01 · Collect
Pool the signal, anonymized.
CVE feeds, exploit databases, your asset graph, OS/app update notes, and anonymized cross-org telemetry — live.
Sectors / Cybersecurity
Security teams aren't short on data. They're drowning in it.
CVE feeds, telemetry, and scanner output that mostly cry wolf. Knowing which vulnerability is real, reachable, and yours before an adversary does is a knowledge problem.
Research
Security teams are buried in signal: CVE feeds, telemetry, threat intel, and scanner output that mostly cry wolf. The cost isn't missing data — it's that ninety-nine alerts in a hundred are noise, and the one that matters looks the same.
Defenders also fight alone. Attackers reuse the same infrastructure across hundreds of targets, but each victim sees only their slice — so no one knows where it's really coming from. And every OS and app update is a map of fresh weaknesses: attackers read the patch notes and diffs to find what to hit before defenders deploy. A knowledge core that pools anonymized threat data tips the asymmetry back toward the defense.
Questions Worth a Clean Answer
Ask hard. Answer with clean data.
- Q01Every OS patch implicitly discloses a vulnerability. Attackers reverse-engineer the diff within hours. Can defenders read it faster and close the window before exploitation begins?
- Q02An attacker reuses the same C2 infrastructure across fifty targets, but each victim only sees their own logs. What would the full picture look like if the data were pooled anonymously?
- Q03A scanner flags 1,200 CVEs on your network today. Of those, how many have a reachable path from the internet to a system that actually matters — and which three should you patch first?
- Q04Zero-day brokers price exploits at millions. What would it mean for defense if the same vulnerability research were conducted openly, at scale, and shared before it's sold?
The Method — A Continual Loop
Collect, refine, hypothesize, test — repeat.
02 · Refine
Collapse to real exposure.
Duplicate, stale, and unreachable findings removed until only reachable risk remains.
03 · Hypothesize
Diff the patch, chain the gaps.
The core reads each update diff — what LLMs do best — to infer the fixed flaw, then chains weaknesses into real attack paths.
04 · Test
Validate the path.
Paths confirmed against the environment — simulated, sandboxed, or red-teamed.
05 · Refine
Adapt with the adversary.
Confirmed and dead paths update the core. Detection sharpens as attackers move. Continual.
The Cascade
Predictive Defense Cascade.
Fused CVE feeds, patch-diff inference, and cross-org anonymized telemetry turn raw signal into ranked, reachable risk. Coherent asset and attack-path graphs let defenders act before exploitation, shrinking dwell time and the attack surface.
Signal
Insight
Defense
Outcome
Select any node to trace its chain. Left to right: Signal → Insight → Defense → Outcome.
What the Core Delivers
Knowledge you can act on.
- Patch-diff intelligence: the flaw an update fixes, read from the diff before attackers weaponize it.
- Attack origins and reused infrastructure surfaced across victims — where it's really coming from.
- A private security core mapped to your assets, with anonymized cross-org threat sharing.