Inside the report
8 chapters of research
A scannable chapter guide of what the full write-up covers.
01
Threat-modeling AI systems
Prompt injection, data exfiltration, model poisoning, and supply-chain risks mapped to concrete controls.
02
Data governance for training and inference
Retention, redaction, and purpose limitation requirements under GDPR and HIPAA, with template DPIA.
03
SOC 2 controls for AI systems
The 12 auditor-standard controls, with acceptance criteria, evidence, and example policy language.
04
HIPAA specifics for LLM-powered tools
BAA coverage, PHI handling, and de-identification pipelines that survive audit scrutiny.
05
GDPR and automated decisioning
Article 22 obligations, human-in-the-loop design, and right-to-explanation implementations.
06
Audit trail and observability requirements
Per-inference logging, prompt/response retention policies, and PII-safe telemetry patterns.
07
Third-party and model vendor review
How to assess OpenAI, Anthropic, AWS Bedrock, and Azure OpenAI under a formal vendor-risk program.
08
AI incident response playbook
From first prompt-injection report to root-cause remediation and disclosure timelines.