The Harmonic Difference
We put the worker at the center of AI security
In a busy market, controls for AI, AI governance and agentic security can become watered down and generic. We outline what actually makes Harmonic different, how we compare to the categories you're evaluating, and where we come head to head with other AI vendors.
How Harmonic is different
Four beliefs that start with the worker
Most AI security starts with the tool. Ours starts with the person using it. That means understanding what they're trying to accomplish, removing the friction from doing it safely, and coaching them in the moment instead of reactive blocking.
Bottom-up beats top-down
A thousand Copilot licenses bought off a strategy deck rarely produce value, because the org chart cannot see where AI helps and the people doing the work can. Every real gain has come from someone finding a use for the tool, not from a mandate handed down. Strategy should follow usage, not the other way round.
That means meeting people where they work. Watch what they do with a tool before deciding whether to allow it, and remove whatever friction pushes them around you. Block clumsily and the work does not stop; it just moves out of view. Coach rather than police, and adoption becomes something you shape instead of chase.
For the worker: the use case they found shapes the strategy instead of breaking a rule.
Before you jump to controls, you need to understand AI use, properly
This isn't about looking at traffic, or even just understanding shadow AI. It's about understanding use cases, categorizing every AI task your employees do. If they're using an unapproved tool, why? For what reasons?
Get that right and the security team ends up holding some of the best evidence in the company of where AI is actually creating value, not just where it's creating exposure. Reducing the risk is still the job. It just comes second, once you know what you're looking at.
For the worker: they're understood before they're policed.
You need to be on the device to offer real control
It's nice to integrate with your CrowdStrike or Microsoft, but real control needs more than that. It means an endpoint agent, a browser extension, a local MCP gateway.
Sure, you need the APIs and the OTels, but that's about visibility. Use the controls of those platforms and you get blunt enforcement, and massive end user frustration.
For the worker: protection travels with them, in the browser, on the desktop, inside the agent.
Small language models give you inline control without blocking employees
We layer on our small language models to offer inline control that reduces risk without being a blocker to employees. That works because it's quick enough (200ms) and accurate enough (96% fewer false positives). We went heavy on this early on.
And it's more than this. It's the customization, explaining to users why they can't, letting them give feedback, making it an awesome experience.
For the worker: a nudge that explains itself in 200ms, with a route to disagree.
Customer testimonials
What security and IT leaders tell us
Two themes come up in almost every conversation. Leaders want to understand how AI is being used, not just which tools show up in a log. And they want that understanding without a deployment project.
Competitive comparisons
"AI security" now describes three different markets
The AI space often looks like one category. We have broken it down into three, each with a different control point and buyer. Working out which one you are shopping for removes confusion from an evaluation, and it explains why several vendors that look like competitors turn out to be complementary in practice.
Market one
Securing the models you build
Red teaming, prompt injection defense, and runtime guardrails for the LLM applications your own engineers ship. The risk is that an attacker manipulates a model you are responsible for.
Typical vendorsHiddenLayer, Lakera, Straiker, Protect AI (now Palo Alto), NeuralTrust.BuyerAppSec and platform engineering.
Market two
Securing the identities of centralized agents
Discovery, posture management, permissions, and runtime supervision for agents built on platforms like Copilot Studio, Agentforce, and ServiceNow. The question these tools answer is whether a given agent identity is allowed to take a given action.
Typical vendorsNOMA, Zenity, Astrix (now Cisco), Natoma (now Snowflake), Pillar, SGNL.BuyerPlatform engineering and identity teams.
Market three
Where Harmonic lives
Securing the workforce
What your people and the agents running on their machines actually do with sensitive data, across every AI tool they touch, sanctioned or not. The control point is the moment of use, and the currency is content rather than credentials.
Vendors hereHarmonic Security, Onyx, plus network and browser vendors extending into the space.BuyerCISO, CIO, and the AI committee.
Harmonic lives in the third market and only in the third market. We do not red team your homegrown models and we do not manage agent credentials. In larger enterprises we frequently sit alongside vendors from the first two markets, and we will tell you that in a deal rather than after one. Our research library covers each of these markets in more depth.
Category comparisons
How we compare to the categories you're already evaluating
Most teams looking at Harmonic are also looking at a SASE platform, an endpoint vendor extending into AI, or an agentic security startup. Here's the honest read on each, including where the other approach is the better fit.
SASE & network security
Zscaler, Netskope, Cato, Palo Alto, Cloudflare
They see the traffic. We see what's inside the prompt. Most of our customers run both.
