Comparison · agent governance · Entro Security

Entro Security, measured.

Q: What is Entro Security?

It is a security platform organised as three pillars over one identity graph. Secrets Security detects over 1,200 secret types across source code, automation, cloud and on-prem, collaboration apps and vaults, triaging findings with a context model and attributing each one to its human owner. Non-Human Identities unifies every service account, token and API key into one inventory and governs it from creation through usage to retirement, with posture management and behavioural detection through NHIDR. Agentic Governance and Administration is the agent layer: it profiles each agent by where it runs, what it touches and which identities it uses, and enforces real-time policy over which AI client may access which resource, when, and for how long.

Q: Does Entro Security enforce policy at runtime?

Yes, and it is worth being unambiguous about it. Policy is enforced in real time across every agent and non-human identity, and enforced continuously rather than waiting for the next certification cycle. Just-In-Time access stops an agent holding elevated privilege longer than a task requires, provisioning onboards new agent credentials with minimum permissions, Segregation of Duties controls prevent one identity accumulating conflicting scopes, and MCP activity carries policy controls over sanctioned targets with audit trails of allowed and blocked activity. The bound is what the policy decides about: the unit is a client's access to a resource, not the individual action taken against it, and no verdict latency is published.

Q: Where does Entro Security score better than Agen.co?

Three rows of sixteen, and two more are level. Secrets discovery is the clearest and it is not close: over 1,200 secret types across the entire software lifecycle, with contextual triage and attribution to the engineer who committed each one. We do not scan for secrets on any surface, and we score a 1 on that row. Credential rotation, vaulting and retirement is the second — rotation policies with automated workflows, dual-secret switchover, encrypted vault storage and idle-credential reporting, a discipline we do not practise. The third is ecosystem reach, on cloud-marketplace availability and more than eighty named integrations. Level with us on prerequisite licensing, where neither product requires a suite tier first, and on first-party depth inside the productivity suite, where neither of us lives inside the suite we govern.

Q: How does Entro Security identify the human behind an agent?

Through creation lineage, and it does it well. Every discovered agent and identity is mapped with ownership, permissions, lineage and blast radius, and every secret incident is automatically attributed to the engineer, team or service responsible for it. That scores a 4 on our ladder and it is more than most tools in this category carry. The distinction is what the ownership is a property of: it belongs to the identity and describes who created it. What the published model does not describe is an individual agent action attributed back to a named accountable human — which is the record that matters when an autonomous agent acts overnight and somebody has to answer for what it did.

Q: How is Entro Security priced?

It is not published. There is no pricing page on the site, and every commercial path is a demo request. The one public data point is a cloud-marketplace Starter Pack listed at a fixed price for a twelve-month contract, but the listing labels its dimension in units without defining what one unit maps to — it may be identities, secrets, or another tracked object. That means the cost of governing a given number of agents cannot be modelled before a sales conversation, and it is why both pricing rows were dropped from this comparison rather than guessed. Agen is priced per governed agent with no prerequisite tier.

Q: Can Agen.co run alongside Entro Security?

Yes, and given how little the two overlap it is the natural shape. Entro keeps finding exposed secrets across your lifecycle, rotating and retiring credentials, and holding the inventory of every non-human identity — none of which we do. Agen governs the action from a layer that holds identity: every agent discovered agentlessly across five surfaces, 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.

Q: What is the difference between governing a credential and governing an action?

A credential verdict asks what this identity is allowed to reach. An action verdict asks whether this thing it just tried to do was permitted. The two diverge on ordinary work: an agent with a correctly-scoped credential reads a record it is entitled to read and posts it somewhere it is entitled to post, exceeding no permission and breaking no behavioural baseline — so a credential-scoped control correctly stays quiet, while the question of whether that person's agent should have moved that record has not been asked. Both controls are worth having. Only one of them can answer an auditor who wants to know who authorised a specific action.

Q: 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.

Entro finds every secret and non-human identity in your estate, attributes each one to a human owner, and now governs the AI agents that use them. 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 Entro wins all on the page.

