Technology & AI Security Auditing
AI systems add attack surface most audits never look at: prompt injection, data exfiltration through model outputs, poisoned retrieval sources. We audit both the classic stack and the new one — and hand you a prioritized fix list, not a scare report.
What it includes
Static and dynamic analysis with SonarQube, Snyk, and LLM-based review across the full codebase — findings triaged by exploitability, not raw CVE count.
Red-teaming your AI features: prompt injection, jailbreaks, data leakage through outputs, and retrieval poisoning — with a reusable adversarial test suite.
IAM, network boundaries, secrets handling, and data-flow review across AWS/GCP/Azure — mapped against how a real attacker chains weaknesses.
Gap assessments for SOC 2, HIPAA, and GDPR with evidence mapping — what you already satisfy, what needs work, and in what order.
What you get
What lands in your hands at the end, not activity reports.
- Findings report ranked by exploitability and business impact
- Reusable adversarial test suite for your AI endpoints
- Remediation roadmap sequenced for your team's capacity
- Re-test verification after fixes land
Where it is used
- 01Healthcare: HIPAA-conscious review before an AI feature touches patient data
- 02Fintech: pre-launch audit of an LLM feature that reads customer documents
- 03Any team inheriting a codebase and needing an honest map of what's under the floor
Tools and platforms we work with
Solutions that use it
The same service, framed by the problem it solves.