Company context
Private, VC-backed
Founded by former Digital Shadows leadership; product timeline includes Model Context Protocol (MCP) Gateway launch in 2025
Vendor research
Review what this vendor says publicly, the security topics those statements may support, what remains unverified, and factual company context. This is not an assessment of product effectiveness or fit.
Company scale
A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company context
Founded by former Digital Shadows leadership; product timeline includes Model Context Protocol (MCP) Gateway launch in 2025
Company intelligence
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
Series A · $17.5M · 2024-10-02
Next47 · Ten Eleven Ventures
CEO & Co-Founder
CTO & Co-Founder
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
A hiring count is shown only when a clickable source is available.
Greenhouse careers board ↗Core company facts have supporting public sources.
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
Related frameworks
Evaluation questions
These questions come from security requirements with some public support. Use them as starting points for demonstrations, documentation review, customer references, or a buyer-observed pilot.
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
The test user's AI usage activity can be filtered or exported with AI-specific context.
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
The inventory shows which users, data classes, integrations, or providers are associated with the AI-enabled software as a service (SaaS) app.
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
The control event records policy reason, user, destination, action, and timestamp.
Discover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Block, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Detect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Apply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
Inventory AI systems and owners, translate policy and regulatory obligations into governed workflows, assess risk, manage approvals and exceptions, and retain audit evidence across the AI lifecycle.
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
Test models, applications, retrieval-augmented generation (RAG) systems, and agents before release and continuously thereafter using adversarial probes, evaluation suites, attack simulation, and security release gates.
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
Discover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
Mediate model, agent, tool, application programming interface (API), connector, and Model Context Protocol (MCP) traffic through an enforcement point that applies identity-aware policy, content controls, routing, rate limits, and auditable allow or deny decisions.
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Authorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Inventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
Register AI agents as accountable identities, bind them to owners and delegating users, authorize task- and tool-level access, issue short-lived credentials, review access, and revoke or suspend agent authority.
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
Discover and govern AI coding agents, integrated development environment (IDE) assistants, command-line agents, skills, hooks, extensions, Model Context Protocol (MCP) tools, filesystem access, commands, network activity, secrets, and software-supply-chain actions on developer workstations and build environments.
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
Visibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Publicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.
The vendor can map the sourced commercial model to per-user, per-seat, per-app, per-token, per-integration, or platform packaging.
Public sources
Showing the first 6 of 19 source records. Open additional records only when needed.
Every interaction visible. Every interaction governable.
No quoted source text is recorded for this claim.
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Transparently intercepts all MCP traffic enabling security teams to discover what clients and servers are in use, enforce granular policies to block risky actions.
Agents and humans on the same policy plane.
The ability to apply policy controls at the point where the AI agent interacts with internal systems.
where AI is driving productivity, where it's creating risk, and where the budget is being wasted.
Purview gives you visibility inside Microsoft, on Microsoft tools, with Microsoft pattern matching. Real AI usage is not Microsoft-only. We see the full stack across vendors, including the long tail and the agentic surfaces, and we govern with intent classification rather than regex.
AI does not live in one place. It runs in the browser tab your sales lead opened, the desktop app your developer installed, the agent your engineer kicked off, and the embedded copilot inside the SaaS tools you already pay for. Harmonic Security covers all four.
AI is everywhere your employees work: in approved tools, shadow apps, and the agents they're spinning up on their own. Harmonic Security sits at that layer, understands the intent behind every interaction, and governs it in real time.
You can block in real time, warn the employee with context about why the action is risky, or log silently for security team review.
Our data classification and logging give you the audit trail, the data residency controls, and the ability to demonstrate that AI use in your organization operates within defined boundaries.
Every interaction visible. Every interaction governable.
Tool calls, scopes, destructive actions.
No quoted source text is recorded for this claim.
Real-time visibility and control across the browser and desktop
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Browser extension, desktop client, and MCP gateway in one deployment