRE ATLAS, and deliver a prioritized risk register with severity ratings and remediation guidance. Most assessments take 5–10 business days depending on the complexity of your AI stack.

Conventional penetration testing targets web applications, networks, and infrastructure using established tools like Burp Suite and Metasploit. AI red teaming targets the unique properties of AI systems - their non-deterministic behavior, sensitivity to adversarial inputs, susceptibility to prompt injection, and vulnerability to training data extraction. We use specialized AI attack tools including Garak, PyRIT, and custom adversarial testing frameworks. Our researchers understand the underlying ML architecture, not just the API surface.

Our AI risk frameworks and assessments map directly to NIST AI RMF (Govern, Map, Measure, Manage), EU AI Act (risk classification, conformity assessment, ongoing monitoring), ISO 42001 (AI management systems), and SOC 2 (security, availability, confidentiality). For regulated industries, we also align to HIPAA AI guidance, FDA Software as a Medical Device (SaMD) requirements, and FFIEC guidance for financial AI systems. Every deliverable includes a compliance mapping section.

AI supply chain security addresses risks introduced by the components you don't build yourself - pre-trained foundation models from Hugging Face or model providers, third-party ML libraries and packages, external training datasets, and AI APIs from vendors like OpenAI, Anthropic, or Cohere. Compromised or manipulated models, backdoored ML packages, and poisoned training data are emerging attack vectors that most security teams are not equipped to assess. Our AI Supply Chain Security Audit evaluates these risks systematically.