Talk to us Jump to the comparison

The short answer

Entro enforces, and the score says so. Real-time policy runs across every agent and non-human identity — which AI client may reach which resource, when, and for how long — applied continuously rather than at the next certification cycle. This is not posture work relabelled, and the runtime rows are not scored as if it were.
The policy decides about access, not about the action. The governed object is the credential an agent holds and the resource that credential can reach. Whether an agent was permitted to take one particular action, on behalf of one particular person, is a different question and the model has no place to hold the answer.
They win three rows of sixteen. Secrets discovery is the clearest: over 1,200 secret types across source code, CI/CD, cloud, collaboration apps and vaults. We do not scan for secrets at all. They also take credential rotation, vaulting and retirement, and marketplace reach.
Ownership stops at the identity. Every discovered agent and identity is mapped with ownership, permissions, lineage and blast radius — genuinely more than most tools carry, and it scores a 4. What is not published is an individual action attributed back to the human accountable for it.
The enforcement point is the grant, not the device. Local agent runtimes are surfaced through an EDR integration the customer already owns; there is no endpoint client and no browser coverage, and remediation acts on the credential — rotate it, right-size it, revoke it, retire it — rather than on the single action.
Two rows are level, and both are commercial ones: neither product needs a prerequisite licence tier before you can buy it, and neither of us lives inside the productivity suite we govern.

The long read

What Entro's policy decides about.

Two products can both apply policy in real time and still be governing different things. Entro governs the credential an agent holds — it discovers it, owns it, right-sizes it, time-bounds it, rotates it and retires it. That is a complete answer to a real problem, and it is a different problem from whether this agent, acting for this person, was allowed to do this thing.

Three pillars over one identity graph

The platform is organised as three pillars sharing one graph of identities, the secrets behind them, and the resources they reach. They are worth scoring separately, because a buyer evaluating agent governance is usually shown the third and buying the first two.

Secrets Securitydetection of over 1,200 secret types across the whole software lifecycle — source code, pre-commit hooks, pull requests, automation, cloud and on-prem, collaboration apps and vaults. An AI triage model reads each finding's type, purpose and context to clear false positives autonomously, and every incident is attributed to the engineer, team or service that created it.
Non-Human Identitiesevery service account, token and API key unified into one contextualised inventory, governed from creation through usage to retirement. Posture management removes idle and stale identities, right-sizes permissions and eliminates over-privileged access, while NHIDR baselines each identity's normal activity and triggers remediation when behaviour departs from it.
Agentic Governance and Administrationthe agent layer. It builds a structured profile from three sources — where an agent runs, the targets it touches, and the identities it uses to touch them — surfaces local agent runtimes through an EDR integration, connects natively to the agent foundries, and enforces real-time policy over which AI client may access which resource, when, and for how long.

Where Entro is strongest

Three of the sixteen rows below go to Entro and two more are level. Two of the three are not features we are behind on — they are disciplines we do not practise, because each product starts from a different object: theirs is the credential, ours is the action.

Finding the secrets nobody vaultedthis row goes to them outright and it is not close. Over 1,200 secret types detected across the entire lifecycle, triaged by a model that reads the context around each finding rather than pattern-matching it, and routed to the person who committed it. We do not scan any surface for exposed credentials. We govern what an agent does with the access a credential grants, which is a different job and does not replace this one.
Rotation, vaulting and retirementrotation on a schedule with policy enforced through automated workflow integrations, dual-secret switchover so a rotation does not take a service down, encrypted vault storage, vault monitoring for fetching anomalies and privilege creep, and reporting on secrets that have gone idle or missed their rotation window. Credential hygiene is a whole discipline and it belongs to them. We govern the use of a credential rather than its lifecycle.
Where you can buy ittransactable through a major cloud marketplace against committed spend, with more than eighty named integrations spanning twenty-two categories from EDR and SIEM to vaults, ticketing and SOC automation. We are a focused platform rather than a marketplace presence, and the table reflects that.
Level, not losttwo rows come out even and both are commercial. Neither product requires a buyer to already hold a prerequisite suite or platform tier, which is a real advantage both of us have over the bundled incumbents. And neither of us lives inside the productivity suite we govern — we both govern it from outside, which is precisely what lets one policy also reach agents built anywhere else.

An owner on the identity is not an owner on the action

Ownership attribution is a headline capability here and it deserves the 4 it scores. Every discovered agent and identity is mapped with ownership, permissions, lineage and blast radius, and every secret incident resolves to the engineer or service that produced it. Most tools in this space cannot name a person at all.

What that answers is who made this identity. An accountable owner answers a narrower and more useful question: who answers for what this agent did at three in the morning. The first is derived from creation history and is a property of the object. The second has to be assigned, attested and carried forward onto every action the agent takes, including the autonomous runs with nobody in front of them.

This is why the identity group separates while the runtime group stays close. Attribution to a creator is a strong answer to a question about provenance. It is not the same object as accountability, and it cannot be turned into one by logging harder.

