Governing the
agentic enterprise

The Governance Gap

You can't govern what you can't see.

What this is

The AI you already run, on the record

Organisations run more AI than they can name — agents, LLMs, MCP servers, each holding credentials and acting on someone's behalf. Bairav answers four questions about all of them.

What AI is being used

Discovered entities land in an intake queue for review. Nothing enters the governed environment unclassified.

What can it access

Each agent carries the tools it can invoke and the data sources it reaches. PII access is flagged, not inferred.

What actions it takes

Runtime telemetry per agent: chat sessions, tool executions, model calls, and every database read and write.

Whether those are safe

Policy decides at the moment of action — allow, block, hold for approval, flag, or notify the governance team.

The analogy

If an AI agent is an employee, Bairav is the company's governance function — rules, permissions, approvals, and audit records — watching and controlling its actions.

The core flow

From discovery to evidence

Six stages, one system. Each stage feeds the next: what discovery finds becomes what assessment scores, what policy governs, and what the ledger can later prove.

Discover

Every AI entity in the environment is found and queued — agents, LLMs, IDEs with AI features, MCP servers.

Assess

Four weighted factors produce a risk score that recomputes live while a reviewer classifies the entity.

Govern

Rules attach to agents: block, approval gate, flag or notify. The rule shows its scope before you save it.

Monitor

Runtime telemetry — sessions, tool executions, model calls, and a graph of what each agent is wired to.

Enforce

Every allow, block, flag and notify decision, made at the policy layer and streamed to a live feed.

Audit

Hash-chained, append-only entries with chain verification and CSV export. Evidence, not just visibility.

Discover · Dashboard

The landing screen answers one question

Is anything wrong right now? The posture banner answers it before any supporting detail loads. Everything below it exists to explain that verdict.

1

Posture banner

One colour, one sentence, at the top of the page. Green reads Posture: Healthy — all rules clear. It changes the moment that stops being true.

2

Headline stats and the 24-hour delta

Agents in production against total discovered, actions gated today, approvals pending against a 24-hour SLA — plus a delta strip for what changed while you were away.

3

Live enforcement feed

The most recent decisions, each showing the outcome, the agent, and how long ago it happened. A condensed preview of the full Enforcement screen.

The operating principle

Verdict first, action second, audit trail third.

Applied the same way, every time

Coverage quality

The product tells you when it isn't in control

Most dashboards report a coverage percentage and stop. Bairav splits coverage by strength — a flagged agent isn't protected the way a blocked one is.

1

Enforcing, monitor-only, ungoverned

One stacked bar, three honest categories. Monitor-only coverage — flag and notify — is weaker than an enforcing policy, and the caption says so outright.

2

Build-type mix

How agents were constructed, across Gartner's four agent-construction techniques: built, blended, embedded, and bring-your-own-agent. The panel notes that governance is organised by risk tier, not build type.

3

Fleet freshness and the audit trail

Connectivity flips automatically from real heartbeats rather than manual status entry, next to a running count of hash-chained, append-only ledger entries and a link straight into chain verification.

How the product is organised

Three pillars

Discover

One verdict for the whole environment, the queue that routes what just showed up, and the registry behind both — every agent, its scores, and its rules.

Dashboard · Intake Queue · Agent Registry

Open Discover

Govern

The rulebook, the live feed of every decision it makes, and the human call when the rulebook isn't enough — plus containment that stops an agent without deleting it.

Enforcement · Quarantine · Policies · Approvals

Open Govern

Prove

What agents actually sent, received and touched at runtime, everywhere that data could end up, and the tamper-evident record that outlives both.

Traffic · Data Flow · Audit Ledger

Open Prove

See it against your own fleet

A working session on your environment: what discovery finds, how it scores, and which agents run ungoverned. Already in a regulated industry? Ask about the Lighthouse Program instead.

Book a Demo

Product

No agent skips a step

Every AI entity in Bairav goes through the same sequence, in the same order, whether it's a supplier-facing chatbot or an internal automation script nobody remembers approving. Nothing gets fast-tracked into production because it seemed low-risk at a glance.

