Comparison · agent governance · Prompt Security

Prompt Security, measured.

Prompt Security sits between your people and the AI they use — in the browser, on the endpoint, and in front of the MCP servers your agents call. This page scores that architecture across 16 capabilities against the same capabilities in Agen.co, with the sources, the scoring ladder, and the three rows Prompt Security wins all on the page.

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The short answer

  • This is real inline enforcement, and five rows say so. A browser sensor inspects a prompt as it is composed, an endpoint agent restricts a risky capability before it executes, and the MCP gateway evaluates every agent action before it runs. Nothing here is posture work relabelled.
  • They win the browser outright, against our own early access. A DOM-level extension deployed through MDM, doing real-time inspection and context-aware redaction at the moment someone types. Our BrowserShield is in early access and scores a 3 on every comparison we publish, including this one.
  • Redaction is their strongest capability and it ties us. AI-native data-loss protection across thousands of AI services and nearly thirty programming languages, catching secrets, PII and source code before they leave. That row is level, and so are the audit record per action and evaluating every agent action.
  • The product is shaped around a person at a keyboard. Coaching on a first violation, in-moment guidance while a prompt is being written, sensors installed in the browser someone is using. That is the right design for employee AI use, and it is the case with a human present.
  • Policy names the actor by the agent's role. For an agent running with nobody in front of it, a role is what the published model holds. Agen issues a named accountable human per agent and attributes every individual action back to them, autonomous runs included.
  • The meter counts people, not agents. Three touchpoints — employees, code assistants, homegrown apps — each licensed separately and each priced by the number of people it covers. Agen is priced per governed agent, which is the thing whose number is growing.
The long read

A product built around the person using AI.

Almost everything Prompt Security does well is organised around a human being present: a sensor in the browser they are typing into, guidance at the moment they compose a prompt, coaching when they get it wrong the first time. That is a genuinely good design, and it is worth being precise about where its assumptions stop holding.

Three touchpoints, four enforcement points

The product is sold as three touchpoints and deployed as four enforcement points, and the two do not line up one to one. Worth separating, because a buyer evaluating agent governance is usually shown the employee product first.

  • The browser extensionDOM-level interaction awareness, real-time prompt inspection, context-aware redaction and in-moment guidance, installed locally so data is caught before it leaves the machine. It ships through standard device management in minutes. This is the strongest thing in the product and it wins its row.
  • The endpoint agenta separate, system-wide client that watches local model execution — desktop assistants, local models, autonomous agents and MCP servers running on the machine. It detects new agents, their active skills and connectors, and can restrict a risky capability or disable a connector before execution rather than after.
  • The MCP gatewaysits between agents and MCP servers, inspecting every request and response, allowing or blocking by user, server or action, against a catalogue of more than thirteen thousand MCP servers it risk-scores. It evaluates every agent action before that action executes, with rules written per agent, per tool and per workflow.
  • The application patha reverse proxy or lightweight agent for an organisation's own AI applications, plus direct coverage of AI code assistants inside the development workflow, redacting secrets, PII and intellectual property across nearly thirty programming languages.

Where Prompt Security is strongest

Three of the sixteen rows below go to Prompt Security and four more are level. They follow from a real difference in where each product places itself: theirs is on the path data travels, ours is on the decision an agent's action requires.

  • The browserthis row goes to them by two and it is the clearest loss we publish on this page. Their extension reads the page at DOM level, inspects a prompt as it is being composed, redacts in context and guides the person in the moment, deployed through existing device management. BrowserShield is in early access and carries a 3 on every comparison we publish. When it reaches general availability the number changes in the rubric and every page inherits it — until then it stays where it is.
  • The network layerproxy deployment and network monitoring are theirs by design, and that row is scored a 2 for us on every comparison. We govern the action rather than the path, and we run alongside whatever already sits in that path — which is what lets one policy also reach an agent that never crosses it.
  • Where you can buy itthe product is now part of a major endpoint security platform and is transactable both through that platform's marketplace and a cloud marketplace against committed spend, with self-hosted variants of all three touchpoints for buyers with residency requirements. We are a focused platform rather than a marketplace presence, and that row is a 2 for us everywhere.
  • Level, not lostfour rows come out even and they are substantial ones. Redaction at the moment of action — their signature capability — stands beside ours rather than below it. Every agent action is evaluated before it executes, which is a level score on the row that separates a runtime product from a grant-time one. The audit record covers every agent action and decision. And their endpoint client is a real client, not a hook, so that row is level too.

