eBook (PDF)
AI Security Buyer's Guide
AI has crossed a threshold. Agents now read email, ship code, and query databases without human oversight. Cyberhaven Labs found that nearly 40% of AI interactions involve sensitive data, yet most security programs were not designed for this reality. This guide frames six criteria for evaluating AI security programs built for the agentic era, not legacy architectures.
This is essential reading for security leaders, CISOs, and practitioners responsible for governing AI tools and autonomous agents across the enterprise.
Key Takeaways
Understand Why AI Security Requires a Different Approach
- Legacy tools see system behavior, not data behavior: EDR, legacy DLP, and cloud-based AI security tools each have blind spots that agentic AI exploits
- Shadow agents are already running in your environment: locally installed coding assistants, open-source agent frameworks, and custom MCP servers generate no footprint in SaaS inventories
- Enforcement has to follow the data, not the tool: as AI tools evolve, organizations anchored to data-centric security will scale with adoption rather than against it
Learn the Six Criteria That Separate Modern AI Security from Point Solutions
- Continuous agent and app inventory across endpoints and SaaS, including tools IT never approved or deployed
- Full execution lifecycle observability that reconstructs what an agent accessed, what it did, and what it passed downstream, not just what a user typed
- Data lineage that follows sensitive data through agent pipelines, format changes, and MCP tool calls to external models
Build a Program That Enforces Without Blocking Productivity
- Context-aware controls that distinguish a developer using test data from an agent exfiltrating production PII through the same channel
- Unified policy enforcement across GenAI SaaS and locally installed agents, managed from a single console
- AI Security built on the same platform and data layer as DLP, DSPM, and IRM, so investigations connect across the full program
Download the AI Security Buyer's Guide to get the evaluation criteria, readiness checklist, and vendor questions your team needs to govern AI in the agentic era.