DiscoverAssessGovernMonitorEnforceAudit

Discover

Assess

Govern

Monitor

Enforce

Audit

Intake Queue showing newly discovered agents grouped by risk tier, each with a proposed classification and priority rank.

Discover — Intake QueueIllustrative data from a demo environment.Swipe the panel to read it.

You can't score what you haven't found

Discovery doesn't wait for someone to register an agent — it finds AI activity across the estate and routes it into an intake queue instead of straight into "operating." The queue sorts by risk tier so the entities most worth a human's attention surface first, not last.

The operating principle

Verdict first, action second, audit trail third.

Applied the same way, every time

Walk the lifecycle on your own environment

We run discovery against a scoped slice of your estate and show you the intake queue it produces.

Book a Demo

Pillar — Discover

The one page that has to be right before anything else does

Discover is where the day starts and ends for whoever is accountable for the fleet — not a reporting layer to check when there's time, but the place that says, in one look, whether today is a normal day.

DashboardIntake QueueAgent RegistryAgent Detail

Dashboard

A status, not a summary

Most dashboards make you assemble the verdict from six charts. This one states it first — healthy, or not — and everything below just justifies that line.

Status

Healthy — every rule enforcing, nothing overdue

Agents in production

12

of everything discovered, how much has actually been reviewed in

Gated actions today

0

held for a human, not waved through

Pending approvals

0

against a 24-hour service commitment, not left open-ended

Open quarantines

0

barred from operating, not just flagged

Governance Posture dashboard showing the Healthy status banner, four headline stat cards, the since-you-last-looked delta strip, needs-attention panel, and live enforcement feed. — **Discover — Dashboard**Illustrative data from a demo environment.Swipe the panel to read it.

Coverage quality

“Covered” is a claim. “Enforcing” is a fact.

A fleet can be fully accounted for and still unprotected. Coverage here splits into three honest states — enforcing, monitor-only, ungoverned — not one percentage nobody can back up.

4

Enforcing

3

Monitor-only

5

Ungoverned

Two different claims

A 100%-covered fleet and a 100%-enforced fleet are different claims. This page is built to keep you from confusing them.

The operating principle

Verdict first, action second, audit trail third.

Applied the same way, every time

Intake Queue

Found is not the same as trusted

Showing up in the queue means someone has to look, nothing more. Entities rank by risk and priority, and a proposed tier and score sit on every row — not a separate screen.

High

3

Medium

6

Low

4

No silent default

The queue has no silent default. An entity that nobody looks at stays exactly where it is — waiting — not quietly promoted to approved because time passed.

How the score is built

Every proposed classification runs through the same four weighted factors — data sensitivity, permissions, autonomy, external exposure — and changing any one of them moves the composite score immediately, before a decision is saved. Nobody has to submit a form and wait to find out if they just approved something riskier than they meant to.

Data sensitivity — 30%

How sensitive the data this entity can reach actually is — the heaviest single input, because what an agent can touch is what a breach is made of.

Accountability is a required field

No entity clears review without a named accountable owner attached. Not a team, not a distribution list — a person who can be asked about it later.

Agent Registry

Everyone who's made it past the door

Every agent that's cleared intake lives here, sorted by risk, carrying the fields that drive a decision — not the ones that just look complete on a slide.

Total agents

12

across all risk tiers

Ungoverned

5

no enabled policy attached

High-risk

0

critical + high tiers

Pending approvals

0

awaiting human decision

Agent Registry full list, filterable by risk tier, build type, lifecycle, status and connectivity, showing risk and trust scores per agent. — **Discover — Agent Registry**Illustrative data from a demo environment.Swipe the panel to read it.

What a record carries

Identity

Name, ID, and enough of a fingerprint to tell two similarly-named agents apart.

Status

Where it sits right now: approved, pending, suspended, blocked.

Risk

Tier and the number behind it, so "medium" near the boundary reads as the edge case it is.

