The world’s first AI governance runtime with a fully customisable policy DSL and enforcement architecture
Define policies in organisational language and enforce them across 26 AI execution boundaries using multiple enforcement patterns.
01.
Policy Enforcement at AI Execution Boundaries
02.
Governance Defined in Plain Language
Policies are written in an English like domain specific language aligned to organisational vocabulary. Legal obligations and other policies can be expressed directly in this language, creating a single authoritative source that is readable by engineers and policy teams.
03.
Multiple Enforcement Patterns
ARCS enforces policy through multiple integration paths. Use explicit boundary calls for precision, in process runtime instrumentation with no source changes, framework semantic interceptors, network gateway enforcement and MCP aware tool protocol control.
Runtime Enforcement Model
• Policies written in an English like policy DSL aligned to organisational vocabulary.
• Compiled into deterministic runtime rule artefacts
• Invoked at defined execution boundaries
• Multiple enforcement paths with minimal application change
• Supports direct boundary calls, in process runtime instrumentation, framework interceptors, network proxy enforcement and MCP aware tool protocol control
• Runtime decisions return one of four outcomes: Allow, Block, Modify or Escalate.
• Structured DecisionRecord emitted for every evaluation
Where ARCS Enforces Policy
Agent Based Systems
Controls model calls, tool execution, retrieval and workflow transitions in autonomous or semi autonomous agents through defined execution boundaries.
Enterprise AI Platforms
Provides a central decision authority across models, teams and applications, ensuring consistent and auditable policy enforcement.
High Risk AI Systems
Systems subject to regulatory oversight such as credit, recruitment, healthcare and critical infrastructure where policy enforcement must occur at execution time.
Why ARCS Exists
AI governance cannot rely on documentation alone. When AI systems operate in production, policy must exist at the point where decisions are executed.
Organisations are deploying AI systems that generate outputs and trigger actions with legal and operational consequences. Yet most governance today remains external to those systems as policy documents, review processes or audit reports. The people accountable for risk are often separated from the execution boundaries where decisions actually occur.
ARCS closes that gap. It introduces an independent decision authority layer that evaluates structured context at defined execution boundaries across model calls, tool execution, workflow transitions and protocol interactions. Policy is enforced deterministically before actions occur. Governance moves from paper to runtime. Read more …
