"Eight layers. One data plane. One console.
Continuously red-teamed by design."
Most enterprises run seven or more disconnected AI security tools and still cannot answer the question: is our AI safe right now?
Forrester reported a Fortune 500 organisation discovering 600+ AI agents in its environment that had never been catalogued. Microsoft confirms 80% of the Fortune 500 now run active agents. Inventory is broken.
20% of breached organisations had a breach linked to shadow AI, adding USD 670k per incident (IBM, 2025). 63% of organisations have no functional AI governance policy. Visibility ends at the browser tab.
Protect AI found ~352,000 unsafe issues across 51,700 Hugging Face models. Unit 42 demonstrated namespace-reuse attacks substituting backdoored models into Vertex AI and Azure AI Foundry catalogues.
Red teaming is still a separate, point-in-time procurement — a report dated six weeks ago describing a system that no longer exists. No vendor validates AI controls continuously inside the same data plane.
Every layer feeds a single correlation engine. Every signal enriches every other. The defensive stack and the offensive stack become a single feedback loop.
Real-time inventory of every AI-related asset across code, cloud, browsers, OAuth grants, network flows, and SaaS. Models, agents, MCP servers, vector DBs, prompts, datasets — sanctioned, in-house, or shadow.
Every model and dataset entering the enterprise carries a cryptographically verifiable provenance record. Policy is enforced at ingestion and continuously re-verified against threat intelligence.
Every AI agent, MCP server, and inference call is treated as a first-class non-human identity. Scoped, short-lived runtime credentials. Just-in-time access. Tamper-evident audit receipts for every action.
The architectural keystone: an open AI security telemetry schema that captures every prompt, completion, tool call, agent message, identity exchange, and inference event. OCSF-aligned, designed to coexist with your existing SIEM.
AI-specific kill-chain detection logic that converts low-fidelity signals into high-fidelity incidents. Cross-layer correlation surfaces multi-stage attacks that single-vendor tools cannot see.
An investigation console built for the AI-SOC analyst persona. Full session reconstruction. Explainability and lineage views. Orchestrated response playbooks tuned to AI-native incidents.
MLOps-grade dashboards for the teams building proprietary models — wired directly into the security correlation engine. Drift becomes a detection signal, not a separate dashboard.
Red teaming moves from a procured report to a continuous, automated capability running inside the same data plane as your detections. Every red-team finding tunes the platform's defences. Every defence improvement spawns new attacks. The flywheel that no point vendor can build.
Answer the questions the board now asks: how many AI agents do we operate, who can they reach, how do we know our controls work, and what did our red team prove this quarter? AEGIS·AI produces the audit trail — not the assertion.
Stop pivoting between seven tools. Start with the incident, get the full session, see the lineage, run the playbook. Built around AI-specific kill chains, not retrofitted from legacy SIEM templates.
Drift, bias, fairness, hallucination, and robustness signals — surfaced where data scientists already work — with the security team consuming the same signals downstream. Build velocity without building exposure.
| Category | The Gap | AEGIS·AI |
|---|---|---|
| Point AI Firewalls | See prompts in, completions out. Blind to tool calls, agent identity, model lineage, and supply chain. No investigation layer. | Inline gateway is one of eight layers, all feeding a single correlation engine and a single investigation console. |
| AI-BOM Scanners | Static inventory at a moment in time. Disconnected from runtime alerts. No continuous re-verification, no response capability. | Inventory is continuous, cryptographically verified, and feeds correlation rules that detect runtime model substitution. |
| Stand-Alone Red Teaming | A report dated six weeks ago. No connection to your live detections. No way to verify whether the gap has been closed today. | Red teaming runs continuously inside the same data plane. Every finding tunes detections. Every detection improvement spawns the next attack. |
| Hyperscaler Bundles | Lock-in to one cloud's identity, one cloud's SIEM, one cloud's models. Multi-cloud enterprises are stranded. | Cloud-agnostic, model-agnostic, SIEM-agnostic. Designed to integrate with the incumbent platform, not replace it. |
AEGIS·AI is model- and framework-agnostic. We instrument where the AI runs, not where it was built.
We don't ask you to rip and replace your SIEM. We make it AI-fluent.
Evidence collection mapped to the frameworks regulators actually ask for, not generic checklists.
AEGIS·AI is live and deploying across enterprise environments in Singapore and Asia Pacific. Speak to us about your environment and we will show you what eight layers look like in practice.
For organisations running AI in production — agents, models, pipelines, or all three. We scope a deployment to your environment, connect the eight layers to your existing SIEM and IAM stack, and have you operational. No rip-and-replace. No disruption to existing tooling.
Start a Deployment →Not sure where your AI security gaps are? We run an advisory engagement first — mapping your AI asset landscape, identifying your highest-risk exposures, and building the case for how AEGIS·AI closes them. The platform follows the strategy, not the other way around.
Request an Advisory →We are looking for practitioners who have worked at the frontier of AI security, red teaming, ML infrastructure, and distributed telemetry. If you want to work on the hardest problem in the space with people who have built national-scale security programmes, this is the conversation to have.
Start the Conversation →