Inline enforcement
Evaluating and deciding on an action before it executes, out-of-band, rather than detecting it after the fact. Fail posture (open or closed) is set per surface.
Learn more: AI Runtime Security
Part of the Agent Control Fabric: Highflame's identity, policy, and enforcement substrate for AI agents.
Keep exploring the glossary.
Just-in-time (JIT) access
Issuing short-lived, task-scoped credentials on demand that expire when the work is done: eliminating standing access there's nothing to leak or over-grant.
Read →LLM firewall
A checkpoint in front of a model that inspects prompts and responses for injection, sensitive data, and unsafe content. It guards the model's edge, not the agent's actions behind it.
Read →LLM security tools
The stack that protects LLM applications: input/output filters, firewalls and gateways, runtime enforcement, red teaming, and observability. No single tool covers all five.
Read →MCP (Model Context Protocol)
An open protocol that connects agents to external tools and data. Powerful for capability. But every connection is a new access path that has to be governed.
Read →MCP Gateway
A governed checkpoint every tool connection passes through (authenticated, policy-checked, and logged) so credentials stay central and unapproved servers can't connect.
Read →Mission drift
When a non-deterministic agent gradually diverges from its intended task. Tracked at runtime so it can be contained before consequences land.
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