Funding
FUNDING GAP:

Neo Raises $100M To Control Enterprise AI Software Actions

Newsroom brief

SecurityWeek reported that Neo emerged from stealth with $100 million for a platform that governs AI agents, MCP servers and software actions across enterprise systems.

Verified against source materialEdited by SendTech Times Cybersecurity Desk
Neo Raises $100M To Control Enterprise AI Software Actions
Image source: SecurityWeek

Neo Funding Targets Enterprise AI Software Control

SecurityWeek reported that Neo emerged from stealth with $100 million for software that governs AI agents, AI-enabled applications and traditional enterprise tools, turning the round into a security bet on who controls automated actions inside company systems.

The American-Israeli cybersecurity startup received the seed and Series A investment from Andreessen Horowitz, Bessemer Venture Partners, Craft Ventures and Merlin Ventures.

The company plans to use the capital to expand engineering and go-to-market teams.

Neo's announcement names recognised venture backers and a broad control layer for agentic software, but does not name customers, deployment counts or measured risk-reduction results.

The Platform Catalogues Agents, Models And MCP Servers

Neo says the product is built around visibility first.

The platform keeps a continuous catalogue of agents, models, browser extensions, MCP servers and other active software elements across enterprise environments.

The control layer evaluates those assets for excessive access privileges and configuration vulnerabilities.

Security teams get one inventory for agentic tools and conventional software, including AI applications that might otherwise sit outside normal software controls.

Identity is the second part of the product claim.

Neo maps individual software actions to the originating human user, automated agent or application identity, then applies policies to tool calls, data movement, agentic workflows and API access.

The mechanism is relevant because agent deployments can operate through credentials that already look legitimate to enterprise systems.

A conventional malware alert may not explain whether an action came from a person, an authorised application or an automated agent acting through delegated access.

Security Teams Get A Manual-Review Gate

The operating model covers more than detection.

Neo says security teams can restrict unauthorised activities or pause suspicious operations for manual review, a workflow aimed at agent deployments that may act through valid permissions.

Founder and CEO Nick Warner framed that point around AI agents entering browsers, developer tools, SaaS platforms and traditional applications.

Warner said those systems can reason, invoke tools and move through workflows with valid user permissions, creating a need for real-time control over what the software can do.

The founding team gives the launch a security-operations profile.

Warner previously held leadership roles at SentinelOne, Cylance, McAfee and Forepoint; Shlomi Salem, Neo’s chief product officer, previously led detection engineering at SentinelOne; and chief technology officer Eran Shirazi co-founded EasySend.

Those resumes do not prove customer adoption, but they place the product in a security-operations lane.

The company is targeting the point where software identity, user permissions, API access and agent autonomy overlap.

Funding Does Not Yet Prove Deployment Scale

The financing gives Neo room to build product and sales capacity before enterprises standardise how they approve agentic software.

The mechanism is specific: as agents inherit user permissions and call tools across systems, access control and action attribution become part of deployment governance.

The product detail also separates the launch from a pure funding notice.

Inventory, policy enforcement, manual review and identity tracing are specific controls that security teams can evaluate when AI agents begin moving across developer tools and SaaS applications.

The announcement includes policy enforcement, inventory and identity tracing, while customer outcomes, deployment volumes and audited performance data remain unnamed for the new control layer.

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