Endpoint & XDR
CrowdStrike, SentinelOne, Proofpoint, Microsoft Defender
Built to stop things coming in. Workforce AI governance is a question about what goes out.
Agentic & identity security
Zenity, NOMA, Straiker, Astrix, Natoma
They govern which agent can call which tool. We govern what's inside the call.
Named vendor comparisons
Four vendors we get asked about most
These are the direct comparisons that come up in live evaluations. We've tried to describe each vendor the way they'd describe themselves, then be specific about where we differ. If you spot something out of date, tell us and we'll fix it.
Onyx
A control plane for enterprise agents
Onyx catalogues agents across your environment. We understand usage at the task level and deliver context-aware controls.
Glow
Endpoint supply chain integrity
Glow protects against malicious packages and components. We govern what data flows through AI usage. Different problems, rarely competing.
Prompt Security (SentinelOne)
The closest architectural comparison
Same surfaces, different center of gravity. Their strongest work is app security. Workforce AI governance is the whole of what we do.
WitnessAI
Governs from the network
We govern from the device. That one architectural choice explains almost every difference between us.
Frequently asked questions
The questions buyers actually ask us
Short, direct answers, including the ones where the answer is no.
Is Harmonic a replacement for our SASE or our DLP?
No, and we do not pitch it that way.
Your SASE handles network access and your DLP handles known data channels. Neither was designed to read what an employee types into an AI tool or what an agent sends through an MCP server. Most of our customers keep both in place and add Harmonic for the interaction layer. If a vendor tells you their AI product replaces your entire data security stack, ask them which prompts they can read and on which surfaces.
We have Microsoft E5 and Purview. Do we still need Harmonic?
The two are complementary, and the split is clean: Purview protects Microsoft AI deeply, and Harmonic protects everything else.
Turn on Purview AI for Copilot. It does that job well. The gaps we fill are the 1,000+ third-party AI tools your employees use daily, prompt and response capture in Chrome and Firefox rather than only Edge for Business, and the distinction between a personal AI account and an enterprise one. Viva Insights can tell you which Microsoft app Copilot ran inside. It cannot tell you that finance is running quarterly pipeline reviews through a free-tier chatbot.
What surfaces does Harmonic cover?
Three, under one classification engine and one policy set.
- Browser extension for AI in the tab, including AI features embedded inside SaaS applications.
- Endpoint agent for desktop and native AI apps, IDEs, and CLI tools such as Claude Code and Codex.
- MCP Gateway running locally, for agent-to-tool traffic, with enforcement on the content of each call rather than only on the connection.
We also ingest APIs and OpenTelemetry feeds and integrate with endpoint platforms, which adds visibility. The three surfaces above are what give us control.
How long does deployment take before we see anything useful?
About 30 minutes to first insight, with no prerequisites to satisfy first.
This matters more than it sounds. Several products in this market require multiple prerequisites, licence tiers, and a day or more of processing before the first report appears, which pushes the evaluation into a project. Mike Janielis at Advisor360 described the difference as hours to seconds once a new tool is covered.
How does Harmonic handle an employee using an unapproved AI tool?
We show you the work they were trying to do, then let you decide.
The interaction is classified into a business use case, and the sensitive data inside it is classified separately, so you can see both the intent and the exposure. If the use case is legitimate and the tool is wrong, that is a procurement conversation rather than a security incident. If the exposure is real, the employee gets coached inline with an explanation of what was flagged and a route to disagree. Blanket blocking is available, and it is almost never the setting our customers keep.
Can Harmonic secure the AI applications and agents we build ourselves?
No. That is market one on this page, and it needs a different product.
Red teaming your own models, defending them against prompt injection, and putting runtime guardrails in front of an app your engineers shipped are jobs for a model security vendor. We govern how your workforce and their local agents use AI. In larger enterprises the two sit side by side, and we are happy to say which vendors we see doing that work well.
Why do you talk about use cases so much instead of blocking?
Because a block list is the output of an evaluation, not the input to one.
Policy written without knowing what work AI is doing tends to be either too loose to matter or so tight that employees route around it. Once you can see that legal is drafting first-pass contract reviews in one tool and engineering is debugging production data in another, the policy writes itself and the exceptions queue shrinks. That is the whole argument for belief one, and it is the thing Neil Patel at Apax was pointing at: which tools are being used is the easy question, and how they are being used is the one that changes decisions.
Next step
See what your workforce is actually doing with AI
Thirty minutes to deploy, and you will have a map of AI use across your organization with the sensitive data exposure attached to each use case.
Last updated: August 2026