Grant time and action time are different tenses

Consider an agent with a valid, correctly-scoped credential for a customer database. It reads a record it is entitled to read and posts it into an internal channel it is entitled to post in. No permission is exceeded, no baseline is broken, no sanctioned target is violated. The grant was right and the action was wrong.

The published model has real answers either side of that moment. Beforehand, provisioning onboards the agent with the minimum permissions it needs, Just-In-Time access stops it holding elevated privilege longer than a task requires, and Segregation of Duties controls stop one identity accumulating conflicting scopes. Afterwards, behavioural detection notices when activity departs from the baseline and triggers remediation. Both are the right controls in the wrong tense for this case.

Agen evaluates entitlement in the moment, against the human behind the agent, with five verdict types rather than allow and deny — including step-up authentication and a human approval step before the action proceeds. Those rows in the table are not scored against a missing feature. They are scored against a decision object that describes access rather than actions.

Where the enforcement point sits

Coverage arrives through integrations rather than through anything installed in the path of the work. Cloud service providers, agent foundries, git repositories, CI/CD, vaults, collaboration tools and identity providers all connect over API with nothing to deploy — genuinely agentless, and one of the strongest capabilities on the sheet.

Two surfaces have no coverage. Local agent runtimes on a workstation are surfaced through an EDR integration the customer already owns, which means the enforcement point is somebody else's product and there is no control on the device itself. Nothing in the platform pages, the twenty-two integration categories or the six use cases addresses the browser at all — no extension, no in-browser control, no coverage of browser-driven agents. Those two rows are why endpoint and browser score where they do.

The same shape decides the intervention row. When something is wrong, the remediation acts on the credential: rotate the secret, right-size the permission, revoke the access, decommission the identity. Every one of those stops the agent rather than the action, which is the difference between a governed agent and a disabled one.

What is not published

Eight of the rows we score were not published. Two of them are the pricing rows: there is no pricing page, and the single public data point is a cloud-marketplace Starter Pack listed at a fixed annual figure whose unit the listing does not define. That means the cost of governing a hundred agents cannot be modelled before a sales conversation. It is a normal enterprise motion and it is not a criticism, but it is why a row that publishes on almost every comparison could not publish here.

Agent-to-agent delegation, on-behalf-of access, deployment model and integration SLA were dropped because no primary source addresses them in either direction. One more was dropped on fairness rather than evidence: connector and tool breadth measures a broker, and Entro brokers nothing — its integrations read telemetry from systems the customer already runs. Scoring that row would have measured our architecture rather than a capability theirs was built to have.

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 Entro wins.

Capability depthNoneComplete	Entro Security
01 · Runtime enforcement
Verdict rendered at the moment of action	Entro SecurityReal-time policy on resource accessCapable3/5	Agen.coPer-action verdicts, <30msComplete5/5
Allow / deny enforcement	Entro SecurityAllow and block, access scopeStrong4/5	Agen.coAllow and deny, every actionComplete5/5
Human-in-the-loop approval on a risky action	Entro SecurityNo approval step documentedBasic1/5	Agen.coHuman-in-the-loop, built inComplete5/5
Blocks a single action without disabling the agent	Entro SecurityRevokes access, not one actionPartial2/5	Agen.coAction-level enforcementComplete5/5
02 · Identity & accountability
A named human accountable for each agent	Entro SecurityAttributed to the creating ownerStrong4/5	Agen.coNamed owner, every agentComplete5/5
Each individual action attributed to that human	Entro SecurityIdentity owner, not per actionPartial2/5	Agen.coAttributed per actionComplete5/5
Access evaluated at action time, not only at grant time	Entro SecurityContinuous, JIT-bounded, not per actionCapable3/5	Agen.coJudged in context, per actionComplete5/5
Credential rotation, vaulting and retirement	Entro SecurityRotation, vaulting, idle retirementComplete5/5	Agen.coGoverns use, does not rotatePartial2/5
03 · Coverage
Endpoint enforcement	Entro SecurityEDR telemetry, no endpoint clientPartial2/5	Agen.coAgenShield on the deviceComplete5/5
Browser enforcement	Entro SecurityNot documentedBasic1/5	Agen.coBrowserShield, early accessCapable3/5
External customer-facing agents	Entro SecurityEnterprise-internal scopeBasic1/5	Agen.coCustomer-facing agents, same planeComplete5/5
MCP tool governance	Entro SecuritySanctioned MCP targets, tools visibleCapable3/5	Agen.coMCP tools governed per callComplete5/5
Secrets discovery across code, CI/CD and vaults	Entro Security1,200 secret types, full SDLCComplete5/5	Agen.coNot offeredBasic1/5
04 · Operate & buy
Prerequisite licensing	Entro SecurityStandalone, no prerequisite tierComplete5/5	Agen.coNo prerequisite tierComplete5/5
First-party depth inside the productivity suite	Entro SecurityGoverns the suite from outsidePartial2/5	Agen.coGoverns the suite, doesn't live in itPartial2/5
Ecosystem and marketplace breadth	Entro SecurityAWS Marketplace, 80+ integrationsCapable3/5	Agen.coFocused platform, not a marketplacePartial2/5

