Privacy engineering
8 guides tagged Privacy engineering, grouped by topic, with the SDLC phase and role each is written for.
Application security
1 guideIdentity & cryptography
3 guidesEnvelope encryption and key rotation: data keys, KMS keys and backups
Encrypt each record with its own data key, wrap that key in a KMS or HSM key, and keep wrapping-key rotation, data re-encryption, key deletion and restored backups as separate, tested operations.
Design, Maintenance · Developers, Platform engineers and SREs
Data minimization and retention: collect less and prove that deletion works
Design collection and deletion around a data-flow graph that includes logs, derived tables, exports and restored backups, and use per-person keys so deletion can reach copies you cannot edit.
Planning, Design, Security program · Developers, AppSec engineers
Tokenization, pseudonymization and privacy transformations: choose deliberately
Choose between vault tokens, keyed pseudonyms, format-preserving encryption, aggregation and differential privacy by the joins, reversibility and leakage each allows, and test re-identification on the data you release.
Planning, Design, Security program · Developers
Governance & frameworks
2 guidesGDPR, CCPA, HIPAA or the FTC rule: scope privacy and health-data obligations before choosing controls
Map data, actors and processing purposes to the relevant GDPR, California or HIPAA requirements and preserve uncertainty about applicability.
Planning, Design, Security program · Security leaders
GDPR privacy by design for engineers: Articles 25, 32 and 35 in code
Turn GDPR data protection by design and by default, security of processing and DPIA triggers into engineering work: a personal-data inventory checked in CI, defaults that collect and share less, a DPIA screen at design time, and access and erasure requests that reach every store.
Planning, Design, Security program · Platform engineers and SREs, Security leaders
AI security
2 guidesAssess extraction, membership and inference risks
Define what an adversary can observe and measure privacy leakage without confusing a model’s output with proof about its training data.
Design, Development, Release · AppSec engineers
Call a hosted LLM API safely
A code-first checklist for wiring up an OpenAI or Anthropic API call from a backend: keep keys server-side, redact PII before prompts and logs, choose provider retention settings deliberately, validate structured output and still treat it as untrusted, and set timeouts, retries and per-user cost caps.
Design, Development · Developers, AppSec engineers