Industry guide
AI security controls need to fit the environment. A startup, hospital, university, SaaS company, and bank may use similar AI tools, but the data, trust boundaries, and failure modes differ.
Risk focus: patient data exposure, clinical workflow disruption, vendor AI sprawl, and identity recovery.
Controls to prioritize: PHI boundaries, approved model gateways, break-glass identity, audit trails, and vendor review.
Risk focus: fraud automation, model-driven social engineering, data leakage, trading workflow abuse, and third-party risk.
Controls to prioritize: transaction monitoring, privileged action approval, red-team testing, data loss prevention, and model logging.
Risk focus: source code leakage, support-ticket prompt injection, MCP misuse, tenant data exposure, and CI/CD compromise.
Controls to prioritize: repository isolation, secure coding assistant policies, tenant-aware logging, secrets scanning, and package controls.
Risk focus: open research environments, student account compromise, lab data exposure, and fast-moving AI experimentation.
Controls to prioritize: research sandboxing, identity hygiene, segmented lab networks, security champions, and AI acceptable use policies.
Risk focus: unreviewed AI tools, exposed API keys, weak vendor review, and production changes made by agents.
Controls to prioritize: simple approved-tool lists, virtual keys, secure defaults, code review gates, and incident checklists.
Risk focus: sensitive data leakage, procurement lag, identity compromise, and AI-assisted reconnaissance.
Controls to prioritize: strong data classification, approved providers, logging, segmentation, tabletop exercises, and supply chain review.
Next steps
Standardize model access, logs, policy, budgets, and key control before every team creates its own AI stack.
Model capability changes the clock. Detection, identity, and rollback need to keep up with faster attacker workflows.