Trust

Separate from risk on purpose, built from behaviour over time, never rewritten after the fact.

Build type

Built in-house, blended, embedded, or bring-your-own-agent.

Coverage

Enforcing, monitor-only, or ungoverned — the same three states as the dashboard.

Owner

A named human, always. Not a team inbox, not "TBD."

Filters cut this list by risk, build type, lifecycle, status, and connectivity, with a live count of what's actually in view. The agents worth reading closely are the ones tagged ungoverned — that's the exact population the coverage split upstream was warning about.

Agent Detail

Three numbers. None of them are the same question.

Confidence, Risk, and Trust get treated as one score with three names. They're not — collapsing them would hide which one is actually doing the work.

70/ 100

Confidence

Capped, deliberately

How much the platform genuinely knows about this agent. It stops short of 100 on purpose — certainty about something discovered, not built, is a claim we won't make.

48/ 100

Risk

Recomputed, not fixed

The same four-factor weighting used at intake — sensitivity, permissions, autonomy, exposure — kept live rather than frozen at the moment of approval.

11/ 100

Trust

Earned, and only earned

Built from observed behaviour over time. It goes up slowly and it does not get quietly reset because last week looked fine.

Autonomy & controls

Oversight level, action class, observed autonomy, whether an approval gate is attached, kill-switch state: the specific levers that cap how far this agent can act without a human.

Supervised Action class: write Approval gate: yes

Capabilities & data access

The tools it can call and data it can reach, recorded as fact, not assumed from a role. PII access gets its own flag, never buried in a scope list.

read:database send:email write:database PII data

See what your own registry actually says

We run discovery against a slice of your real environment and hand you the registry, coverage split, and the agents running ungoverned — not a demo tenant's version.

Book a Demo

Pillar — Govern

The point where "we noticed it" becomes "we decided about it"

Govern is three different jobs kept in three different rooms on purpose: the rulebook that says what's allowed, the live feed of every decision it makes, and the place a human makes the call when the rulebook alone isn't enough.

EnforcementQuarantinePoliciesApprovals

Enforcement

A feed of decisions, not a feed of noise

Every allow, block, flag, and notify decision the policy layer makes, in real time, attributed to the rule that triggered it. In the last 24 hours: 68 blocked, 5 flagged, 12 allowed — a live ratio.

Decisions today

85

since midnight

Blocked

94

last 7 days

Median latency

0 ms

policy evaluation

Enforcement screen showing decision stats and a live feed of blocked, flagged, and notified actions, each with the policy and reason that triggered it. — **Govern — Enforcement**Illustrative data from a demo environment.Swipe the panel to read it.

Quarantine

Stopped, and still on the record

Three states take an agent out of operation — rejected, suspended, blocked for cause — each preserving identity and full history. Containment isn't deletion.

Quarantine screen showing rejected-at-intake, suspended, and blocked-for-cause counts, with a table of quarantined agents and their reactivation paths. — **Govern — Quarantine**Illustrative data from a demo environment.Swipe the panel to read it.

State	How it happens	Path back
Rejected at intake	A reviewer declined it during classification.	Re-review
Suspended	An operator paused it from the agent's own record.	One-click reactivate
Blocked for cause	Stopped because of something it actually did.	Re-review only — no one-click return

Quarantine vs. block

Quarantine acts on the entity; block acts on a single action. Confuse them and you stop the wrong thing — or the right thing in a way that won't hold up.

Quarantine vs. suspend

Quarantine is the state; suspend is the action that puts something into it, taken from the agent's own page. One is a place, the other is a decision.

Nothing quarantined disappears

Nothing quarantined is ever deleted, because a regulator asking what happened to a specific agent needs an agent still there to ask about.

Operating constraint

Scope is honest — the platform never claims control it doesn't have.

No rule outruns what it can enforce

Policies

Four instruments. Not four flavours of the same one.

Block, hold, flag, or notify — each does a different job; we won't blur them into one severity dial. A rule shows how many agents it affects before you save it.

