A scanner finds everything. We find what matters.

Q: How many findings do you typically surface per month?

It varies by product and how aggressively you ship. The point is not the number - it is that every finding we deliver is real and proven. We would rather hand you 3 confirmed critical vulnerabilities than 300 maybes.

Scanners aggregate from dozens of tools and return 1,000-item lists sorted by CVSS. We run AI agents with a security engineer behind every finding. You get 3–5 proven priorities.

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

Scanner output

1,000+

Flagged items. Sorted by CVSS, not business risk. Most theoretical, many unreachable.

Greywatch output

3–5

Proven, prioritized, business-critical findings. All real. All actionable this week.

No strawman

What the scanner model gets right

Consolidated AppSec solves a real coordination problem. This is a straight read.

Broad coverage

SAST, DAST, SCA, secrets, containers - all in one dashboard. Real coordination value for teams drowning in separate tool outputs.

Continuous scanning

Scan on commit, scan on schedule. The category solved the "quarterly pentest" cadence problem. That is a genuine step forward.

Compliance footprint

For teams with compliance-driven scan requirements (SBOM, SAST on CI), a consolidated tool checks boxes cheaply. A real use case for the right context.

The gap

The structural problem downstream

Every scanner flags. None of them prove. That is the model’s structural limit, and more tools feeding the same dashboard does not fix it.

Alert fatigue is a design output, not a bug

An engineer opens the first finding, traces the code path, discovers it is theoretical. Item 2: same result. By item 12, they stop trusting the list. This is the scanner model at work - not a misconfiguration, the output.

CVSS is not business risk

Scanners sort by severity score, not by what a breach of your specific product would cost. A 9.8 in a library you never invoke is not your most urgent problem. A 6.2 in your payment flow is. The scanner cannot tell the difference.

No proof, no fix confidence

A scanner flags a pattern. It cannot show you the request that worked, the data it exposed, or reproduce the issue step-by-step. Your engineers fix something they cannot verify was real - or they deprioritize it and move on.

How it works

1 framework. 3 motions.

Every engagement runs the same loop. Nothing leaves without human validation.

Continuous

AI agents continuously test your live product the way an attacker would, at machine speed. Not a scheduled scan against a signature list.

Reviewed

Every candidate is filtered by a security engineer first. A few real, prioritized issues - not 1,000 low-signal tickets.

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. The sharpest line between us and a list of theoretical findings.

Learn more: how it works →

Side by side

Scanner model vs. Greywatch

	Scanner / consolidated AppSec	Greywatch
How it finds issues	Automated scanning + aggregation	AI agents probing your product
Human review	None	Security engineer reviews every finding
Output volume	100s–1,000s of flagged items	3–5 proven, prioritized findings
Proof of exploitability	CVSS score + description	Exact request, data exposed, reproduction steps
Continuous?	Yes (scan on commit / schedule)	Yes (AI agents, 24/7)
Fix guidance	Generic remediation advice	Fix written by the engineer who exploited it
False positives	High	Near zero (filtered before delivery)

Right fit

Who each model is built for

Scanner / consolidated AppSec

Compliance-driven teams with a dedicated security function to absorb and triage a noisy feed. Large eng orgs with SBOM or SAST requirements baked into their CI pipeline. If you have the in-house capacity to filter, a scanner is a cheap feed.

Greywatch

Series A-C healthtech, fintech, and B2B SaaS teams shipping fast, holding real customer data, without a dedicated security function. You want findings you can act on immediately - not a dashboard your engineers stop trusting in 90 days.

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 switching from scanners

Do you integrate with our existing scanner output?

We do not. We run an independent external attack from the outside - no source code access, no scanner feed. That is intentional; we want to see what an adversary sees, not what your static analysis sees.

Can Greywatch replace our AppSec tooling entirely?

For most Series A-C companies without a dedicated security function, yes. If you have compliance requirements calling for specific scan types (SBOM, secrets on CI), a narrow tool makes sense alongside us. We do not try to be everything - we try to find what actually gets you breached.

How many findings do you typically surface per month?

It varies by product and shipping cadence. The point is not the number - every finding we deliver is real and proven. We would rather hand you 3 confirmed critical vulnerabilities than 300 maybes.

What does “continuous” actually mean?

The harness runs against your product every day, not on a quarterly cycle. New code you ship gets tested. New attack patterns we develop run against your existing surface. You ship multiple times a day - we test at that cadence.

Get in touch

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