Opsin announced on October 15, 2026, that it raised a $21 million funding round led by SYN Ventures and Race Capital. The company is building an enterprise platform that shows security teams which AI agents are running, what they can reach, and when their behavior crosses policy boundaries.
What Opsin announced
The round is intended to accelerate product development, expand into regulated industries, strengthen Opsin’s risk-protection layer and support hiring. Opsin describes itself as an enterprise AI security and governance company founded by James Pham (co-founder and CEO), Oz Wasserman (co-founder and CPO) and Jeremy Mailen (co-founder and CTO).
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The company’s core premise is that conventional asset inventories do not answer the questions created by autonomous or semi-autonomous software: which agents are active, who owns them, which identities and permissions they use, what data they can reach, and whether their actions match their approved purpose.
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Why AI-agent visibility has become a security problem
Opsin’s homepage cites the following figures, each attributed to the named organization and dated 2026:
| Issue | Reported figure | Attribution |
|---|---|---|
| Workforce access to sanctioned AI tools | 60% | Deloitte, 2026 |
| Enterprises with unknown AI agents | 82% | Cloud Security Alliance, 2026 |
| Enterprises reporting over-permissioned agents | 74% | Cloud Security Alliance, 2026 |
| Enterprises reporting hard-to-monitor access pathways created by agents | 79% | Cloud Security Alliance, 2026 |
These figures describe the scale of the governance challenge, not an independent validation of Opsin’s product. An agent can be approved for a narrow task yet retain access to broader systems, files or APIs. As agent builders and enterprise platforms proliferate, security teams can lose track of ownership and runtime activity even when the underlying accounts and applications are known.
How Opsin discovers and evaluates agents
Continuously updated inventory
Opsin says it creates an inventory that is continuously updated with each agent’s owner, last activity, permissions, tools and data reach. The company uses delegated API access across supported enterprise AI platforms, rather than endpoint software, browser extensions or employee surveys. That design is intended to give security teams platform-level visibility without installing software on every workstation or asking employees to report their own agents.
Opsin says it normalizes agents across Microsoft 365 Copilot, Copilot Studio, Claude Enterprise, ChatGPT Enterprise and Gemini. Normalization matters because equivalent information can be represented differently from one platform to another; a single inventory view can make ownership, access and activity easier to compare.
The Risk Graph
Its Risk Graph connects an agent’s intent, identity, access, data, tools and observed behavior. Opsin compares what an agent was intended to do with what it can actually reach and how it acts at runtime. That comparison is designed to distinguish a legitimate connection to a sensitive system from an unnecessary or unexplained one.
Response when boundaries are crossed
When policy boundaries are crossed, Opsin says teams can quarantine the agent, block the action or notify the owner. The product pages emphasize evidence-backed prioritization and remediation rather than attempting to block all enterprise AI use. In practice, that means a security team can investigate the context around a finding before choosing an intervention.
Questions the platform is designed to answer
Which AI agents are active in my company?
The inventory is intended to show discovered agents, their owners and their last activity across the connected enterprise AI platforms. A current activity field can help separate an abandoned experiment from an agent that is still operating in production workflows.
What data can my AI agents access?
Opsin says it maps permissions, tools and data reach, allowing teams to examine the systems and information available to an agent’s identity. The relevant question is not only whether an agent has a connection, but whether the scope of that connection exceeds the task it was created to perform.
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Security teams can use the relationship between intended purpose, identity, permissions and reachable data to prioritize agents whose access is broader than necessary. The product’s stated approach is to surface evidence for review instead of treating every powerful permission as an automatic incident.
How can I tell whether an agent is acting outside its intended purpose?
The Risk Graph is meant to compare declared intent with observed behavior and runtime access. A mismatch—such as an agent created for a limited workflow reaching unrelated tools or data—can be escalated for notification, blocking, quarantine or remediation according to the organization’s policy.
How this approach differs from a basic agent list
A name-only inventory can show that an agent exists, but it does not necessarily explain accountability or risk. Opsin’s described capabilities map several layers together:
| Security question | Opsin’s described capability | Why it matters |
|---|---|---|
| Agent inventory and visibility | Continuously updated records across supported platforms | Reveals active and inactive agents in one view |
| Ownership and accountability | Owner and last-activity information | Gives teams a person or group to contact when action is required |
| Permissions and data reach | Mapped identities, permissions, tools and reachable data | Shows the practical blast radius of an agent |
| Intent-versus-runtime alignment | Risk Graph comparison of intended purpose and observed behavior | Helps identify activity that does not fit the approved use |
| Evidence and prioritization | Context linking intent, access and behavior | Supports risk-based investigation rather than blanket blocking |
| Response options | Notification, action blocking or quarantine | Lets teams choose a proportionate response |
Customer references and reported outcomes
Opsin says it serves organizations ranging from mid-sized enterprises to Fortune 500 companies, including customers in healthcare, financial services, manufacturing and other regulated sectors. Its public references include Culligan and UiPath; the funding announcement also mentions Barry-Wehmiller and Wellstar Health System. These are company-provided customer references, not an independent market ranking.
“I need to know whether an agent’s connection to sensitive systems is risky or legitimate. Opsin gives me the deep context to tell the difference.” — Lior Eldad, VP of IT, UiPath
“Finally, I have a way to assess agent risks based on its capabilities and whether those capabilities align with the intended purposes.” — Jim Shelby, Global IT Security Engineer, Culligan
Opsin reports 67% faster investigation and 60% less exposure as impact outcomes. Those percentages are Opsin’s claims; the available material does not identify an independent study or audit validating them.
What organizations should verify before deployment
- Platform coverage: confirm that the enterprise AI services in use are among the supported integrations and understand how quickly newly deployed agents appear in the inventory.
- API permissions: document the delegated access required for discovery and ensure it fits the organization’s identity, privacy and least-privilege controls.
- Ownership data: establish how owners are assigned when an agent was created by a team, service account or departing employee.
- Policy definitions: write down what counts as an unacceptable mismatch between an agent’s stated purpose, permissions, data reach and observed behavior.
- Response workflow: decide who can notify an owner, block an action or quarantine an agent, and preserve the evidence needed for review.
- Regulated-data handling: validate how records about agent activity, identities and data access are retained and limited to authorized security personnel.
What the funding means for Opsin
The investment gives Opsin capital to pursue broader platform coverage, product development and hiring while extending its focus on regulated environments. Its stated strategy is not to stop enterprise AI adoption, but to make agent activity, access and accountability visible enough for security teams to govern it. The practical test for buyers will be whether the inventory remains current, the risk context is specific enough to support decisions, and the available controls fit existing incident-response and compliance processes.
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