11 rows Agen.co leads2 tied3 rows Entro leads

Levels reflect capability depth and supporting evidence. Capability descriptions based on vendor public documentation, August 2026.

What the table says

Both products apply policy in real time. Only one of them is deciding about the action.

The runtime group is closer than the shape of this market would suggest, and it should be. Real-time policy over which client may reach which resource, enforced continuously rather than on a certification cadence, is genuine enforcement and it scores as such. Read the four groups in order and the pattern is clear: enforcement is close, coverage splits on where the enforcement point sits, and the identity group is not close at all.

That is not an accident of feature scope. A platform built on the credential can reason precisely about what a credential may reach, which is why it ties or beats us on the rows about credentials and wins the two about their lifecycle outright. Reasoning about whether one action was permitted requires a different object: an agent identity with a named human behind it, evaluated at the moment of the action rather than bounded at the moment of the grant.

The findings

Four groups, four boundaries.

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

The policy decides about access

Real-time policy runs across every agent and non-human identity, continuously rather than at the next certification cycle, covering which AI client may reach which resource, when, and for how long. What it does not evaluate is the action taken with that access. There is no published step-up control and no approval step that holds a risky action for a person before it proceeds.

policy objectclient → resource

verdict types publishedallow · block

Ownership lands on the identity

Every discovered agent and identity carries ownership, permissions, lineage and blast radius, and every secret incident resolves to the engineer or service that created it. That is real attribution and it scores a 4. What no published capability does is resolve an individual agent action back to the human accountable for it, which is the record an auditor asks for.

attribution basiswho created it

per-action attributionnot published

The enforcement point is the grant

Discovery is agentless across cloud, foundries, repositories, CI/CD and vaults, with nothing to install — a genuine strength. But local agent runtimes are surfaced through an EDR integration the customer already owns, no browser coverage appears anywhere, and remediation acts on the credential: rotate, right-size, revoke, retire. Each of those stops the agent rather than the action.

endpoint clientnone

browser coveragenot documented

Priced on request, bought in the marketplace

There is no pricing page and no published unit, so both pricing rows were dropped rather than guessed — the one public figure is a cloud-marketplace Starter Pack whose unit the listing does not define. Marketplace reach itself is a row they win: transactable against committed cloud spend, with more than eighty integrations across twenty-two categories.

published pricing unitnone

named integrations80+

Watch it happen

Every action resolved to the human who answers for it.

Not the engineer who created the credential, and not a baseline the behaviour departed from — a named accountable owner carried 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 credential hygiene. Add the layer that decides about the action.

Nothing about finding an exposed secret or rotating a stale one conflicts with governing what an agent does with the access it holds. Agen adds what a grant cannot carry: every agent discovered without being routed anywhere 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 →

Every request is evaluated against the identity behind the agent and answered in under 30ms: allow, mask, step up, hand to a human, or deny. The action is where the risk lives, so the action is where the decision happens.

Per-action verdicts anchored to the human behind the agent
Step-up and human-in-the-loop only when policy is crossed
Evidence generated at action time, not reconstructed later

Agen Govern runtime AI agent governance flow: an attempted action such as an assistant reading a document, an agent exporting records, or a custom agent writing to production is resolved to the identity behind the agent and checked against policy in 22 milliseconds, returning a per-action verdict of allow, mask, step-up, human-in-the-loop, or deny. — Every agent action is judged in-line against the human behind it, and answered in under 30ms.

ClosesWho approved this action, and who is accountable for it?

<30ms verdictstep-uphuman-in-the-loop

Govern in depth →

One policy plane enforced identically on the endpoint, in the browser, at the gateway, and in the cloud. Agent risk lives in the gaps between point tools, so there are no gaps to live in.

