Agents that plan their own work
The model plans the engagement, picks the tool, writes the payload, and rewrites the plan from what it just learned. There is no fixed scan template to outgrow.
AI-native application & infrastructure security
Security agents that find, prove and explain vulnerabilities
ScanSuite runs AI agents across source code, web applications and infrastructure. Each one plans its own work, verifies every finding against the real system, and proves it with an exploit that actually runs — on-premise or in a private cloud.
- AI Native SAST
- AI Pentest
- AI DAST
- Dependency Reachability
AI-Verified Findings
Not another scanner. An agent platform.
Classic scanners produce lists that someone still has to triage. ScanSuite's agents produce conclusions — with the reasoning, the call path and the working proof attached.
44
Tools the pentest agent can call
34
Web attack technique modules
30+
Languages analysed by the code agent
Every finding is re-checked
Every finding is re-checked before it is reported: source-to-sink tracing for code, baseline-versus-payload comparison for web, and for infrastructure a proof of concept that has to run.
Existing scanners become tools
Nessus, OpenVAS, Acunetix, ZAP, Nuclei, Trivy and 30+ more stay in the platform — and are handed to the agent as callable tools it can reach for on its own.
Part One
AI Static Analysis
Scanners flag thousands of lines. Working out which of them an attacker could actually use is the slow part, and it lands on the security team. ScanSuite reads the code the way a reviewer would and reports only what it can prove is reachable.
Selected stage
Reads the codebase
Point it at a repository or upload an archive. Before judging anything it builds context a rule engine never has: which parts of the application are exposed, how data moves between them, and what changed recently. Severity then reflects how the application is actually built.
Which dependency alerts actually matter
A dependency scan flags every advisory that touches the lock files, and someone is expected to work through the list. Three gates, cheapest first, cut it down to the ones worth acting on.
The evidence behind the first gate
Around 130,000 exploited CVEs, held locally
ScanSuite ships a database of vulnerabilities that carry real exploitation evidence — exploited in the wild, a published exploit, or proof-of-concept code. It separates "scored critical" from "actively used", and every lookup happens inside the installation, with no external queries.
What counts as evidence
On the same run, against the same checkout
Each one is enabled per scan.
AI-verified secrets
Detected keys and tokens are re-checked by the model, which filters out sample credentials and rotated test keys before they are reported.
Incremental scanning
Monitored branches and incremental scans re-analyse only the files that changed, so the cost of a re-scan tracks the size of the change.
Deduplicated findings
Findings are matched by vulnerability class, file and parameter across scans and engines. A repeat occurrence reopens the original record.
Part Two
AI Pentest & AI DAST
A penetration test is usually a twice-a-year event, and the report ages quickly. This runs the same work on demand: the scope is one sentence, and a finding is reported only once it has been demonstrated.
The whole configuration is a sentence
No scan profile to tune and no attack tree to build in advance. The scope, credentials where they exist, and the priorities.
“Enumerate services, prioritize SMB and web, and verify exploitable findings with a PoC.”
The operator instructions field — everything else is the agent's decision
Selected phase
Plan
The agent works out the engagement before touching anything: what to try, in what order, against which targets, and the expected outcome of each step. The plan can be read before execution begins.
What makes it practical to run
Works with existing scanners
Nessus, OpenVAS and Acunetix are driven by the agent and their results folded into its reasoning, so existing licences feed one conclusion instead of three separate reports.
Tests past the login page
The agent holds a session, so it reaches the authenticated surface where access control, IDOR and privilege-escalation flaws live.
Stays inside the defined scope
Scope is enforced in code: targets are locked to an allow-list, exploits run sandboxed, and every engagement has a hard budget. An out-of-scope action is refused before it is issued.
Behind these: 44 callable tools, from a full OSINT sweep to a single crafted HTTP request, including exploit research that builds a proof of concept and records the finding once it runs.
AI DAST — the web scanner that writes its own payloads
Run it on its own or hand it to the pentest agent. The model examines each endpoint and decides which technique and which payload values to send.
What it does differently
- Payloads chosen per endpoint
- Covers flaws with no matching rule or template
- Adapts payload encoding when a WAF is present
- A confirmation pass discards anything it cannot reproduce
- Every finding carries the exact request and response that produced it
- Tests the authenticated application
34 attack techniques
- SQL & NoSQL injection
- Command injection
- SSRF
- SSTI
- XXE
- XSS, reflected and stored
- Path traversal
- Access control & IDOR
- Authentication bypass
- JWT & OAuth
- Business logic
- GraphQL & API
Close the loop
Vulnerability Management
Findings from every engine land in one place, deduplicated and validated, carrying the evidence needed to act on them.
Selected stage
One record per issue
Every engine and every classic scanner writes into the same record. An issue reported by three tools appears once, and one that returns after being closed reopens the original entry, so the dashboard count stays meaningful.
The reasoning is available for review
Call path, rationale and confidence on every finding
Developers are usually asked to take security findings on faith. Here the call path is drawn, the rationale is written down and the confidence is stated, so review starts from evidence.
A proof of concept that executes
Generated, edited and run from the interface
The proof of concept is readable code that can be edited and saved. Run it against the target and read the result, or have the model rework it until it succeeds.
How proofs are generatedPlatform & Coverage
How it fits an existing environment
Code never leaves the perimeter
On-premise, private cloud, or fully air-gapped
The whole platform runs inside the target environment, including the exploit database it queries — no source code and no scan data is sent elsewhere for analysis. For regulated industries this is usually the question that decides the evaluation, so it is answered first.
Deployment modelsFits an existing pipeline
Run scans from an existing CI/CD pipeline and send results to DefectDojo or Jira, so remediation, SLAs and reporting stay in the tools already in use.
CI/CD integrationChoice of model
Hosted providers, a compatible gateway, or models running locally on dedicated hardware. Token spend is recorded per scan, so the cost of a scan is visible before the invoice.
Providers and costExternal attack surface
Start from a domain name and discover the subdomains, hosts, exposed email addresses and leaked credentials attached to it — including the assets nobody listed in scope.
Attack surface discoveryCustom rules everywhere
Organisation-specific checks can be written as rules across code, web and infrastructure scanning — including rules the platform generates from existing findings.
Custom rulesWhere it fits in the delivery lifecycle
Security that only runs at the end produces a list nobody has time to act on before the release date. ScanSuite attaches to each existing stage, so issues surface while they are still cheap to fix.
Selected phase
Development
Risks are flagged in the developer's own workflow, before the code is shared — the cheapest possible moment to fix anything.