AI SDLC & Product Security

4 articles

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Defend

Secrets Management for AI Apps: API Keys, Model Providers, Tool Credentials, and Delegated Access

AI applications need disciplined secrets management across model provider keys, vector stores, tool credentials, OAuth tokens, browser sessions, cloud keys, notebooks, logs, prompts, and agent runtimes. Secure design requires centralized secret storage, short-lived and scoped credentials, delegated authorization, redaction, rotation, revocation, and incident-ready evidence.

10 min read

Defend

Secure AI Product Design: How Product Decisions Create or Reduce AI Risk

AI product decisions can create or reduce security risk by controlling autonomy, data visibility, uncertainty, approval design, reversibility, source attribution, workflow placement, and abuse resistance. Product security must be involved early enough to shape the feature, not merely review it after launch.

9 min read

Defend

AI Application Security Review Checklist: 100 Questions Before Production Launch

AI security reviews should use a structured checklist covering governance, data, prompts, RAG, tools, agents, providers, evals, telemetry, and claims before launch.

8 min read

Defend

Least Privilege for AI Agents: Designing Permissions for Tools, APIs, Browsers, and Filesystems

AI agents need least privilege at the tool, API, browser, filesystem, credential, tenant, and action level. Safe design requires tool classification, read-only defaults, argument validation, scoped credentials, sandboxing, approval gates, and auditable enforcement outside the model.

13 min read