The case with nobody at the keyboard

Read the employee product carefully and its assumptions are visible in the features themselves. Non-intrusive explanations of risk, shown to someone as they compose. Coaching on a first violation, escalating to enforcement on repeats. A sensor installed in the browser a person is looking at. Every one of those controls needs somebody there to receive it.

That is not a criticism of the design. It is the correct design for the problem it was built for, which is the enormous and real problem of employees pasting things into AI tools. It is worth stating plainly because agent governance is a different problem wearing similar words, and the difference is exactly the absence of that person.

An agent that runs on a schedule, reconciles a ledger and writes back to a system of record at four in the morning cannot be coached. There is no prompt being composed, no browser session, and no one to show an explanation to. What that action needs is not guidance but a decision, made before it lands, against something that says whether this agent — acting for a particular person — was permitted to do it.

A role is not a person

The gateway's policy model is documented in some detail, and it is a real model. Rules apply per agent, per tool and per workflow, evaluated against the agent's role, real-time situational factors, its behavioural history, and the trust relationships between agents, MCP servers and the data sources underneath them. That is a great deal more context than most products bring to a tool call.

It is also a description of the agent, not of anybody accountable for it. A role is a property the agent carries; behavioural history is a property of how it has acted. Neither answers the question an auditor asks first, which is who authorised this and who answers for it. On the employee side that question has an easy answer, because the sensor is bound to the person using it and their prompts are attributed to them by name. The two halves of the product answer it differently, which is why attribution scores a 3 rather than a 1 or a 5.

Agen holds the accountable human as a durable property of the agent rather than a property of a session. Every agent has a named owner, and every individual action resolves back to that owner whether or not anyone was watching when it ran. That is a different object to hold, and it has to be held before the action rather than reconstructed from a log afterwards.

The meter counts people

The commercial model is the clearest single statement of what the product is organised around, and it is published rather than inferred. There are six purchasable dimensions: employees, AI code assistants and homegrown applications, each with a self-hosted variant. They are billed independently of one another, and each scales by the number of users it covers. A user is defined as one person whose AI activity the platform covers.

For governing what employees do with AI that unit is exactly right, because the population being governed is the workforce and the workforce is countable. For agents it is the wrong denominator in a specific way: the number of agents in an enterprise is not tracking headcount, and a team of ten shipping four hundred agents is billed as ten. That sounds like it favours the buyer until the governance question is asked — those four hundred agents are the thing that needed governing, and they were never what was being counted.

Agen is priced per governed agent, with no prerequisite tier, which is why both commercial rows go the way they do. Our own row here is a 4 rather than a 5: for a buyer already paying for an incumbent, we are net-new spend, and that caveat is why the 4 is credible.

What is not published

Nine of the forty-two rows we score were dropped rather than published. Decision latency is the notable one: third-party summaries cite a figure, no vendor page publishes it, and a number we cannot trace to primary documentation does not score in either direction. Delegated access and agent-to-agent authority chaining are undocumented on both sides, so they were dropped rather than guessed in our favour. Identity-provider compatibility could not be checked at all, because the integrations page does not resolve.

One row was dropped on fairness rather than evidence. Connector breadth would have set their published figures — thousands of AI services observed, thousands of MCP servers risk-scored — against ours, which count connectors we broker actions through. Those are different measurements, and putting them in one row would have flattered us with a number that does not mean the same thing.

One more is ours. Self-hosted deployment ties at the top of the scale, on every touchpoint, and it did not make the sixteen. It is credited here instead: for a buyer with data residency requirements, that is a genuine strength and it belongs in the evaluation whether or not it discriminates between the two columns.

The scored comparison

Sixteen capabilities, scored side by side.

Runtime enforcement, identity and accountability, coverage, and what it costs to operate — each scored 0–5 on capability depth against vendor documentation, including the three rows Prompt Security wins.

