MoorAI enforces at the point of use, on the device. It wraps the AI agents your developers already run — Claude Code, Codex, GitHub Copilot CLI — and reviews every prompt and output locally against policy in real time. Content never leaves the machine; security teams see only redacted, content-free signals. Open source (AGPL-3.0).
BigID governs data at rest. It's an enterprise data-security and governance platform: AI-asset discovery, best-in-class PII and sensitive-data classification, zero-trust access governance, shadow-AI and vector-DB monitoring, and policy push into data warehouses. These are different layers of the same stack, not substitutes — and they fit together well.
Read against Gartner's AI-TRiSM stack, MoorAI and BigID occupy adjacent layers. MoorAI sits in AI Runtime Inspection & Enforcement — live prompt and output interception at the point of use, on the endpoint. BigID sits in Information Governance — discovering, classifying, and governing sensitive data across an organization's stores, catalogs, and warehouses (AI-TRiSM as defined per Gartner's glossary). BigID answers “where is our sensitive data and who may touch it?” MoorAI answers “is this specific prompt about to leak something, right now, on this laptop?” BigID's blind spot is exactly there: the live developer endpoint, after data has left a governed store and is being typed into an agent.
| MoorAI | BigID | |
|---|---|---|
| AI-TRiSM layer | AI runtime inspection & enforcement | Information governance |
| Primary focus | Prompts & outputs at the point of use | Data at rest — discovery & classification |
| Where enforcement happens | On the device, in real time | In data stores, catalogs & warehouses |
| Coding-agent / terminal depth | Wraps the CLI / PTY — prompt + output | Not the endpoint agent layer |
| Data-at-rest discovery breadth | Endpoint AI inventory only | Broad — hundreds of data sources |
| PII / sensitive-data classification | Category-level, on-device | Best-in-class, ML-driven |
| Warehouse / catalog policy push | Out of scope | Unity Catalog: RBAC + column masking |
| Prompt content leaves the machine | Never | N/A — governs stored data |
| Blocks secrets read into agent context | ✓ Claude Code hooks · Codex / Copilot CLI detect-only | Out of scope — governs data at rest |
| MCP tool-call interception (arguments) | ✓ Inspects mcp__* call arguments | Out of scope |
| MCP allow-list enforced at call time | ✓ Non-allow-listed servers blocked | Out of scope |
| Reviews AI output, not just prompts | ✓ Output-stage detectors + CLI redaction | N/A — not a runtime AI layer |
| Runs fully on-device (no egress) | ✓ Always | Enterprise platform |
| Content-free telemetry | ✓ Category, risk level, one-way hash | Reads content by design — that is the classification |
| AIBOM export (AI bill of materials) | ✓ Providers, models, agent CLIs, MCP servers | Data-asset inventory across the estate |
| Time to first value | Minutes — install the agent | Enterprise rollout |
| Licensing | Open source (AGPL-3.0) | Proprietary |
Where BigID is stronger. BigID is a mature, broad data-security platform. Its discovery reaches across hundreds of data sources, its PII and sensitive-data classification is best-in-class, and it pushes policy directly into the warehouse — for Databricks Unity Catalog that means role-based access control and column masking today. It's the right tool for knowing where sensitive data lives across the enterprise and governing access to it.
Where MoorAI is stronger. MoorAI is single-purpose and lives at the point of use: real-time interception of the prompts and outputs flowing through a developer's coding agent, on the device, with nothing leaving the machine. It deploys in minutes, governs the agent / tool / MCP surface where BigID has no presence, and is open source.
Use them together. BigID governs data where it rests; MoorAI enforces on the prompt where the work happens. BigID tells you a store holds regulated data; MoorAI stops that data from being pasted into an agent from an endpoint BigID can't reach.
BigID capabilities described here reflect its published material. Databricks Unity Catalog integration ships role-based access control and column masking today; attribute-based (ABAC) row-level enforcement is on BigID's stated roadmap and is described as such here, not as a shipping feature. The AI-TRiSM layer names follow Gartner's glossary definition of AI Trust, Risk and Security Management; no Gartner quote is implied. BigID is a trademark of BigID, Inc.; this page is not affiliated with or endorsed by BigID. Comparison is architecture-level — verify current specifics against each vendor's docs. A “—” cell means we could not confirm the capability either way from published material; it is not a claim that the capability is absent.
Recently shipped in MoorAI — all on-device and content-free: a lethal-trifecta detector, rules-file poisoning detection (CLAUDE.md / .cursorrules), a capture-tier toggle (content-free by default), per-tool MCP argument rules, a per-agent assurance score, a data-lineage / Event Flow view, on-device model escalation, and cryptographically signed agency decisions — mapped across OWASP LLM Top 10, NIST CSF, NIST AI RMF, SOC 2, ISO 27001/42001, and the EU AI Act.