BlockApproval gateFlagNotify

Block

Stops the action outright. The only instrument the coverage reporting counts as real enforcement.

22 rulesenforcing

Approval gate

Holds for a named human. The action pauses; it doesn't proceed until someone decides.

10 rulesenforcing

Flag

Allows, but marks for review. Useful, and honestly weaker than blocking — the product doesn't pretend otherwise.

5 rulesmonitor-only

Notify

Allows, and tells the governance team. Awareness without interruption. Counted as monitor-only, not enforcement.

1 rulemonitor-only

Conditions combine with AND/OR logic, and scope is shown before it's saved — a rule never quietly governs more agents than the person writing it intended.

Approvals

Where a person, not a policy, makes the final call

Anything a policy holds lands here, waiting on one named approver, not a timeout that resolves itself. This queue records every judgment call the system declined to make alone.

Single approver by design

Approval today is single-approver by design — multi-stakeholder sign-off is roadmap, not shipped. A request cancelled because its policy was removed is recorded as cancelled, not folded into "approved."

Bring us the rule you can't currently enforce

The most useful first conversation is a policy you already want and can't apply today. We'll build it live and show you exactly which agents it would catch.

Book a Demo

Pillar — Prove

What the registry says should happen, and what actually did

The registry records what exists. Policy records what's allowed. Prove is the only one of the three that records what happened — every request, every query, every path data could take — in real time, not in hindsight.

TrafficData FlowAudit Ledger

Traffic

The individual request, not the rolled-up number

Two views, two different problems: model calls and data access. Aggregated metrics show the shape of activity; Traffic shows what happened in one specific request.

LLM calls

Requests

69

all time

Allowed

44

passed guardrails

Blocked

25

by guardrails / kill-switch

Avg latency

2,245 ms

per request

Tokens

634,754

630,728 in · 4,026 out

Traffic screen showing LLM call stats, a throughput chart of allowed versus blocked requests over the last 24 hours, and a per-agent breakdown of models and token use. — **Prove — Traffic**Illustrative data from a demo environment.Swipe the panel to read it.

Data access

Queries

543

last 24h

Writes

63

insert / update / delete

Tables

15

distinct touched

Avg time

3 ms

per query

Slowest

52 ms

single query

Data access screen showing query and write volume, distinct tables touched, average and slowest query time, and per-hour query volume. — **Prove — Traffic (Data access)**Illustrative data from a demo environment.Swipe the panel to read it.

Data Flow

What's reachable, not what's declared

An agent's registered capabilities say what it's allowed to touch. Data Flow shows what actually happened — every source pulled from, every tool called, everywhere the result could go.

Places data is stored

16

last 7 days

Paths to critical data

0

last 7 days

Unprotected paths

31

no rule can stop them

Ways data can leave

9

last 7 days

Issues found

0

last 7 days

Data Flow map showing places data is stored, unprotected paths, and per-agent source/tool/destination counts, ordered by what needs attention first. — **Prove — Data Flow**Illustrative data from a demo environment.Swipe the panel to read it.

Declared versus reachable

A capability the registry lists as read-only doesn't guarantee that data can't reach an uncontrolled destination three hops later. This page exists to catch that gap.

Agent Audit Ledger

Evidence, not just visibility

Actions recorded

69 of 69

matching current filters

Blocked

9

denied at guardrail

Awaiting review

16

pending / quarantined

Critical-risk actions

0

high-scrutiny events

Agent Audit Ledger with stat cards, filters, a chain-verified indicator, a CSV export control, and an event table listing timestamp, event ID, agent, owner, action, target system, risk, decision, approval and outcome. — **Agent Audit Ledger**Illustrative data from a demo environment.Swipe the panel to read it.

What gets recorded

Nine questions, answered for every single action

An audit record is only useful if it survives contact with someone who was not there. These are the fields the ledger writes for every governed agent action.

