AI‑powered SaaS expands the attack surface: prompts, retrieval indexes, embeddings, model gateways, tool‑calls, and decision logs introduce new paths for data exfiltration, account takeover, and policy bypass. Treat AI features like high‑privilege automation endpoints: enforce identity and least privilege, harden retrieval and prompts against injection, constrain actions to typed schemas with policy‑as‑code, and monitor for misuse. Build incident response and auditability in from day one.
The expanded threat landscape
- Prompt‑injection and indirect prompt‑injection
- Untrusted content (emails, web pages, docs) instructs the model to ignore policies, leak secrets, or take unsafe actions.
- Retrieval/RAG data exposure
- Cross‑tenant leakage via indexing, mis‑scoped ACLs, stale/incorrect sources, or query‑time filter bypass.
- Embedding and cache leakage
- Sensitive data encoded in embeddings, tenant‑shared caches, or verbose model traces.
- Tool‑call and action abuse
- Free‑text payloads to production APIs; missing approvals; missing idempotency enabling replay/fraud; privilege escalation through tools.
- Model supply‑chain risk
- Third‑party model vendors, plugins, and weights with unclear data use, logging, or training policies.
- Data residency and egress
- Cross‑border inference or logging violating contracts or law; support tooling copying data out of region.
- Jailbreaks and policy evasion
- System prompt disclosure, unsafe completions, or biased/off‑policy outputs causing legal and brand harm.
- Injection into evaluation or logging paths
- Model‑generated content with active payloads rendered in consoles, causing script execution or misleading operators.
- Abuse and fraud
- Bot exploitation of generous APIs, variant‑spam to mine outputs, enumeration of knowledge bases, or cost‑exhaustion (DoS via tokens).
- Integrity and drift
- Silent failure from partner API/schema changes; model updates changing behavior beyond safe bounds.
Defense‑in‑depth controls (technical and product)
- Identity, auth, and tenancy
- SSO/OIDC with MFA; RBAC/ABAC and row‑level security; per‑tenant encryption keys; strict service‑to‑service auth with scoped tokens.
- RAG hardening
- Index only permissioned content; apply ACL filters before embedding and at query time; store provenance (URI, owner, timestamp, jurisdiction); refuse on low/conflicting evidence; show citations.
- Prompt‑injection and egress guards
- Content sanitization, URL/domain allowlists, HTML/JS stripping, untrusted‑content isolation; outbound egress filters; specialized jailbreak detectors; require grounded citations for claims.
- Typed tool‑calls with policy‑as‑code
- Strong JSON Schemas for every action; validate before execution; simulate diffs and show rollback; enforce eligibility, limits, maker‑checker, change windows; idempotency keys and replay protection.
- Least privilege for tools and models
- Separate credentials per tool; minimal scopes; JIT elevation with approvals and audit; secrets rotation and short‑lived tokens.
- Model gateway and routing controls
- Centralized gateway with timeouts, retries, quotas, per‑tenant budgets; route small‑first; cap variants; separate interactive vs batch lanes to prevent cost DoS.
- Data minimization and retention
- Trim prompts/context; redact PII/PHI; tenant‑scoped, encrypted caches/embeddings with TTLs and DSR‑aware deletion; “no training on customer data” by default.
- Output filtering and safety
- Classifiers for toxicity/PII leakage; fairness/exposure constraints for recommenders; refusal paths when evidence is weak or policy disallows.
- Supply‑chain security
- Vendor DPAs with “no training,” data locality, and deletion terms; pin model/SDK versions; SBOMs; signature verification of weights; sandbox plugins/connectors.
- Contract tests and drift defense
- Canary probes for partner APIs; schema/semantic drift detectors; auto‑generated PRs with mapping fixes; block risky releases.
Secure SDLC and ops for AI features
- Threat modeling per surface
- Identify assets (prompts, embeddings, tool creds), actors, and abuse cases; document mitigations and accept residual risk explicitly.
- Golden evals and safety gates in CI
- Tests for grounding/citations, JSON/action validity, refusal correctness, jailbreak/egress resistance, fairness metrics; block on regressions.
- Observability and anomaly detection
- Traces across retrieve → model → tool; dashboards for groundedness, JSON/action validity, p95/p99, router mix, cache hit; alerts on unusual retrievals, token spikes, variant explosions, tool‑call failures, and cross‑tenant access.
- Logging and auditability
- Immutable decision logs linking input → evidence → action → outcome; redact sensitive spans; signer identity; exportable for audits and investigations.
