As organizations adopt AI-native workflows, the attack surface expands: data pipelines, model weights, prompts, plugins, and agent tools. For ETPA members driving adoption, security must evolve from perimeter defense to model-aware protection.
Begin with data. Classify sensitivity, enforce least-privilege access, and monitor exfiltration paths, including embeddings and logs. Adopt confidential compute for high-risk workloads, and use synthetic or masked datasets for collaboration. Next, treat models like packages: verify provenance (SBOM for models), scan for known risks, and pin versions. Maintain a registry with approval gates and rollback plans.
Prompt security matters. Create a shared policy library to filter user inputs and model outputs. Defend against prompt injection by limiting tool capabilities, sanitizing instructions, and verifying external calls. For agents, isolate execution, enforce timeouts, and log every tool invocation.
Evaluate continuously. Integrate red-team tests for jailbreaks, data leakage, bias, and toxicity. Automate evals in CI/CD so model updates can’t bypass safety checks. Couple this with runtime monitoring: detect anomalous outputs, sudden cost spikes, or unusual tool sequences.
Third-party ecosystem risk is real. Plugins and connectors can backdoor your environment. Vet vendors, use signed manifests, and prefer sandboxed connectors with scoped tokens. For regulated industries, align controls with frameworks (e.g., ISO/IEC, NIST AI RMF) and maintain auditable decision logs.
People and process close the loop: run incident response drills specifically for AI failures, define escalation paths for harmful outputs, and train teams to recognize social engineering around AI interfaces. For ETPA members, security is a product feature—marketable, measurable, and mandatory. When you can confidently say “our AI is safe by design,” adoption accelerates.
