Machine speed is not enough. Judgment is the other half.

Q: Is there a meaningful speed difference?

The harness runs 24/7; the human review step adds some latency before you receive a finding. We think the trade-off is straightforwardly worth it - you get findings you can act on immediately, not a queue you need to triage first. If speed of raw output matters more than signal quality, we are probably not the right fit, and that is worth knowing upfront.

AI tools probe at scale and return findings fast. What they cannot do: decide what matters to your business, or separate a critical breach from noise. We add that.

Talk to the founder →

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

Pure-AI agent output

Fast.

Everything the pattern matched - at volume, without context. A triage problem delivered at speed.

Greywatch output

Signal.

Machine speed on attack. Human judgment on output. 3-5 findings that are real, prioritized, and ready to fix.

No strawman

What autonomous AI offensive tools do well

Worth being honest: the pure-AI model has real strengths. This is a straight read.

Machine speed

Autonomous agents probe at a pace no human team can match. They scale to a surface area a quarterly engagement cycle cannot cover.

Continuous coverage

The category solved the quarterly pentest problem. Always-on attack coverage is a genuine step forward over a point-in-time engagement.

Known vulnerability classes

IDOR, injection, broken auth, exposed secrets - autonomous agents catch these faster than a human engagement. Real improvement in the right attack surface.

The gap

Where machine judgment runs out

The gap is not speed. It is understanding. A machine finds what its training is built to find. It cannot read your product and reason about what a breach of this business would cost.

Severity without context is noise

An autonomous agent flags an IDOR. It cannot tell you whether that IDOR exposes another user’s profile picture or their full transaction history. CVSS 9.8 in a library you never call in production is not your most urgent problem. The machine does not know the difference.

Volume without a filter is a different kind of triage problem

At machine speed, without a human filter, everything that fires gets delivered. The agent cannot decide “this one matters, this one does not” - it can only flag what matches a pattern. For a startup without a dedicated security function, that is noise wearing a different hat than a scanner.

No business context in the output

The machine cannot read your revenue model, your customer data structure, your regulatory exposure, or your upcoming launch. An engineer who has spent time attacking your product can. That context is what turns a finding from “item on a list” to “fix this before Thursday.”

How it works

Machine speed on attack. Human judgment on output.

Greywatch is not a slower AI agent. The agents are in the stack. What we add is the engineer who makes the output useful.

Continuous

AI agents continuously test your live product the way an attacker would, at machine speed. Always on, never unsupervised.

Reviewed

Every candidate is filtered by a security engineer first. 3–5 real, prioritized issues - not a raw feed to triage.

Reproduction, as delivered

$ curl -H "Auth: <user-a>" /api/records/8815

← 200 OK · returns record 8815

user-a is not the record owner. Count recorded, 1 sample redacted, nothing extracted.

Reproducible

The exact request that worked, the data exposed, step-by-step reproduction, and a recommended fix. Not “this pattern suggests a vulnerability” - “here is the request that worked.”

Learn more: how it works →

Side by side

Pure-AI agent vs. Greywatch

	Pure-AI offensive agent	Greywatch
Attack speed	Machine speed	Machine speed (same class of agents)
Human review	None	Security engineer reviews every finding
Output	All flagged findings at volume	3–5 proven, prioritized findings
Business context	None - pattern-matched only	Engineer contextualizes by product and risk
Proof of exploitability	Finding + severity score	Exact request, data exposed, reproduction steps
Fix guidance	Generic or none	Fix written by the engineer who exploited it
Continuous?	Yes	Yes (24/7)

Right fit

Who each model is built for

Pure-AI offensive agent

Security teams with in-house capacity to act as the judgment layer - to take raw machine output and filter, prioritize, and contextualize it for your product. If you can build that layer yourself, a pure-AI agent is a powerful feed into it.

Greywatch

Series A-C healthtech, fintech, and B2B SaaS teams without a dedicated security function. You pay for attack coverage; you get back a proven, business-prioritized finding - not a triage problem to build capacity around.

From our customers

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

Common questions

Questions about AI-assisted offensive testing

Does Greywatch use AI at all, or is it all human?

Both. The AI agents do the attack work - probing at machine speed, running continuously, covering your product attack surface. The security engineer reviews every result before it reaches you. Neither half works as well alone.

Is there a meaningful speed difference?

The harness runs 24/7; the human review step adds latency before you receive a finding. The trade-off: you get findings you can act on immediately, not a queue to triage first. If raw output speed matters more than signal quality, we are probably not the right fit - and that is worth knowing upfront.

Can you attack my product the way a real adversary would?

Yes. We have no source code access, no inside knowledge. The agents probe your product from the outside - the same surface a real attacker sees. That is intentional. We want to know what is actually reachable, not what static analysis flags in your repo.

Do you cover API security specifically?

Yes. API endpoints, broken object-level authorization, exposed admin surfaces, and auth logic are common attack paths for the products we test. The agents are built around how modern startups ship - REST APIs, third-party integrations, webhook endpoints.