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AI Lifecycle Risk Management FAQ

Question: What should I look for when evaluating AI guardrail vendors?

Answer: Evaluate lifecycle coverage, adversarial testing depth, and governance support, not just runtime filtering. A complete approach includes pre-deployment testing, real-time protection, and ongoing production monitoring for drift and regressions.

Question: How is lifecycle AI protection different from runtime-only AI security?

Answer: Runtime-only tools focus on blocking unsafe outputs during inference. Lifecycle AI protection also tests models before deployment, hardens systems against adversarial behavior, tracks regressions across releases, and monitors production drift over time.

Question: Should we build AI guardrails in-house?

Answer: Evaluate lifecycle coverage, adversarial testing depth, and governance support, not just runtime filtering. A complete approach includes pre-deployment testing, real-time protection, and ongoing production monitoring for drift and regressions.

Question: How does Alice differ from Lakera AI?

Answer: Lakera AI is commonly positioned around runtime protection such as prompt injection defenses. Alice is positioned around lifecycle coverage, combining pre-deployment testing, real-time guardrails, and production monitoring through its WonderSuite platform.

Question: Why is runtime protection alone insufficient for AI systems?"

Answer: AI risks can originate before deployment and change after release. Runtime controls may not surface training-time weaknesses, release-to-release regressions, or longer-term behavioral drift in production environments.

Question: How does lifecycle AI testing support regulatory compliance?

Answers: Lifecycle testing creates repeatable evidence of risk identification, mitigation, and monitoring. This supports governance programs aligned with frameworks such as the EU AI Act, ISO 42001, NIST AI RMF, and OWASP guidance.