Column	What it establishes
Timestamp	When the action occurred.
Event ID	A unique ledger reference that can be cited outside the product.
Agent	Which agent performed the action.
Owner	The accountable person on record — set as a required field at intake, not inferred later.
Action	What was attempted, in plain language rather than an opaque event code.
Target system	Which system or database was touched.
Risk	The risk tier at the time of the action, not the tier the agent carries today.
Decision	Allow or deny.
Approval	Whether human approval was required, and whether it was given.
Outcome	Executed or blocked — what actually happened, as distinct from what was decided.

Why decision and outcome are kept separate

"Policy denied it" and "it didn't happen" are two different claims. An auditor is right to treat them differently — conflating them becomes a large problem under a follow-up question.

Nobody edits history, including us

Entries chain to each other rather than sitting as editable table rows — verification reports how many were checked and confirmed intact: 69 of 69, last run. That's load-bearing, not a nice-to-have.

Evidence that leaves the product

Filtered results export to CSV, so the record can go to an auditor, a regulator, or an internal investigation without a single screenshot in the chain of custody.

Filterable by the questions people ask

Search by agent, owner, action or event ID, with independent filters for agent, owner, risk, decision and outcome — the shape of a real audit request.

How the product is ordered

Verdict first, action second, audit trail third.

Applied the same way, every time

Watch a decision happen

In a live session we trigger a policy against a real agent and follow the decision through the enforcement feed — evidence the moment it lands in the Audit Ledger below.

Book a Demo

Evidence

Built against the frameworks your compliance team already answers to

NIST AI RMF, the EU AI Act, ISO/IEC 42001, the OWASP Agentic Top 10 — Bairav's registry, policy layer and evidence model are designed against these frameworks directly, not bolted on afterward as a separate compliance exercise.

NIST AI RMFEU AI ActISO/IEC 42001OWASP Agentic Top 10

Why the ledger is last, not optional

Verdict first, action second, audit trail third.

Applied the same way, every time

Framework mapping

Where this lands against the frameworks you already report to

Bairav's evidence model is built against reference frameworks your compliance team already answers to — the ledger, registry, and policy layer are the concrete answers underneath those abstract requirements.

NIST AI RMF 1.0

Four functions

Expects: Govern, map, measure and manage AI risk on an ongoing basis.
Bairav operationalizes it as: Named policies with an enforcement state, agent discovery, four-factor risk scoring, and block/approve/flag/notify actions tied to an accountable owner.

EU AI Act

Deployer obligations

Expects: Logging, human oversight, documented risk management, and transparency about AI in use.
Bairav operationalizes it as: An append-only action ledger, approval gates that pause high-impact actions for a named reviewer, and a registry that includes bring-your-own and embedded agents.

ISO/IEC 42001

AI management system

Expects: Named accountability, documented operational controls, and ongoing performance evaluation.
Bairav operationalizes it as: A required owner and department on every agent, policies as the documented control set, and continuous coverage, intervention and SLA tracking.

OWASP Agentic Top 10

Applied at classification

Expects: Named agentic risks — goal-hijack, PII exposure, excessive agency — assessed per entity.
Bairav operationalizes it as: Findings marked on the agent's record at classification, visible to whoever signs off the approval.

Mapping is not certification

Bairav's audit and control model is designed against NIST AI RMF, the EU AI Act, and ISO/IEC 42001 — a design claim, not an attestation. We don't claim SOC 2 or ISO 42001 certification, and neither should anyone who conflates the two.

Bring your auditor to the demo

The most useful session is the one where someone who will have to defend this asks the questions.

Book a Demo

Platform

A closed loop, not a shelf of point tools

Most of this category is either detection with no stop attached, or a dashboard with no decision attached. Bairav closes the loop instead: discovery feeds scoring, scoring feeds policy, policy feeds enforcement, and the record feeds back into discovery — one system, no gap where an incident has time to happen.

NHI lifecycleABAC / RBACMulti-mechanism enforcementMCP gateway

Why the loop has to close

A tool that only detects hands you a finding. A dashboard that only monitors hands you a chart with no button to press. That gap — "we saw it" vs "we stopped it" — is where incidents live.

