AI and Agent Security
Protection for models, LLM applications and autonomous AI agents
AI deployments open layers that classic tools do not cover: foundation models, orchestration frameworks, embeddings, MCP servers and agents that make autonomous decisions. An inventory of models and agents, including those launched without the security team's knowledge, is the precondition for any control. Protection for LLM-based applications stops prompt injection, jailbreaking and data leakage in model responses. Agents run on broad permissions, so the blast radius of a breach is decided by detecting abuse while the agent acts and enforcing policy at the level of a single operation, not only when a token is issued.
What problems it solves
- AI agents and MCP servers running outside the security team's control
- Prompt injection and data leakage through LLM-based applications
- Over-permissioned agents and a wide blast radius
- No audit trail for autonomous actions
Typical use cases
- Inventory of models, agents and MCP servers across the organization
- Protecting LLM-based applications against prompt injection
- Detecting and blocking attacks on agents at runtime
- Enforcing agent policies and permissions at operation level
Partners in this area
Sweet Security
Sweet Security is a Runtime CNAPP that detects and blocks attacks on cloud infrastructure, workloads, applications and AI agents while the attack is still under way. A lightweight eBPF sensor and cloud logs build a behavioral baseline of the environment, and a single detection engine correlates events across every layer to cut MTTR to minutes, according to Sweet Security data.
Cloudflare
Cloudflare is a global cloud platform that protects applications, APIs, users and network infrastructure while improving performance and resilience. Its services combine application security, DDoS protection, Zero Trust, SASE, network security and developer capabilities in a globally distributed platform.
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