Sovereign AI foroffensive security
Sovereign AI and agentic unified platform for security engineering.
Backlogs grow faster than engineering can fix them
Rule-based triage and CVSS scores can’t tell an exploitable vulnerability from a false alarm in your environment. A memory layer that learns from your data changes that, correlating findings over time and re-prioritising them by real business impact instead of a static score. Without it, teams drown in alerts, chase findings that will never be exploited, and the risks that matter slip past.
of scanner findings are noise
False positives, measured across Cohort 1.
security tools in the average stack
Each integration adds alerts, none add clarity.
per EU security engineer, per year
Before they can even reach the backlog.
Two findings here are real and worth an engineer’s time. Everything dimmed is the work that never should have reached them.
One loop, run end to end — and proven at every step
The same loop a senior engineer runs, only it never sleeps — and it proves its work before it reaches you.
- 01
Observe
Every surface you run — code, cloud, APIs, AI agents — indexed continuously. Read-only, live in minutes, then watching for change.
Attack surface · indexedSource codeCloud infraAPIsAI agentsSupply chainWeb appsLive in minutes. Watching for change, continuously.
- 02
Reason
A memory layer correlates findings across layers and ranks them by real business impact, not raw CVSS. Sovereign AI reasoning, not a generic LLM.
Memory layer · ranked by business impactSSRF · /api/fetchExploitableOpen S3 · logs-archivePublic readOutdated lib · lodashNot reachableCVSS would rank these by score. Impact ranks them by you.
- 03
Validate
Each finding that survives is proven with a working proof of concept. No false alarms reach your team — only exploitable, in-context risk.
Proof of concept · executed$ curl '…/api/fetch?url=http://169.254.169.254/latest/' 200 OK ← reached cloud metadata iam/security-credentials/ exposed verdict: EXPLOITABLE — proven, not predicted
- 04
Deliver
A merge-ready pull request waits in your repository, with the PoC attached. Engineers apply the fix — they never triage the alert.
Pull request · ready to mergefix/ssrf-api-fetch+ ALLOWLIST = {"billing-cdn"} - requests.get(user_url) + safe_fetch(user_url, ALLOWLIST)
+2-1· tests pass · PoC attached
What sovereign AI actually delivers
Stop triaging false alarms
Every finding is contextualised against your actual environment. What's unfixable is filtered; what matters is flagged, and proven.
scanner noise filtered before it reaches you
Cut time-to-remediation
Validated findings arrive with a working PoC and a merge-ready PR. Engineers apply the fix instead of re-validating the alert.
of security engineering work, done in seconds per scan
Replace tool sprawl
One AI engineer handles scanning, triage, validation and fix generation, consolidating the stack instead of adding to it.
saved annually: €30k all-in versus a €220k+ legacy stack
Tested by the engineers who built the benchmark
“We ran SecNode’s beta against our CTF benchmark site, the same one we built to interview security engineers. It found every vulnerability. Hive Mind reasoned through each one like a senior security engineer would.”
The math
A legacy stack of point tools and a senior hire, replaced by one AI security engineer, all-in, per year.
See autonomous security reasoning in action
Watch how SecNode observes, reasons, and validates vulnerabilities across your stack, and hands back the fix, not another report.