Non-human identity

Agents are not users wearing a costume

Extending your identity system to cover agents gets you no real lifecycle, no clear owner, no concept of what it's allowed to invoke. Bairav treats non-human identity as its own class — discovered, classified, registered, governed, contained, evidenced.

1 · Discovered

Has an identity and a fingerprint. Counted as ungoverned until it has a rule.

2 · Classified

A reviewer sets sensitivity, capabilities and an accountable owner. Risk recomputes live.

3 · Registered

Enters the governed system of record with an agent ID and three independent scores.

4 · Governed

Rules attach — block, approval gate, flag or notify — reported by enforcement strength.

5 · Contained

Rejected, suspended, or blocked for cause. Identity and history persist; only operation stops.

6 · Evidenced

Every action, decision and approval, retained in an append-only ledger.

Enforcement mechanisms

More than one way to enforce, on purpose

A control plane with one integration point has one point of failure. Bairav supports several enforcement paths, so the mechanism matches the deployment — not the other way round.

Sidecar / proxy

Enforcement sits in the request path, alongside the workload. Fits container and service-mesh environments where the platform team already owns that pattern.

DNS override

Redirects model and API traffic to the enforcement point without touching application code. The path for agents nobody can safely modify — including the ones nobody formally procured.

SDK

Direct integration for teams building their own agents who want the decision made inside the application, not in front of it.

MCP gateway as a first-class enforcement point

MCP servers are where agents acquire new capability, so the gateway is a first-class enforcement point, not just a discovered artifact — it has to sit between the agent and the tool.

ABAC and RBAC, together

Role-based access answers "who is this." Attribute-based access answers "what's true right now." Agents need both — a role, plus the attributes to say whether this action should be allowed.

Operating constraint

Scope is honest — the platform never claims control it doesn't have.

No rule outruns what it can enforce

One identity, start to finish

The loop, in interfaces

Discovery

Finds the entity and proposes a classification and provisional risk score.

Assessment

Scores it on four factors, checks it against OWASP, assigns an accountable owner.

Policy

Attaches enforceable rules with a declared scope and enforcement state.

Enforcement

Decides — allow, block, flag or notify — and records the reason and latency.

Audit

Writes an append-only, hash-chained entry: decision, approval and outcome.

Because the same identity carries through every stage, a question asked at the end — why was this allowed? — resolves backwards to the rule, the classification, and the person who approved it, without correlating across three tools and two teams.

Administration

Gateway config, roles and platform settings

An Administer pillar sits underneath everything above: gateway configuration, role-based administration, and platform settings for the team running Bairav day to day.

Disclosure

No certification claims. No named-competitor comparisons. No customer counts, deal sizes or agent volumes. Every number on this site comes from a demo environment and is labelled as such.

Architecture review, not a slide deck

A working session with your platform and security engineers on enforcement placement, identity model and evidence retention.

Book a Demo

Plans

Three tiers. No number invented for a deployment we haven't seen yet.

Governance spend is easiest to defend when it starts with a number nobody had before. Discovery produces that number. Govern and Command are for what you do once you have it.

DiscoveryGovernCommand

Discovery

Entry tier

Finding out what's actually running. Discovery answers the first question — what AI is being used here — without asking you to commit to an enforcement programme first.

Discovery of AI entities: agents, LLMs, IDEs with AI features, MCP servers
Intake queue with proposed classification and provisional risk scoring
Agent registry with three-axis scoring
Coverage quality reporting, including ungoverned agents
Multi-tenant with logical isolation
Capped at 50 agents per client

Start with discovery

Govern

Core platform

Turning findings into enforceable rules. Everything in Discovery, plus the policy layer, the human-in-the-loop queue, and the containment states that let you stop an agent without destroying its history.