Capability depthNoneCompletePrompt SecurityAgen.co
01 · Runtime enforcement
Verdict rendered at the moment of actionPrompt SecurityInline, where a sensor sitsStrong4/5Agen.coPer-action verdicts, <30msComplete5/5
Masking or redaction at action timePrompt SecurityRedacts before data leavesComplete5/5Agen.coMasking at action timeComplete5/5
Blocks a single action without disabling the agentPrompt SecurityPer tool and per workflowStrong4/5Agen.coAction-level enforcementComplete5/5
02 · Identity & accountability
Agent identity unified with human and machine identityPrompt SecuritySeparate touchpoints, separate coveragePartial2/5Agen.coOne fabric: humans, machines, agentsComplete5/5
A named human accountable for each agentPrompt SecurityPolicy targets an agent roleBasic1/5Agen.coNamed owner, every agentComplete5/5
Each individual action attributed to that humanPrompt SecurityAttributed where a person promptsCapable3/5Agen.coAttributed per actionComplete5/5
Access evaluated at action time, not only at grant timePrompt SecurityEvery agent action evaluatedComplete5/5Agen.coJudged in context, per actionComplete5/5
Audit record per actionPrompt SecuritySearchable log, every agent actionComplete5/5Agen.coA record per actionComplete5/5
03 · Coverage
Endpoint enforcementPrompt SecurityDedicated AI endpoint agentComplete5/5Agen.coAgenShield on the deviceComplete5/5
Browser enforcementPrompt SecurityDOM-level extension, MDM-deployedComplete5/5Agen.coBrowserShield, early accessCapable3/5
Cloud & SaaS enforcementPrompt SecurityAI services, not every SaaSCapable3/5Agen.coCloud and SaaS, one planeComplete5/5
Network-layer enforcementPrompt SecurityProxy and network monitoringStrong4/5Agen.coNot our layer — works alongsidePartial2/5
Agentless discovery — no SDK, no self-registrationPrompt SecurityRequires extension or endpoint agentPartial2/5Agen.coAgentless, 5 surfaces, no SDKComplete5/5
04 · Operate & buy
Pricing unit — what you are billed perPrompt SecurityPer person coveredPartial2/5Agen.coPer governed agentComplete5/5
Platform cost — total spend to govern N agentsPrompt SecurityThree touchpoints, billed separatelyPartial2/5Agen.coScales with agents, not headcountStrong4/5
Ecosystem and marketplace breadthPrompt SecuritySingularity and AWS marketplacesStrong4/5Agen.coFocused platform, not a marketplacePartial2/5
9 rows Agen.co leads4 tied3 rows Prompt Security leads
Levels reflect capability depth and supporting evidence. Capability descriptions based on vendor public documentation, September 2026.
What the table says

Both products act before the action lands. They disagree about who the action belongs to.

Read the four groups in order and the shape is unmistakable. Runtime enforcement is close, and two of its rows are level. Coverage is genuinely mixed — they take the browser and the network, we take agentless discovery and reach beyond AI tools into the rest of the estate. Then the identity group separates, and it separates in one direction on every row.

That is not a gap in feature scope. A product that inspects what passes through it reasons brilliantly about content and context, which is why it ties us on redaction and on evaluating every action. Reasoning about accountability requires a different object: an agent identity with a named human behind it, resolved at the moment of the action — not a role the agent carries, and not a person who happened to be in the session.

The findings

Four groups, four boundaries.

One per group in the table above, each traceable to the rows beneath it.

01

Inline, wherever a sensor sits

The browser extension blocks as a prompt is composed, the endpoint agent restricts a capability before execution, and the MCP gateway evaluates every agent action before it runs. Two of the three runtime rows are level. The bound is reach rather than timing: enforcement arrives on the surfaces where a sensor or gateway has been deployed, and those are licensed as separate touchpoints.

enforcement points4
evaluated before executionyes
02

The actor is a role

Policy identifies the agent by its role, its behavioural history and its trust relationships — a rich description of the agent, and not a person accountable for it. Where a human is prompting, the sensor is bound to them and attribution is real. Where an agent runs alone, there is no session user to name and no documented owner to fall back on.

accountable owner per agentnone
autonomous run attributionrole only
03

Discovery arrives with an install

The inventory is built by the sensors: a browser extension and an endpoint agent, deployed to machines. That is thorough where they are installed and silent where they are not, and it is why agentless discovery scores the way it does. Coverage is also scoped to AI tools and MCP servers rather than to every system an agent might act against.

discovery requires a sensoryes
browser enforcementtheirs
04

Billed per person, three times over

Six purchasable dimensions across three touchpoints, each billed independently and each scaling by the number of users covered — a user being one person whose AI activity is covered. Self-hosted variants exist for all three, which is a real strength for residency. The unit is the question: agent count and headcount are not the same number, and only one of them is growing.

pricing unitper person
separately licensed touchpoints3
Watch it happen

Every action resolved to the human who answers for it.

Not the person who happened to be typing, and not a role the agent carries — a named accountable owner on every agent, and every individual action attributed back to them, including the agents that run with nobody watching.

agent ownership · live product scene
What closes the gap

Keep the sensors. Add the layer that knows whose action it is.

