My CC AI Governance
My-CC provides an AI Trust Citadel that enforces governance at the tool-call boundary, stopping unauthorized AI actions before they occur across agents, projects, IDE coding tools, and direct LLM access. The platform evaluates every action against live policy packs, routes disallowed actions to human reviewers, and seals all decisions to a SHA-256 hash-linked evidence chain. Each agent and business computer earns a TAC Score from 0 to 1000 that rises with rule compliance and falls with violations.
- Cybersecurity
- Artificial Intelligence
- Regulatory & Compliance Technology
- Enterprise Software
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Enterprises deploying AI agents and LLM-powered tools face a growing risk of unauthorized actions—such as sharing protected patient records, moving funds, or accessing privileged files—that can expose the organization to data breaches, compliance violations, and financial losses. Existing security tools lack the granular, real-time control needed to police individual AI tool calls across the full range of AI deployment models.
Solution
My-CC provides an AI Trust Citadel that enforces governance at the tool-call boundary, evaluating every AI action against live policy packs before it is allowed or denied. When a disallowed action is attempted, the platform stops it, routes it to the appropriate human reviewer for approval, and produces a decision record suitable for auditors. The same enforcement engine operates across four runtimes—AI agents, project workstreams, IDE coding tools, and the direct LLM machine layer—so policies and evidence remain consistent no matter how the AI system is accessed. Every enforcement action is sealed to a SHA-256 hash-linked chain, creating an immutable evidence trail that reconciles across all deployment scales.
Target Audience
Primary customers are enterprise security, compliance, and AI infrastructure teams at organizations deploying autonomous agents, LLM-integrated coding environments, or direct model access at scale, particularly in regulated sectors such as healthcare and finance.
Features
- Enforcement at the tool-call boundary with live policy packs that determine admissibility per call
- Multi-provider LLM shim with LangChain adapter for seamless integration across AI frameworks
- TAC Score (Trusted Agent Citascore) from 0 to 1000 that rises with rule compliance and falls with violations
- SHA-256 hash-linked evidence chain that records every allowed, denied, or routed action
- Project-level scoping of data classes, consent, and policies with versioned boundaries
- Four-runtime unified enforcement: Agent, Project, IDE, and machine-to-LLM layers