Advanced AI Safety

🟢 Smarter AI 🟢

We Implement...

AI Advanced Safety Set 1

✅ Inventory data flows → where PII/PHI enters, moves & exits our system ✅ Data minimization → to remove unnecessary fields before ingestion ✅ Anonymization/de-id → k-anonymity, l-diversity, differential privacy ✅ Encrypt everywhere → at rest (AES-256) & in transit (TLS 1.3+) ✅ Access control → RBAC/ABAC with least privilege ✅ Audit logging → immutable logs for access and data use ✅ Data retention policy → retention limits and auto-delete expired records

AI Advanced Safety Set 2

✅ Dataset audits → demographic balance regularly ✅ Bias mitigation → reweighting, augmentation, adversarial debiasing ✅ Evaluation across groups → fairness benchmarks and real-user testing ✅ Transparency artifacts → publish model cards & dataset sheets

AI Advanced Safety Set 3

✅ Prompt injection defenses → sanitize/validate user input ✅ Model robustness → test against adversarial attacks and data poisoning ✅ Rate limits & monitoring → detect model extraction attempts ✅ Content filtering → red-team outputs for disallowed or harmful content

AI Advanced Safety Set 4

✅ Framework alignment → map to NIST AI RMF, EU AI Act, ISO/IEC 42001 ✅ Continuous monitoring → automate drift detection and error logging ✅ Incident response → close data leaks, bias incidents or unsafe outputs ✅ Regular external audits → do independent red-teaming and compliance checks ✅ User transparency → clear consent forms, data-use disclosures, and opt-outs

Zero Trust ArchitectureBias ProtectionsGuardrails+HIPAA | SOC2 | PCICompliant LLM GatewayCompliant SubprocessorsAI Knowledge+Privacy+