Enterprises evaluating AI chat SaaS offerings now demand mature security posture—NOT promises of future hardening. Embedding a formal threat model and explicit controls early reduces breach risk, accelerates procurement, and provides a marketing differentiator (trust). This article operationalizes security for AI chat platforms.
1. Core Threat Categories
| Category | Examples | Impact | Control Themes |
|---|---|---|---|
| Prompt Injection | User craft attempts to exfiltrate hidden policies | Data leakage | Output filtering, allowlist context, instruction sandbox |
| Retrieval Poisoning | Malicious page injects false facts | Integrity loss | Canonical source validation, content hashing, diff review |
| SSRF via Crawler | Crafted URLs hitting internal hosts | Internal network exposure | Domain allowlist, IP range filters, protocol restrictions |
| Data Exfiltration | Token leakage, over‑broad logs | Privacy breach | Redaction, scoped logging, encryption at rest & transit |
| Supply Chain | Compromised dependency or model | System compromise | SBOM, signed artifacts, dependency scanning |
2. Threat Modeling Workflow
- Decompose architecture by trust boundary (embed → BFF → services → data stores).
- Enumerate STRIDE per boundary (spoofing, tampering, repudiation, info disclosure, DoS, elevation).
- Assign likelihood & impact scoring; produce risk matrix.
- Map existing & planned controls; identify residual risk + owners.
- Define test cases (penetration, fuzzing, injection payloads) per high risk vector.
- Review & version threat model (changelog + owners). Store in version control (
AGENTS.md).
3. Embedded Controls Catalogue
| Layer | Control | Rationale |
|---|---|---|
| Ingestion | robots.txt + allowlist | Prevents out-of-scope fetch & SSRF |
| Indexing | Content hash + canonical URL dedup | Reduces duplicate noise & poisoning vectors |
| Retrieval | Tenant + policy filters pre-score | Avoids cross-tenant leakage influencing ranking |
| Generation | Refusal policy + citation enforcement | Grounds answers & flags weak evidence |
| Storage | Field-level encryption (PII) | Minimizes breach blast radius |
| Transport | mTLS service-to-service | Prevents MITM within internal mesh |
| Ops | Secret rotation schedule | Limits credential lifetime value |
4. Evaluation & Telemetry
Key metrics: hallucination rate (trust), refusal appropriateness, retrieval precision@k, access anomaly alerts, failed auth attempts, crawler disallowed ratio, fallback reason distribution.
Ingest these into centralized dashboards; set alert thresholds (e.g., sudden spike in fallback timeouts could signal provider outage or internal regression).
5. Procurement & Compliance Alignment
- Provide documented data flows & retention policies.
- Offer configurable retention (delete on demand, GDPR support).
- Supply audit logs & export to customer SIEM via signed feed.
- Maintain SSO + RBAC alignment with least privilege.
6. Continuous Improvement Loop
- Monthly control effectiveness review.
- Simulate injection & poisoning payloads; record detection rate.
- Patch & retest; update threat model residual risk.
- Report summarized metrics to stakeholders / prospects.
Key Takeaways
Security is an ongoing engineering practice, not a marketing bullet. Codifying a living threat model, attaching metrics, and integrating controls into standard development workflows accelerate enterprise trust and adoption.