One system that is SIEM and SOC at once — with AI, sovereign.

The gap between “too small for enterprise” and “too big for bare open-source”.

Mid-market organisations (50–500 servers) need enterprise-grade capabilities but hit three barriers at once.

Regulatory context. NIS2, DORA and the EU AI Act turn monitoring and incident response from an option into an auditable obligation. Whoever offers detection and response — traceable and on-premises when needed — wins the regulated market.

A single platform: collect, index, measure, detect, understand, act.

Each layer is a proven engine, configured to work with the others, with a proprietary AI layer on top that turns data into decisions. One product, not tools glued together.

flowchart LR
  subgraph SRC["Telemetry sources"]
    S1["Servers & VMs"]
    S2["Containers"]
    S3["Network appliances"]
    S4["Apps & APIs"]
  end
  subgraph CORE["AEGIS core"]
    C1["Collection & indexing · SIEM"]
    C2["Detection & metrics · SOC"]
    C3["AI pipeline · local LLM"]
  end
  subgraph OUT["Action & visibility"]
    O1["Dashboards"]
    O2["Automatic response"]
    O3["API + WebSocket"]
    O4["Compliance reports"]
  end
  SRC --> CORE --> OUT
  classDef src fill:#e0e7ff,stroke:#6366f1,color:#312e81
  classDef core fill:#dbeafe,stroke:#3b82f6,color:#1e3a8a
  classDef out fill:#dcfce7,stroke:#16a34a,color:#14532d
  class S1,S2,S3,S4 src
  class C1,C2,C3 core
  class O1,O2,O3,O4 out

SIEM and SOC — in one platform, not two products bolted together.

Others sell either a SIEM (log collection + search) or SOC services (monitoring + response) and leave you to integrate the rest. AEGIS does both natively, on the same data core.

flowchart TB
  DATA["Single data core"]
  subgraph SIEM["SIEM · the memory"]
    M1["Indexing & retention"]
    M2["Search & investigation"]
    M3["Audit reports"]
  end
  subgraph SOC["SOC · the reflexes"]
    R1["Threat detection"]
    R2["AI triage + prioritisation"]
    R3["Response & remediation"]
  end
  DATA --> SIEM
  DATA --> SOC
  classDef data fill:#fef9c3,stroke:#ca8a04,color:#713f12
  classDef mem fill:#dbeafe,stroke:#3b82f6,color:#1e3a8a
  classDef ref fill:#dcfce7,stroke:#16a34a,color:#14532d
  class DATA data
  class M1,M2,M3 mem
  class R1,R2,R3 ref

What we do.

The base engines are proven open-source (no lock-in). On top of them we built the AI layer that makes the difference.

An alert becomes a story, not a dump — with a local LLM.

This is where the lost hours are recovered. Every alert goes through an AI pipeline that enriches it with context and prioritises it — and the model runs inside your perimeter.

flowchart LR
  A["Raw alert"] --> B["Context enrichment"]
  B --> C["Local LLM"]
  C --> D{"Priority?"}
  D -->|"known & severe"| E["Automatic response < 5s"]
  D -->|"ambiguous"| F["Narrative + severity → operator"]
  classDef n fill:#dbeafe,stroke:#3b82f6,color:#1e3a8a
  classDef good fill:#dcfce7,stroke:#16a34a,color:#14532d
  classDef warn fill:#fef9c3,stroke:#ca8a04,color:#713f12
  class A,B,C n
  class E good
  class F warn
  class D warn

SaaS, on-premises or air-gapped — from the same build.

The same platform, three delivery modes. Choose based on how sensitive your data is and what internal capability you have — not on product limitations.

flowchart TB
  Q{"How sensitive<br/>is the data?"}
  Q -->|"standard"| SAAS["SaaS · hosted by CAI<br/>platform + AI in CAI cloud"]
  Q -->|"mixed"| HYB["Hybrid<br/>data & LLM at the client"]
  Q -->|"regulated / isolated"| ONP["On-premises / air-gapped<br/>full stack + local AI"]
  classDef q fill:#fef9c3,stroke:#ca8a04,color:#713f12
  classDef a fill:#dbeafe,stroke:#3b82f6,color:#1e3a8a
  class Q q
  class SAAS,HYB,ONP a

Which capability covers which European framework.

AEGIS maps methodically onto the recognised frameworks. We do not claim certifications — we show which capability supports which standard, with its real status.

✓ covered · ~ partial / opt-in · — not covered

Why you win with AEGIS.

Frequently asked questions