AI AGENT SECURITY PLATFORM
ASH is designed to support AI agent security across the model supply chain,
runtime behavior, endpoint execution, and governance.
Existing security products protect their own layers well,
but an AI agent's reasoning, tool calls, and actions sit outside their field of view.
ASH places protection points across the full agent execution flow.
Each stage's risks can be validated against your environment during a PoC.
User Prompt
User input enters
Threat Examples
Reasoning
Model reasoning and planning
Threat Examples
Memory
Context and memory lookup
Threat Examples
Tool Call / MCP / API
External tool and system calls
Threat Examples
Action
Task execution
Threat Examples
Result
Result return and follow-up
Threat Examples
Audit
Records and audit evidence
Threat Examples
Protection capabilities aligned with each stage of the execution flow.
Analyzes user input and context at the semantic layer for prompt injection and jailbreak attempts.
Analyzes whether agent reasoning and plans drift outside the approved scope of work.
Applies policy-based control to tool, MCP, and API calls to reduce over-privileged use and tool-chaining risk.
Designed to inspect sensitive data flows in tool-call parameters and responses.
Blocks risky actions in real time or routes them to Human-in-the-Loop approval.
Records the full execution trajectory, helping establish post-incident analysis and regulatory audit readiness.
ASH is organized into four layers spanning model, runtime, endpoint, and governance.
The effect of each protection layer can be validated during a PoC.
A layer designed to control supply-chain risk before models and adapters enter your organization.
A layer designed to analyze and protect prompts, tool and MCP calls, and data flows in real time during agent execution.
A layer that enforces policy on the endpoints and workloads where agents actually run.
A layer that records decisions and execution trajectories, helping establish governance and audit readiness.