Nothing about inspecting a prompt in the browser or redacting a secret before it leaves conflicts with governing the action itself. Agen adds what a sensor cannot carry: every agent discovered without anything being installed first, a named accountable human behind each one, and every action judged against your policy at the moment it happens — internal agents and the ones facing your customers, on the same plane.

Select a capability

Continuous discovery across your IdP, gateway, devices, cloud, and registries. Nothing has to self-register: agents are found, risk-scored, and resolved to a named human before they act.

  • Agentless and API-based across five surfaces
  • First-party, custom, and third-party agents alike
  • Every agent mapped to an owner, approver, and escalation
Agen Discover AI agent discovery flow: any identity provider, productivity suites and assistants, cloud, gateway, endpoints, and MCP server registries all feed one agent registry where every AI agent is inventoried, risk-scored on arrival, given a named human owner, and shadow AI is surfaced.
Discovery pulls from five surfaces into a single agent registry — no SDK self-registration required.
ClosesWhich agents are running that nobody registered?
no SDK required5 surfacesowner-mapped
Discover in depth →

Methodology

How these scores were reached.

Sixteen capabilities publish from a worksheet of forty-two scored rows, on one 0–5 ladder applied to both columns. Nine rows were dropped rather than guessed: decision latency, because no vendor page publishes a figure and third-party summaries do not score; step-up authentication, folded into the human-approval row so one gap is not counted twice; delegated access and agent-to-agent authority chaining, undocumented on both sides; new-integration SLA, unpublished; identity-provider compatibility, unverifiable because the integrations page does not resolve; connector breadth, dropped on fairness because the two sides publish figures that measure different things; our own audit-first rollout row, held pending internal confirmation; and self-hosted deployment, which ties at the top of the scale and is credited in the long read instead. Ties and the rows Prompt Security wins are printed rather than filtered.

Sources8 primary
Evidence ledger
  1. Prompt Security — agentic AI security and governanceTier AThe MCP Gateway sits between AI agents and MCP servers, inspecting every request and response in real time, deployed as a lightweight agent or a reverse proxy for custom applications. It allows or blocks by user, server or action according to policy, tracks and risk-scores more than 13,000 MCP servers, detects shadow MCP usage, and keeps complete searchable logs of every interaction.
  2. SentinelOne — Prompt SecurityTier ARedacts sensitive data and enforces policy across 15,000+ AI services in real time; blocks adversarial prompts and scrubs sensitive outputs with a real-time AI firewall; enforces least-privilege access so agents operate only within their defined scope; discovers shadow MCP servers and unsanctioned agent deployments automatically; and maintains a searchable audit log of every agent action, decision and enterprise system interaction. Deployment is described as taking minutes rather than months.
  3. AWS Marketplace — Prompt SecurityTier BSix pricing dimensions across three touchpoints — Employees, AI Code Assistants and Homegrown Apps — each with a self-hosted variant that runs in the customer's own environment, listed from $10,000 per 12-month contract and scaling by user count. A user is defined as one person whose AI activity the platform covers, varying by touchpoint: workforce members, developers, or application users. Multiple options can be combined and are charged independently per touchpoint.
  4. Prompt Security — browser and endpoint sensorsTier AThe browser extension provides DOM-level interaction awareness, real-time prompt inspection, context-aware data redaction, in-moment user guidance and discovery of shadow AI tools, installed locally so interactions are intercepted before data leaves the endpoint. A separate dedicated AI endpoint agent runs system-wide, detecting new AI agents, their active skills and connectors, prompt construction and responses, and autonomous behaviour across desktop assistants, local models and MCP servers, applying runtime policies that restrict risky capabilities and prevent exposure before execution.
  5. Prompt Security — the MCP Security GatewayTier AThe MCP Security Gateway evaluates every agent action before it is executed and applies rules per agent, per tool and per workflow, evaluating the agent's role, real-time situational factors, behavioural history, and the trust relationships between agents, MCP servers and downstream data sources. It embeds guardrails into agent workflows, applies AI-native data-loss protection to block or redact sensitive output, and includes human-in-the-loop approval mechanisms for high-risk actions.
  6. Prompt Security — for employeesTier AFor employee AI use the product provides observability to instantly detect and monitor all AI tools used within the organisation, data privacy through automatic anonymisation, risk management through granular department and user rules and policies, and employee awareness — coaching employees on safe AI use with non-intrusive explanations of the associated risk.
  7. Prompt Security — for developersTier ACoverage spans thousands of web-based AI tools and dozens of AI code assistants including GitHub Copilot and Cursor, instantly redacting and sanitising code to prevent the exfiltration of secrets, PII and intellectual property across nearly 30 programming languages, with visibility into AI usage across development cycles.
  8. Prompt Security — shadow AITier APositions an accurate AI inventory as the foundation of governance, on the principle that an organisation cannot govern what it cannot see.