Everything in Discovery, without the agent cap
Policies: block, approval gate, flag and notify, with AND/OR conditions
Approval gates and the human-in-the-loop queue
Quarantine: rejected at intake, suspended, blocked for cause
Runtime monitoring, traffic and the live enforcement feed
Audit ledger with chain verification and CSV export

Talk to us

Command

Enterprise scale

Estates where enforcement placement, tenancy, and evidence retention are architecture decisions — and where more than one team has to be able to answer for the fleet.

Everything in Govern
Enforcement across sidecar/proxy, DNS-override and SDK paths
MCP gateway as a managed enforcement point
ABAC and RBAC policy administration at scale
Deployment and tenancy options for regulated environments
Named support and architecture review

Talk to us

On top of the tiers

Two things that scale differently

Consumption layer

Enforcement and telemetry volume scale with what your agents do, not how many seats you've bought. A small team running a large, active fleet shouldn't be penalised for doing the job well.

Compliance evidence packages

Structured evidence output for a specific framework or audit cycle, pulled from the ledger, bought when you need it. An audit is an event, not a subscription.

Why there's no price on this page

Estate size, enforcement mechanism, tenancy, and retention move the number more than the tier name. A number posted here would be wrong for almost everyone — you'll have a real one after the first call.

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Get a number for your estate

Tell us roughly how many AI entities you're running — usually the real figure is higher. Already in a regulated industry? Ask about the Lighthouse Program instead.

Book a Demo

Partner

Help shape the product. Get the terms of an early customer, not a beta tester.

A small number of organisations get direct influence over what Bairav builds next. In exchange, we ask for something more valuable than a logo: a real fleet, a real auditor, and a real objection when something doesn't hold up.

Regulated industriesReduced ACVReference customer

What you get

Reduced ACV for the program term. Direct access to the people building the product, not a success manager. Roadmap influence where your gap is real — an honest answer when it isn't.

What we ask

A real environment, not a sandbox. Access to whoever will actually defend the governance model — security, governance, platform engineering, whoever signs off. Permission to name you as a reference.

Who this fits

Security, AI governance, and platform engineering leaders already running agentic AI in production — particularly in regulated industries, where the question isn't whether agents get governed, but who proves it first.

The governance gap

You can't govern what you can't see.

EU AI Act · Aug 2, 2026

Other ways in

If the design-partner programme isn't the right shape

Pilot to contract: A scoped, time-boxed pilot with success criteria and commercial terms agreed before it starts, so it ends in a decision instead of another pilot.
Azure-native deployment: For teams whose security posture and procurement path are already settled around Azure and don't want to reopen those conversations to adopt a governance layer.
Systems integrator partnership: For programs already being delivered through an SI. We work through the integrator on deployment and enablement, not around them.

Apply to the Lighthouse Program

Tell us what you're running, what you're being asked to prove, and who is asking. We'll tell you honestly whether we're the right fit yet.

Book a Demo

About

Governance wasn't missing a feature. It was missing a discipline.

Every other page on this site describes what Bairav does. This one is about why it had to exist: enterprises already run more AI agents than anyone can name, with no consistent answer for what those agents are allowed to do or what they've actually done. Bairav was built to make that answer available on demand, not reconstructed after an incident.

Why Bairav

Three things that were true before there was a product

None of this started as a market thesis. It started as three separate observations that turned out to be the same problem.

1

The gap was already there

Organisations already run more AI than they can name — agents, LLMs, MCP servers, each holding credentials and acting on someone's behalf. That came from real environments, not a projected trend.

2

The tools didn't fit the problem

What existed treated agent oversight as a feature bolted on — a logging add-on, or a compliance checkbox inside an identity system. Neither treated it as its own discipline.

3

It had to be a control plane, not a dashboard

Discovery, scoring, enforcement, and evidence had to run as one connected system, not four tools compared after the fact — otherwise the coverage number is a claim nobody can back up.

The operating principle

Verdict first, action second, audit trail third.

Applied the same way, every time

Why Us

What's structurally different

Not a longer feature list — a different shape underneath it.

