Your AI agents call other agents, which call tools, which call more agents. RedLens tests the whole chain: cascading privilege escalation, shadow AI your teams already adopted, and instant virtual patches. It runs from your pipeline through a CLI and GitHub Action that gate the build at your severity threshold, and findings stream to Splunk, SentinelOne or Devo. Priced by workflow, not capped by asset count.
Download Solution Sheet (PDF)A single injected payload can propagate hop-by-hop through a chain of agents when a higher-privilege agent blindly trusts a lower-privilege agent's output. RedLens traces the full chain, identifies where an agent inherits privilege it shouldn't, and surfaces exactly which unauthorized tools or data a compromised hop could reach.
Engineering and business teams adopt AI tools and agent frameworks faster than security can inventory them. Shadow AI Discovery passively identifies unofficial AI usage across your environment: the agents nobody filed a ticket for.
Every confirmed finding can generate a ready-to-deploy virtual patch: a system-prompt wrapper, sanitizer rules, a WAF-style rule set, and executable middleware tailored to your framework. It is a mitigation you can ship immediately, while a permanent fix works its way through your normal release cycle.
The riskiest AI in your stack is the agent that can act: query the database, call the API, send the email. These modules measure and shrink that risk before an attacker does.
Security findings that live in a separate dashboard get ignored. RedLens puts them where your engineers and analysts already look.
One production workflow can route through a dozen micro-agents. Counting "AI assets" stopped making sense a while ago. RedLens Professional prices around monitored production workflows and included attack simulations instead, and Enterprise removes the cap entirely.
The AI security market consolidated fast because the category is real, but none of the acquirers built compliance-vertical depth into the product.