AI Exposure Management
Validates which AI agents attackers could actually exploit and prioritizes risk by correlating identity, data, and behavior signals into complete attack paths.
Picari insights
Mid-market to enterprise organizations running autonomous AI agents or copilot ecosystems that need contextual, runtime visibility into how attackers could chain agent permissions and data access.
- Mapping end-to-end exploit paths across AI agents and data stores
- Prioritizing agent vulnerabilities using real-time behavioral context
- Preventing unauthorized data exfiltration via AI runtime policy enforcement
- Teams only needing basic pre-deployment static code analysis (SAST) for AI models
- Organizations without active AI agent deployments or third-party copilot integrations
- Simple chatbot setups without access to enterprise tools, APIs, or data stores
Ask about pricing, alternatives, or if AI Exposure Management is right for you.
- Personalized fit analysis
- See relevant alternatives
- Run an evaluation when you're ready
Core capabilities
Common use cases
credential access monitoring
cross-agent data poisoning detection
data exfiltration prevention via untrusted triggers
privileged action protection from untrusted inputs
Things to consider
Every product has trade-offs, and which of them matter depends on your environment, your team and your timeline.
Zenity AI Exposure Management pricing and integrations
For Zenity AI Exposure Management integration and pricing details, ask Picari. Start a briefing with your question, such as whether it connects to your SIEM, identity provider and ticketing stack, or how it is priced at your seat count and data volume.
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Are you from Zenity? Verify this profileProfile last updated on 24 September 2026 by Picari.