One system, not four

Discovery, classification, policy enforcement and evidence run as one connected system rather than four disconnected tools compared after the fact.

Coverage, reported honestly

Coverage is reported in three states — enforcing, monitor-only, ungoverned — not collapsed into a single number that hides which agents are actually protected.

Non-human identity, its own lifecycle

Agents get their own six-stage lifecycle — discovered, classified, registered, governed, contained, evidenced — instead of being retrofitted onto systems built for people.

Evidence built for regulators from day one

The audit ledger is append-only and tamper-evident from its first entry, not assembled under pressure before an audit.

Founder

The person accountable for this record

Chandrasekhar Chandragiri

Founder & CEOFounded 2026

Chandrasekhar has spent his career building enterprise technology for organisations where getting security and compliance wrong isn't an option. He founded Bairav after watching that same discipline missing entirely from how companies were rolling out AI agents — treated as an afterthought rather than a first-class control problem.

In his words

As Chandrasekhar puts it: "If you can't produce the evidence on demand, you don't actually have governance — you have a hope."

See it against your own fleet

A working session on your environment: what discovery finds, how it scores, and which agents are running without an enforcing rule.

Book a Demo

Contact

A working session, not a slide deck

We'd rather run discovery against a real, scoped slice of your environment and talk about what it finds than walk you through screens you've already seen on this site.

30 minutes

Long enough to run the full lifecycle end to end — discovery, scoring, a policy built live — short enough that you don't need to clear your afternoon.

Bring the sceptic

The session works better with whoever will actually have to defend the governance model afterward — your auditor, your CISO, or the platform engineer who owns the enforcement path. Bring the sceptic, not just the sponsor.

What we'll show

Discovery running against a scoped slice of your estate, the intake queue it produces, coverage split across whatever fleet we find, and one policy built live in the condition builder — not a recorded walkthrough of someone else's data.

What we won't do

Claim a certification we don't hold, invent a customer count, or promise enforcement coverage the product can't actually deliver. That's not a compliance footnote — it's the same operating principle the product itself is built around.

The operating principle

Verdict first, action second, audit trail third.

Applied the same way, every time

Governing theagentic enterprise

The AI you already run, on the record

What AI is being used

What can it access

What actions it takes

Whether those are safe

From discovery to evidence

Discover

Assess

Govern

Monitor

Enforce

Audit

The landing screen answers one question

Posture banner

Headline stats and the 24-hour delta

Live enforcement feed

The product tells you when it isn't in control

Enforcing, monitor-only, ungoverned

Build-type mix

Fleet freshness and the audit trail

Three pillars

Discover

Govern

Prove

See it against your own fleet

No agent skips a step

You can't score what you haven't found

Walk the lifecycle on your own environment

The one page that has to be right before anything else does

A status, not a summary

“Covered” is a claim. “Enforcing” is a fact.

Found is not the same as trusted

How the score is built

Data sensitivity — 30%

Everyone who's made it past the door

What a record carries

Identity

Status

Risk

Trust

Build type

Coverage

Owner

Three numbers. None of them are the same question.

Confidence

Risk

Trust

Autonomy & controls

Capabilities & data access

See what your own registry actually says

The point where "we noticed it" becomes "we decided about it"

A feed of decisions, not a feed of noise

Stopped, and still on the record

Quarantine vs. block

Quarantine vs. suspend

Four instruments. Not four flavours of the same one.

Block

Approval gate

Flag

Notify

Where a person, not a policy, makes the final call

Bring us the rule you can't currently enforce

What the registry says should happen, and what actually did

The individual request, not the rolled-up number

LLM calls

Data access

What's reachable, not what's declared

Evidence, not just visibility

Nine questions, answered for every single action

Nobody edits history, including us

Evidence that leaves the product

Filterable by the questions people ask

Watch a decision happen

Built against the frameworks your compliance team already answers to

Where this lands against the frameworks you already report to

NIST AI RMF 1.0

EU AI Act

ISO/IEC 42001

OWASP Agentic Top 10

Governing the
agentic enterprise