Vendor capabilities change. If a row is out of date or wrong, tell us and we will re-score it — corrections are published with the date they were made.

FAQ

Questions, answered.

What is Prompt Security?
It is a security product for enterprise AI use, sold across three touchpoints: employee AI usage, AI code assistants for developers, and an organisation's own homegrown AI applications. It deploys as a browser extension providing DOM-level prompt inspection and redaction, a system-wide endpoint agent that monitors local models, autonomous agents and MCP servers, an MCP gateway that sits between agents and MCP servers, and a reverse proxy for first-party applications. It is now part of SentinelOne's platform following its acquisition.
Does Prompt Security block agent actions at runtime?
Yes, and it is worth being unambiguous about it. The MCP gateway evaluates every agent action before it is executed, applying rules per agent, per tool and per workflow, and allowing or blocking by user, server or action. The endpoint agent restricts risky capabilities and disables connectors before execution rather than after. The browser extension inspects and redacts a prompt at the moment it is composed. Two of the three runtime rows in our table are scored level as a result, and a third is a 4.
Where does Prompt Security score better than Agen.co?
Three rows of sixteen outright, and four more are level. Browser enforcement is the clearest and it goes to them by two: a DOM-level extension deployed through device management, inspecting and redacting in real time, against our BrowserShield which is in early access and scores a 3 on every comparison we publish. They also take network-layer enforcement, which we score a 2 on everywhere because we govern the action rather than the path, and marketplace reach on the strength of the parent platform's marketplace and a cloud marketplace listing. Level with us on redaction at action time, on evaluating every agent action, on the audit record per action, and on endpoint enforcement.
How does Prompt Security identify the human behind an agent?
It depends on which half of the product is acting. Where a person is using an AI tool, the browser extension or endpoint sensor is bound to that person on their machine, so prompts and violations attribute to them by name — that is real attribution and the table credits it. Where an agent is acting through the MCP gateway, the published policy model identifies the actor by the agent's role, its behavioural history and its trust relationships with servers and data sources. Those describe the agent rather than anyone accountable for it, and an agent running on a schedule has no session user to fall back on. That split is why attribution scores a 3.
How is Prompt Security priced?
Per person, across six purchasable dimensions: employees, AI code assistants and homegrown applications, each available vendor-hosted or self-hosted, listed from $10,000 for a twelve-month contract and scaling by user count. The touchpoints are billed independently of each other, so covering employees and developers and your own applications is three purchases. Their own definition is explicit: a user is one person whose AI activity the platform covers. Agen is priced per governed agent with no prerequisite tier.
Can Agen.co run alongside Prompt Security?
Yes, and given where the two sit it is the natural shape. Their sensors keep doing what they are good at — catching a secret in a prompt before it leaves the browser, redacting source code heading into a coding assistant, scoring the MCP servers your agents reach. Agen governs the action from a layer that holds identity: every agent discovered without anything being installed, a named accountable human behind each one, and every action judged against your policy in under 30ms with five verdict types including step-up authentication and human approval. Nothing you already run has to be removed.
What is the difference between inspecting a prompt and governing an action?
A prompt is what a person or a model composes; an action is what gets done to a system as a result. Inspecting the prompt asks what is in it — a secret, a customer record, an injection attempt — and that question is answered very well here. Governing the action asks whether it was permitted: whether this agent, acting for this named person, was entitled to write to that record. The two diverge on entirely ordinary work. An agent that reads a customer file and posts it to an internal channel composes nothing suspicious and leaks nothing to an outside service, so a content verdict correctly returns clean, while the entitlement question has not been asked.
How current is this comparison?
It is scored against vendor public documentation and re-scored on a schedule we hold ourselves to. Every published row is backed by at least one primary vendor source, all of which are listed on this page — so any row can be checked against the vendor's own documentation rather than taken on trust. Where a row could not be sourced in either direction it was dropped rather than guessed, and this page says which ones and why.

See the row that decides it.

Bring the agent you are least comfortable with — ideally one that runs on a schedule with nobody watching. We will show you the named human behind every action it takes, in a working environment, in under a day.

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