Data protection, tenant isolation, encryption, compliance, and access control for enterprise AI. 36 articles, written by the engineers building AI App Lab.
AI vendor risk assessment framework: five risk domains, 20-question security questionnaire, AIBOM requirements, fourth-party sub-processor disclosure, and contractual protections for AI providers.
Fernet encryption for AI platforms: AES-128-CBC + HMAC-SHA256, key rotation with MultiFernet, protecting API keys and secrets in multi-tenant environments.
Shadow AI is the unsanctioned use of AI tools by employees without IT approval. It exposes enterprises to data leakage, compliance violations, and breach costs.
HIPAA-compliant AI: BAAs with LLM providers, PHI de-identification, audit logging, encryption, zero training on patient data, and per-tenant access control.
Secure LLM API keys in multi-tenant AI platforms: per-tenant isolation, encrypted vaults, virtual keys with budgets, dual-key rotation, and dynamic secrets.
Field-level vs volume-level encryption for AI: AES-256-GCM at column level, TDE at storage level, when to use each, and why AI pipelines need both layers.
Secure AI ingestion pipelines with OAuth credential isolation, PII redaction, encryption at rest, content hashing, and audit trails from connector to corpus.
PII redaction before AI ingestion: three-layer detection (regex, NER, LLM judge), reversible tokenization, pipeline architecture. Presidio and FPE explained.
SSRF protection for AI agents: block cloud metadata, private CIDRs, DNS rebinding, numeric IP encoding. URL allowlists, DNS pinning, and egress proxies.
GDPR and AI compliance: legal bases, DPIA requirements, Article 22 automated decisions, data minimization, and DPA with providers. Both GDPR and AI Act apply.
AI hallucination data leaks: memorization, PII synthesis, training data exposure. Two-sided guardrails, RAG grounding, output validation, and DLP for LLMs.
BYOK AI security: let enterprises bring own LLM API keys and encryption keys. Per-tenant key isolation, cost control, provider flexibility, crypto-shredding
SCIM 2.0 for AI platforms: automate user lifecycle from IdP to AI tool. Endpoints, group sync, SSO integration, deactivation, and enterprise onboarding.
ISO 27001 vs ISO 42001: ISMS protects information security, AIMS governs AI. Overlap, differences, integration, Annex A controls, EU AI Act alignment.
AI risk assessment methodology: identify threats, score likelihood and impact on a 5x5 matrix, map to NIST AI RMF and EU AI Act. Step-by-step enterprise guide.
EU AI Act compliance checklist: classify AI systems, meet high-risk obligations by August 2026, avoid fines up to EUR 35M or 7% of turnover. 12 steps.
AI agent audit trails: log every tool call, reasoning step, policy evaluation, and data access. Hash chaining, EU AI Act Article 12, SOC 2, and GDPR compliance.
NIST AI RMF 1.0 for enterprise: GOVERN, MAP, MEASURE, MANAGE functions. 72 subcategories, Generative AI Profile, EU AI Act alignment, and audit-ready evidence.
2FA for AI platforms: TOTP, WebAuthn, push notifications, per-tenant enforcement, step-up auth for agent actions, recovery flows, and enterprise MFA policies.
RAG document permissions: enforce access control at the vector layer. Metadata filtering, per-tenant partitions, hybrid retrieval, permission-aware chunking.
Vector database encryption: AES-256 at rest, CMEK, per-tenant keys, app-layer encryption, tenant isolation with pgvector RLS, Weaviate, Pinecone namespaces.
Tenant isolation in multi-tenant AI: five isolation layers, vector store strategies, RAG leakage prevention, per-tenant encryption. Architecture guide.
AI incident response plan: detect, contain, and remediate prompt injection, data exfiltration, hallucination leaks, and model misuse. Playbooks, severity tiers, and post-incident review.
Document-level ACLs for AI retrieval: enforce per-document permissions in RAG, metadata filtering, pre-filter vs post-filter, pgvector RLS, and Cerbos PDP.
AI data localization vs residency: region-locked inference, per-tenant storage, sovereign AI, GDPR, EU AI Act, and DPDP compliance for multi-tenant platforms.
Knowledge graph security for AI: per-node RBAC, property-level access control, row-level filtering, tenant isolation, query rewriting, and audit logging.
Self-host your AI platform, encrypt secrets with Fernet, enforce per-tenant isolation, implement document-level ACLs, and redact PII before ingestion.
AI platform OAuth credential security: never store provider tokens in plaintext. Use encrypted token vaults, per-user OAuth, Nango, and short-lived tokens.
Zero-trust for AI agents: per-session credentials, JIT authorization, microsegmentation, behavioral verification, and approval gates for autonomous systems.
AI agent approval gates: risk-tiered action classification, HMAC-locked payloads, evidence packs, escalation SLAs, dual approval, and EU AI Act compliance.
Envelope encryption for multi-tenant AI: DEKs encrypt data, KEKs wrap DEKs, KMS manages KEKs. Per-tenant key isolation, BYOK support, and key rotation.
Service tokens vs API keys for AI agents: why static keys fail, OAuth client credentials, token exchange (RFC 8693), and credential brokers for agent auth.
SSO and OIDC for AI platforms: SAML vs OIDC, just-in-time provisioning, attribute mapping, role-based access, and SCIM integration. Full enterprise guide.
Self-hosted AI vs SaaS security: self-hosted keeps data in your perimeter. SaaS routes prompts to provider servers. For regulated data, self-hosted wins.
Confidential computing for AI: Intel SGX, AMD SEV-SNP, AWS Nitro Enclaves, NVIDIA Confidential GPUs, attestation, federated learning, and homomorphic encryption for enterprise AI.
To prevent sensitive data from leaking into AI prompts, use an AI gateway with DLP, redact PII before the model, and self-host. 410M violations prove the risk.