AgenShield stops dangerous local actions before they execute
BrowserShield catches company data at the paste (early access)
One policy, one identity chain, enforced the same way everywhere

Agen Shield AI agent enforcement flow: one policy plane written once in Govern and anchored to the same identity is enforced identically on the endpoint with AgenShield, in the browser with BrowserShield, at the gateway across 150+ connectors, and in the cloud across SaaS and internal apps. — One policy, written once, enforced the same way on every surface where agents actually run.

ClosesWhat happened on the laptop and in the browser that no log saw?

endpointbrowsergateway · cloud

Shield in depth →

Methodology

How these scores were reached.

Sixteen capabilities publish from a worksheet of forty-four scored rows, on one 0–5 ladder applied to both columns. Eight rows were dropped rather than guessed: both pricing rows, because no pricing unit or rate is published and the one marketplace figure leaves its unit undefined; agent-to-agent delegation and on-behalf-of access, unsourced in either direction; deployment model and data residency, which the site does not state; new-integration SLA, unpublished; our own audit-first rollout row, held pending internal confirmation; and connector breadth, dropped on fairness because Entro brokers no connectors. Ties and the rows Entro wins are printed rather than filtered.

Sources9 primary

Evidence ledger

Entro Security — platform overviewTier APositioned as governing every AI agent and securing every action, across three pillars: secrets, non-human identities and AI agents. Named capabilities include Discovery and Inventory of AI agents, NHIs and secrets; Classification with ownership, permissions and lineage; Posture Management; Lifecycle Management; NHIDR behavioural detection and response; Idle Secrets; Ownership Attribution; Secrets Scanning; Lineage Mapping; and Zero Trust, PAM and JIT access. No verdict latency or performance metric is published.
Entro Security — Entro for AI AgentsTier AAgent coverage is expressed through the non-human identities an agent uses: agents are monitored through their NHIs with NHIDR, Entro tracks how agents use secrets and consume resources and detects abuse in real time, and agentic NHIs are attributed to their creators. The page publishes no verdict types beyond allow and block, no decision latency, and no SDK or in-path interception requirement.
Entro Security — Entro for Non-Human IdentitiesTier AUnifies every service account, token and API key into a centralised contextualised inventory, and governs each non-human identity from creation through usage to retirement with continuous monitoring, human ownership attribution, attestation and policy-led automation. Posture management removes idle and stale NHIs, right-sizes permissions and eliminates over-privileged access. NHIDR baselines identity activity, alerts on suspicious patterns and triggers automated remediation.
Entro Security — Secrets SecurityTier ADetects over 1,200 secret types across the entire software development lifecycle — source code, pre-commit hooks, pull requests, workflows, automation, cloud and on-prem, collaboration apps and vaults. The ContextIQ model analyses each finding's type, purpose and context to autonomously triage false positives. Every secret incident is automatically attributed to its responsible human owner, linking back to the engineers, teams or services that created it, with built-in escalation paths. Integrates with secrets managers to monitor vaulted credentials for fetching anomalies and privilege creep.
Entro Security — integrationsTier AMore than eighty named integrations across twenty-two categories: Active Directory, CI/CD, cloud service provider, collaboration, CRM, developer tools, DevOps tools, DSPM, EDR, git repository, HR, IaC, identity provider, messaging, on-prem, password manager, project management, SIEM and data, SOC automation, storage, ticketing and vault. No integration SLA is published, and no browser, endpoint-client or connector-broker category appears.
Entro Security — use casesTier ASix published use cases, all enterprise-internal: governance and administration, SOC automation, compliance/posture/reporting, securing public cloud and SaaS, securing the private cloud, and mergers and acquisitions. No published use case or capability addresses external customer-facing agents, step-up authentication, human-in-the-loop approval workflows, action-time masking, or browser coverage.
AWS Marketplace — Entro Security listingTier BAn Entro Security Starter Pack is listed at a fixed price for a twelve-month contract; the listing labels its pricing dimension in units but does not define what one unit maps to. The product is described as end-to-end non-human-identity lifecycle management and secrets security, discovering over 1,000 NHIs and secrets across infrastructure, with agentless API integration and real-time NHIDR detection. This is the only public pricing data point; no pricing page exists on the vendor site.
Entro Security — governance and administrationTier AEvery discovered agent and identity is mapped with ownership, permissions, lineage and blast radius. Provisioning is automated so new NHIs and agent credentials onboard with the minimum permissions they need, and offboarding removes access when the need ends. The Agentic Governance Architecture enforces real-time policy across every agent and NHI covering which AI client can access which resource, when, and for how long. Just-In-Time access means no agent holds elevated privileges longer than the task requires, policy is enforced continuously rather than at the next certification cycle, and Segregation of Duties controls prevent a single agent or NHI accumulating conflicting permission scopes.
Entro Security — rotation and vaultingTier AProactive rotation of secrets on a regular schedule, with rotation policies established through automated workflow integrations. Dual-secret strategies enable seamless switchover so rotation avoids downtime. Vault storage with encryption at rest, least-privilege permissioning, and compliance reporting that identifies idle secrets and secrets that have not been rotated per policy.

