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Opsin

A San Jose startup that maps what enterprise AI agents like Copilot and Gemini can access, flagging data oversharing and policy violations in real time.

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45/100Emerging / Unranked

Overview

Opsin, founded in 2024 by James Pham, Oz Wasserman, and Jeremy Mailen, builds a security layer for enterprise generative AI tool usage, focused on Microsoft Copilot, Google Gemini, and similar assistants. Its platform continuously maps which AI agents exist across an enterprise, assesses what data each agent can surface, and detects oversharing and policy violations in real time — addressing the risk that an AI assistant with broad permissions inadvertently exposes sensitive data a user shouldn’t see.

The company is very early stage: public profiles describe as few as three employees shortly after founding, with a pre-seed round reported at around $3 million in mid-2024, and a separate profile describing a later seed round near $7 million in 2025. Founder James Pham previously worked on machine learning and data security at Abnormal Security, which gives the team relevant domain background even though the company itself has little independent track record yet.

Opsin’s differentiation is narrowly targeting the specific, fast-growing risk of GenAI oversharing inside tools enterprises are already rolling out broadly, rather than general LLM security. Given its very early stage, public evidence of named customers, measurable efficacy, or market traction beyond funding reports is essentially absent, which should temper expectations accordingly.

Innovation Matrix Assessment

Innovation Velocity 5/10

A very young company that has already shipped a working product addressing GenAI oversharing, though with limited time to demonstrate iteration speed.

Operational Value 6/10

Directly targets a concrete, increasingly common risk — AI copilots surfacing data a user shouldn't see — as enterprises roll out tools like Copilot and Gemini broadly.

Market Momentum 3/10

Reported funding is small (roughly $3-7M across conflicting sources) and no named customers or case studies were found publicly.

Category Disruption 4/10

GenAI oversharing detection is a real and timely problem, but the orchestration-layer approach is similar to other emerging AI data-governance tools rather than a unique mechanism.

Real-World Efficacy 3/10

No independent test results, named deployments, or case studies were found; evidence is limited to the company's own product description.

Enduring Relevance 6/10

As enterprise AI assistant adoption grows, oversharing and permission-boundary risks will become a more prominent governance concern.

Why CISOs Should Care

Addresses the specific, growing risk of enterprise AI copilots surfacing data outside a user's intended access boundaries.

What Makes It Different

Focuses narrowly on mapping and governing what enterprise AI agents can access, rather than general LLM prompt security.

The Matrix Verdict

45/100 — EMERGING / UNRANKED

Falls into Emerging/Unranked given its very early stage and the near-total absence of independent evidence beyond its funding announcements, despite a sensible and timely product focus.

Editorial Note: Claims vs. Verified Findings

Funding figures conflict across sources (roughly $3M pre-seed vs. $7M seed) and employee count (as low as three) comes from third-party job-board aggregators rather than company disclosures.

Sources