Red teams aren’t just for enterprises anymore.

Greywatch deploys AI agents that attack your product 24/7. Successful exploits are shared with your team for fixing.

Talk to the founder →

A 20-minute conversation with a founder. No pitch deck, no SDR.

greywatch-harness — live

$ harness run --target acme-health.com

[08:42:01] mapping surface… 38 endpoints

[08:42:19] probing auth boundaries

[08:43:04] testing /api/patients/:id

[08:43:07] ! IDOR — read any patient record

[08:43:07] ! severity: CRITICAL

[08:43:12] engineer review queued

[09:20:44] ✓ exploit reproduced · fix recommended

Our AI agents have found real vulnerabilities in growing product companies

Confirmed findings from engagements and responsible disclosures.

31companies with confirmed findings, in the last 7 days

743plaintext API keys exposed by a single finding

100%of the criticals we found would have passed a SOC 2 review

Ada Health

Casca

Adra

Empallo

Ada Health

Casca

Adra

Empallo

Freya Voice

Jupid

Freya Voice

Jupid

AI-enabled product development is increasing attack surfaces.

AI writes your code

It ships fast - but gets reviewed for shape, not security. Bandwidth is the gap, not competence.

AI attacks your product

Autonomous agents probe thousands of products 24/7. Prompt injection, exposed APIs, bad packages - they find what you haven't fixed.

A breach is existential

For data-sensitive companies, a breach destroys reputation and trust - the kind of damage a business doesn't recover from.

The next wave is coming

Next-gen AI models will put offensive capabilities in anyone's hands. The time to get ahead of this is now.

The old model

4×

VAPT a year

Your release cadence

4×

ships to production a day

An always-on red team is the only way forward for products that take customer data seriously.

Your red team, as a product.

AI agents continuously attack your product, an engineer verifies every exploit, you get a short list you can act on.

Continuous

Our AI agents test your live product the way an attacker would. Continuously, not once a quarter.

Agent activity

surface re-mapped after deploylive

auth boundaries re-tested4m ago

your last quarterly pentest83 days ago

Reviewed

A security engineer reviews every finding before it reaches you. You get a short list of real issues. Not 1,000 tickets.

Prioritized findings

IDOR on /api/patients/:idCritical

Overprivileged S3 policy on backupsHigh

Missing security headersLow

Reproducible

The exact request that worked, the data it exposed, step-by-step reproduction your engineers can run, and a recommended fix.

Reproduction

$ curl -H "Auth: <user-a>" \

/api/patients/8815

← 200 OK · returns patient 8815

user-a is not patient 8815

A scanner tells you what might be wrong.
We show you exactly how we got in.

Your product, your rules.

Our AI agents have deep safety protocols that ensure they never do something they aren’t explicitly authorised to do. Agents attack. The harness keeps them in bounds.

Greywatch

OverviewFindings1Assets14RulesIntegrationsTeamSettings

Testing paused

First cycle complete · 17 attack paths queued · map still live

Continue testing

acme-health.com / RulesPaused

Rules

What the agents may touch, and when. Enforced by the harness itself · last changed 2h ago · every change logged

Scope & conduct

Test production hosts13 hosts in scope · 1 excluded

Exclude third-party hostspartner-api.acme-health.com stays untouched

Pause after each cycleNothing runs until you press Continue

Never run destructive actionsNo writes, no deletes · count recorded, 1 sample redacted

Only test in an agreed windowOff · the agents run around the clock

Rate limitPer host, enforced by the harness

20 req/s

Deny list · unreachable, not merely discouraged

*.payments.acme-health.com

/api/admin/*

partner-api.acme-health.com third-party

+ add a host, path or method

Upload the assets you want testedSet scope and rulesProvision exactly the access you chooseSee every probe we run

See the platform →

Man + Machine.
Not man vs. machine.

Pure automation finds everything and understands nothing. We built it differently.

Machine

24/7

AI agents attack your surface

→

Coverage

1,000+

candidate findings

→

Human

cyber-security expert finds exploits & prioritizes

→

Signal

real, exploitable findings

The number varies. The bar does not - if an engineer could not exploit it, you never see it.

RL-trained adversarial agentsDynamic allow/deny guardrailsCVE + exploit-chain verificationEvery finding reviewed by a security engineer

Think of it as security engineers backed by an AI research team - not an AI tool pretending to be one.

What it looks like from the other side.

“

We were focussing on our usual product roadmap and didn't think beyond our quarterly VAPT reports. Greywatch proactively found exploitable vulnerabilities our usual vendor never caught, and now we run their continuous security platform on all our assets. Highly recommend working with them!

Adarsh Tadimari

Co-founder & CTO, Plotline

The old model was built for a slower attacker.

3 things the industry still treats as sufficient. When exploitation is measured in hours, none of them hold up.

Automated scanners are noise

A CVE scanner returns 1,000+ findings with no prioritization. Teams stop reading.

Scan results

Outdated jQuery 3.4.1Unranked

Missing X-Frame-OptionsUnranked

+ 997 moreUnranked

Compliance checkmarks are posture, not protection

SOC 2 and HIPAA mean the right policies are documented - not that the product is secure.

Compliance dashboard

SOC 2 ✓HIPAA ✓ISO ✓

IDOR on /api/users/:idUntested

Quarterly VAPT is a snapshot

A VAPT report is true the day it runs, and decays from that moment. By the time you read it, you've shipped 100 more times.

Pentest report

Issued90 days ago

Deploys since360

StatusHistorical document

All 3 assume an attacker who moves in weeks or months. That attacker doesn't exist anymore.

How we compare to scanners, VAPT firms and AI pentest tools →

Built by people who've seen
what happens when security fails.

Rahul and Ajay co-founded Tequity, an applied AI studio that has built 100+ products for startups.
Across those builds, the same pattern kept showing up: products that were fast and well designed, with security left for later.
Greywatch is how they fix that at the source.

Rahul Dharan

Co-founder

Rahul started as an ML engineer and has spent a decade in product security. As co-founder of Tequity, he led security reviews across its 100+ product builds and kept finding the same gaps: broken access control, loose storage rules, servers that trust the client. He designed the attack methodology Greywatch’s AI agents run today.

ML engineeringProduct securityTequity

Ajay Kumar

Co-founder

Ajay is a serial entrepreneur with 12+ years building tech products. He co-founded Tequity with Rahul, founded MonkSpaces.ai, and previously founded Cozee Homes, which was acquired. At Tequity he watched 100+ products ship with the same security gaps. Greywatch is his answer: fix it once, for every founder moving at full speed with no one watching the door.

12+ yearsTequityMonkSpaces.aiCozee Homes

Red teams aren’t just for enterprises anymore.

Our AI agents have found real vulnerabilities in growing product companies

AI-enabled product development is increasing attack surfaces.

Your red team, as a product.

Your product, your rules.

Man + Machine.
Not man vs. machine.

What it looks like from the other side.

The old model was built for a slower attacker.

We write up what we find.

Built by people who've seen
what happens when security fails.

Talk to the founder.

Red teams aren’t just for enterprises anymore.

Our AI agents have found real vulnerabilities in growing product companies

AI-enabled product development is increasing attack surfaces.

Your red team, as a product.

Your product, your rules.

Man + Machine.Not man vs. machine.

What it looks like from the other side.

The old model was built for a slower attacker.

We write up what we find.

Built by people who've seenwhat happens when security fails.

Talk to the founder.

Man + Machine.
Not man vs. machine.

Built by people who've seen
what happens when security fails.