Aegis ingests raw signals from 42+ sources, normalizes them into a single risk language, correlates patterns no single tool can see, and predicts where risk is heading — all within your security boundary.
How Aegis Turns Noise
Into Actionable Intelligence
Every security tool in your stack is producing signals. The problem isn’t the signals — it’s that no system has ever connected them all. Aegis does, in four continuous stages running in under 60 seconds.
Sentinel uses severity levels 1–5. AWS uses compliance percentages. Entra ID logs raw event counts. Drata tracks control pass/fail. Every source speaks a different language, making cross-source comparison impossible without manual translation.
How Aegis Solves It
42+ pre-built VDS connectors in native API wire format
The RIE runs 10 continuous jobs against unified data — scoring, correlating, predicting, and detecting drift. This is where raw data becomes risk intelligence that no individual tool can produce on its own.
Intelligence without action is just noise. Every correlation finding, drift alert, and risk prediction generates P1–P4 Action Cards with AI-generated remediation guidance — ready to assign, ticket, or escalate in one click.
What You Get
Prioritized Action Cards (P1–P4) auto-generated from signals
›AI remediation guidance with source citations on every card
Every thumbs-up/down on an action card feeds the Intelligence Recalibration Engine. Over time, Aegis learns your environment — reducing false positives, improving prediction accuracy, and adapting scoring weights to your actual risk profile.
Native API wire format connectors — not screen scrapers, not CSV imports. Real-time data in the raw format each tool produces, processed by VDS extractors that understand each source’s schema.
Natural language to SQL to board-ready answer in under 3 seconds. No query language. No data analyst queue. No waiting for the next report cycle.
YOU ASK
"Which controls failed most often in the last 30 days and what's driving it?"
AEGIS ANSWERS IN <3s
CIS Benchmark led with 12 failures, concentrated in the Feb 10–13 drift event. IAM-Config-01 and S3-Encrypt-04 account for 8 of 12. Recovery predicted by Mar 25 at 74% confidence. 3 unassigned P1 Action Cards open.
YOU ASK
"What does NIST AI RMF require for our model inventory under GOVERN-1.2?"
AEGIS ANSWERS WITH CITATIONS
GOVERN-1.2 requires documented AI risk tolerance criteria measurable by your organization. Relevant evidence: AI-GOV-POL-003 (expires in 14 days) and MODEL-RISK-ASSESSMENT-Q1-2026.
Two AI Query Modes
CONVERSEDATAIQ
Natural Language → SQL → Results
Queries your live security database in plain English. Every answer is data-backed with source citations and confidence scores.
RAGCONVO
Chat with 14+ Compliance Frameworks
NIST AI RMF, ISO 42001, SOC 2, FedRAMP, EU AI Act, OWASP LLM Top 10 — ask any question, get a grounded answer with framework citations.
INFERENCE MODEL
gpt-oss-120B · LLaMA 3.3 70B
DEPLOYMENT
Private · Airgapped · Zero Egress
Ready to See It
In Your Environment?
Every POC session is built around your actual data sources. No generic demos — your stack, your signals, your risk picture.