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 Entro Security?

It is a security platform organised as three pillars over one identity graph. Secrets Security detects over 1,200 secret types across source code, automation, cloud and on-prem, collaboration apps and vaults, triaging findings with a context model and attributing each one to its human owner. Non-Human Identities unifies every service account, token and API key into one inventory and governs it from creation through usage to retirement, with posture management and behavioural detection through NHIDR. Agentic Governance and Administration is the agent layer: it profiles each agent by where it runs, what it touches and which identities it uses, and enforces real-time policy over which AI client may access which resource, when, and for how long.

Does Entro Security enforce policy at runtime?

Yes, and it is worth being unambiguous about it. Policy is enforced in real time across every agent and non-human identity, and enforced continuously rather than waiting for the next certification cycle. Just-In-Time access stops an agent holding elevated privilege longer than a task requires, provisioning onboards new agent credentials with minimum permissions, Segregation of Duties controls prevent one identity accumulating conflicting scopes, and MCP activity carries policy controls over sanctioned targets with audit trails of allowed and blocked activity. The bound is what the policy decides about: the unit is a client's access to a resource, not the individual action taken against it, and no verdict latency is published.

Where does Entro Security score better than Agen.co?

Three rows of sixteen, and two more are level. Secrets discovery is the clearest and it is not close: over 1,200 secret types across the entire software lifecycle, with contextual triage and attribution to the engineer who committed each one. We do not scan for secrets on any surface, and we score a 1 on that row. Credential rotation, vaulting and retirement is the second — rotation policies with automated workflows, dual-secret switchover, encrypted vault storage and idle-credential reporting, a discipline we do not practise. The third is ecosystem reach, on cloud-marketplace availability and more than eighty named integrations. Level with us on prerequisite licensing, where neither product requires a suite tier first, and on first-party depth inside the productivity suite, where neither of us lives inside the suite we govern.

How does Entro Security identify the human behind an agent?

Through creation lineage, and it does it well. Every discovered agent and identity is mapped with ownership, permissions, lineage and blast radius, and every secret incident is automatically attributed to the engineer, team or service responsible for it. That scores a 4 on our ladder and it is more than most tools in this category carry. The distinction is what the ownership is a property of: it belongs to the identity and describes who created it. What the published model does not describe is an individual agent action attributed back to a named accountable human — which is the record that matters when an autonomous agent acts overnight and somebody has to answer for what it did.

How is Entro Security priced?

It is not published. There is no pricing page on the site, and every commercial path is a demo request. The one public data point is a cloud-marketplace Starter Pack listed at a fixed price for a twelve-month contract, but the listing labels its dimension in units without defining what one unit maps to — it may be identities, secrets, or another tracked object. That means the cost of governing a given number of agents cannot be modelled before a sales conversation, and it is why both pricing rows were dropped from this comparison rather than guessed. Agen is priced per governed agent with no prerequisite tier.

Can Agen.co run alongside Entro Security?

Yes, and given how little the two overlap it is the natural shape. Entro keeps finding exposed secrets across your lifecycle, rotating and retiring credentials, and holding the inventory of every non-human identity — none of which we do. Agen governs the action from a layer that holds identity: every agent discovered agentlessly across five surfaces, 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 governing a credential and governing an action?

A credential verdict asks what this identity is allowed to reach. An action verdict asks whether this thing it just tried to do was permitted. The two diverge on ordinary work: an agent with a correctly-scoped credential reads a record it is entitled to read and posts it somewhere it is entitled to post, exceeding no permission and breaking no behavioural baseline — so a credential-scoped control correctly stays quiet, while the question of whether that person's agent should have moved that record has not been asked. Both controls are worth having. Only one of them can answer an auditor who wants to know who authorised a specific action.

How current is this comparison?