[frameworks]
Privacy-by-design guidance where engineers can act on it.
Autometric combines GDPR-relevant review context with a serious AI code review engine for bugs, insecure data handling, auth flaws, and performance shortcuts that can create downstream privacy risk.
[framework evidence]
Review depth stays on. Framework evidence layers in.
SOC 2
CC6, CC7, CC8 review pack active
PCI DSS 4.0
Requirement 6 mapped to repository rules
FedRAMP
AU, CM, SI controls attached to findings
Why teams scope it
Privacy-sensitive services need stronger review context around data flows, retention, and exposure risk.
What gets attached
Developers see both the technical issue and the privacy implication without switching tools.
What stays on
The broader review engine continues to catch logic defects and security issues outside pure privacy concerns.
[how enforcement shows up]
GDPR changes the review context, not the reviewer.
- Surface privacy-relevant code patterns closer to the merge event.
- Keep scope explicit for services handling sensitive user data.
- Produce evidence that can inform DPIA or broader privacy reviews.
[best-in-class review]
Core AI code review still stays on.
GDPR context augments rather than narrows the review. Autometric still looks for defects, unsafe assumptions, and reliability issues that shape whether privacy safeguards hold in production.
When a pull request links a bug or feature ticket, that task context can stay attached to the same review and evidence path.
[evidence]
Evidence teams can actually reuse.
- Review artifacts with scope and context preserved.
- Evidence export that can support privacy and security workflows.
- Shared review behavior across modern Git and enterprise SCM systems.
[rollout]
Typical rollout pattern.
- step 1
Scope privacy-sensitive repositories first.
- step 2
Align the review pack with privacy and security owners.
- step 3
Expand once developers trust both the review quality and the policy context.
[cta]