Protecting AI Systems: Threats, Red‑Team Tactics, and Defense-in-Depth
Effective Artificial Intelligence Market Outlook distinguishes demos from deployable systems. Begin with discovery: workflow maps, data sources, privacy constraints, and target KPIs. Define evaluation protocols—factuality, safety, bias, and latency—plus business outcomes like CSAT, conversion, or cycle time.
Construct representative datasets and prompts; include retrieval grounding and policy tests. Compare small domain-tuned models versus large general ones under realistic loads and languages. Validate security (PII handling, prompt injection resilience), governance (model cards, audit logs, retention), and portability (exportable prompts, embeddings, and adapters).
Demand integration proofs with SSO, RBAC, data warehouses, CRMs, and ITSM to ensure closed-loop operation. PoCs should include human review workflows, cost controls (caching, batching), and fallback strategies. Track acceptance rates, escalation, cost per task, and user satisfaction. Assess vendor enablement—training, change management, and support SLAs—and verify long-term costs as usage scales. Publish a scored RFP, rollout roadmap, and risk register with mitigations.
Finally, institutionalize learning: a center of excellence, shared prompt libraries, and evaluation dashboards that inform continuous improvement. With rigorous, transparent research, organizations select AI solutions that are safe, effective, and economically sound at scale.