- Incident response playbooks
- Prompts/weights rollback, key rotation, cache purge, tool disable/kill switches; user comms templates; regulator notification checklists; post‑incident “what changed” reviews.
Product‑level safeguards
- Explain‑why panels with citations, timestamps, and uncertainty
- Reduce over‑trust and aid detection of fabrication or outdated sources.
- Simulation before execution and instant undo
- Show cost/impact and rollback plan; reduce reversal cost and blast radius.
- Progressive autonomy
- Start suggest; one‑click with preview; unattended only for low‑risk, reversible actions with rollback and alarms.
- User controls and transparency
- Budget caps, residency preferences, data‑use settings, model/prompt version visibility; clear refusal messages.
Policy and compliance posture
- Data handling policy
- No training on customer data by default; retention limits; region pinning; DSR automation for prompts/outputs/embeddings/logs.
- Access governance
- SoD/maker‑checker for funds/identity/config changes; periodic access reviews; toxic‑combo detection.
- Regulatory alignment
- SOC 2/ISO 27001/27701 controls; GDPR/CCPA purpose limitation and transfer safeguards; HIPAA/PCI where applicable; model‑risk documentation.
Attack scenarios and targeted mitigations
- Indirect prompt‑injection via uploaded doc
- Sanitize/segment inputs; ignore embedded instructions; confine model to retrieved, permissioned evidence; require citations; add “instructions‑only” filters.
- Cross‑tenant retrieval leak
- Enforce tenant/row filters pre‑embedding and at search; tenant‑scoped vector stores; canary probes for boundary testing.
- Free‑text tool‑call exploit
- Block free‑text; schema validation and policy gates; simulate; require approvals and idempotency; rate‑limit and anomaly‑score usage.
- Token/variant DoS
- Per‑tenant quotas/budgets; variant caps; router mix enforcement; cache aggressively; separate batch lanes; auto‑throttle on burn‑rate spikes.
- Model vendor mishandling
- Private/VPC inference; per‑request “no train” flags; vendor audits; encrypted transport; minimal data disclosure.
60‑day security hardening plan
- Weeks 1–2: Baseline and maps
- Data flow diagrams; asset inventory; residency decisions; enable tenant ACLs in retrieval; lock “no training” defaults; stand up decision logs; add tool schemas and policy gates.
- Weeks 3–4: Gates and tests
- Add JSON/action validators, simulation/rollback, idempotency; CI safety suite (grounding, jailbreak, egress, fairness); contract tests for top connectors.
- Weeks 5–6: Monitoring and controls
- Central model gateway with budgets; anomaly alerts (tokens, variants, retrieval patterns); cache encryption and tenant scoping; egress allowlists.
- Weeks 7–8: Playbooks and drills
- Kill switches, key rotation, cache purge, prompt rollback; red‑team prompt‑injection; cross‑tenant boundary probes; produce audit/export bundles.
Security checklist (copy‑ready)
- SSO/OIDC + MFA; RBAC/ABAC; row‑level security; secrets rotation
- Permissioned RAG with provenance, freshness, jurisdiction; refusal defaults
- Typed tool‑calls; JSON Schema validation; policy‑as‑code; idempotency; simulation and rollback
- Model gateway with quotas, variant caps, small‑first routing, separate batch lanes
- Tenant‑scoped encrypted caches/embeddings; retention limits; DSR automation
- Egress allowlists; content sanitization; jailbreak/injection detectors
- Contract tests, drift monitors, canary probes for connectors and schemas
- Decision logs with redaction and export; incident response runbooks and drills
Common pitfalls (and how to avoid them)
- Letting models issue free‑text production actions
- Enforce schemas, policy gates, simulation, and approvals; refuse on invalid or low‑evidence requests.
- Unpermissioned, stale retrieval
- Apply ACLs and freshness SLAs; cite sources and timestamps; prefer refusal over guessing.
- Over‑trusting model output
- Require citations and reason codes; show uncertainty; keep instant undo and maker‑checker for consequential actions.
- Ignoring cost/latency abuse
- Router/budget enforcement, cache discipline, variant caps; monitor token/GPU spikes.
- Weak vendor controls
- “No training” contracts, VPC/private inference for sensitive data, periodic audits, narrow scopes.
Bottom line: AI makes SaaS more powerful—and more attractive to attackers. Secure it by constraining what the model can see and do, grounding outputs in permissioned evidence, and executing only typed, policy‑governed actions with audit and rollback. Pair strong identity and residency controls with monitoring and drills, and the result is a platform that’s both capable and defensible.