Industry Brief

AI product security for technology companies

How AI-native product teams can ship GenAI features with runtime controls and continuous testing.

Industry Brief8 min readTechnology, AI Product Teams

Product AI Carries Customer Risk

AI features often connect to customer content, product telemetry, support data, documentation, APIs, and admin workflows. The security model needs to live inside the product experience.

Developer Patterns

SDK, gateway, and API patterns should make it easy for engineers to inspect prompts, context, responses, tool calls, and actions without slowing release cycles.

Continuous Red Teaming

Every model, prompt, retrieval change, and tool integration can shift behavior. Continuous testing catches regressions before they become customer-facing incidents.

Release Evidence

Security review becomes faster when teams can show control coverage, test results, policy decisions, and remediation history for each AI workflow.

More Resources

Continue exploring AI security thinking.

Guide

9 min read

The enterprise guide to AI agent runtime security

A practical security model for agents that retrieve context, reason over data, call tools, and act across business systems.

For CISO, Head of AI, Security Engineering

Executive Brief

8 min read

How CISOs can govern workforce AI without slowing adoption

Policy patterns for shadow AI discovery, prompt DLP, app governance, and employee enablement.

For CISO, CIO, Risk and Compliance

Research Note

10 min read

Red teaming RAG systems for poisoned context and over-disclosure

Testing approaches for retrieval abuse, source trust, sensitive context leakage, and model response drift.

For Security Engineering, AI Product Teams

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See how Kavalan helps security and AI teams govern workforce AI, protect agentic systems, and continuously validate GenAI risk.

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