Point the best model at raw, uncorrelated logs and it inherits every gap your SOC
already has. Mitiga is Context Engineering for the AI SOC: turning raw cloud, SaaS, identity, and AI telemetry into deterministic, correlated, investigation-grade context.
The ground every verdict stands on.
Every AI SOC is bottlenecked by context quality, not model quality.
Model quality has raced ahead; context quality is where the AI SOC actually breaks – hardest on the surface of the modern infrastructure. Cloud, SaaS, identity, AI, and third-party services mean dozens of schemas, non-human identities with no user behind them, and native log windows that expire in 90–180 days, long before an investigation needs them.
That's exactly where today's attacks live, and where an AI SOC's context is thinnest.
An agentic SOC isn't a product you buy. It's a data problem you solve now, or pay for twice.
The market is scrambling to assemble two halves almost no one has natively – a specialized data layer and an AI detection-and-response engine – by bolting one onto the other, or assuming the substrate already exists. Both concede the same point: the substrate is the hard, valuable part of an AI SOC, not an afterthought.
AI scales recon, credential abuse, and lateral movement past human triage, turning autonomous agents and non-human identities into a live attack category.
Tooling built for data sitting still inside a perimeter can't watch cloud, SaaS, identity, and AI – exactly where the attacks now move.
Context engineering means feeding an agent the smallest, highest-signal, most trustworthy context instead of raw noise. Mitiga does it for security.
The Agentic Substrate is where it lives: the normalized, attributed, retained record across cloud, SaaS, identity, and AI, for an agent – yours or ours – to reason over.
One consistent structure and format across the entire modern infrastructure – deterministic and AI-ready.
One identity's activity stitched across IdP, cloud, SaaS, and AI into a single timeline.
1,000+ days of normalized history, past windows that expire mid-investigation.
Who, from where, on whose authority – including non-human and agent identities.
Reliability comes from the input, not the model – exposed the way an agentic SOC consumes it: MCP- and API-first, natural-language, aggregation-first to keep token cost low.
Every agentic SOC needs something to reason from. Mitiga is the substrate, for everything EDR can't see.
A substrate alone is a supply-side claim. Helios AIDR is the proof it's reasoned over in production, across three jobs the modern SOC can now do at once.
Runs the SOC's own workflow with AI: cuts alert noise, accelerates triage, raises decision quality.
Watches AI systems, agents, and service identities as first-class assets, catching one doing what no policy authorized.
Detects and contains AI-powered attacks that scale reconnaissance, phishing, and credential abuse at machine speed.
What makes context investigation-grade
of alerts triaged – each a structured, ready-to-act analysis, not a raw notification.
fewer false positives, with 90–100% noise reduction depending on the environment.
more signals covered than a traditional SOC can sustain on human triage alone.
investigation and remediation, ~6× faster than the unaided baseline.
An AI SOC is only as reliable as its context. An investigation-grade foundation is why every Helios AIDR verdict holds.
Where Mitiga fits in your AI SOC
Mitiga doesn't replace your SIEM's reach or your endpoint tooling. It's the deepest,purpose-built substrate for cloud, SaaS, identity, and AI, and it feeds the rest of your AI SOC from there.
At a glance
Mitiga doesn't replace your SIEM's reach or your endpoint tooling. It's the deepest,purpose-built substrate for cloud, SaaS, identity, and AI, and it feeds the rest of your AI SOC from there.
Model quality is a commodity. Context quality is the differentiator – the part almost no one has built.
objectives
Mitiga doesn't replace your SIEM's reach or your endpoint tooling. It's the deepest,purpose-built substrate for cloud, SaaS, identity, and AI, and it feeds the rest of your AI SOC from there.
01
Deterministic, correlated context so verdicts hold without re-checking each one.
02
Alert to containment as attacker automation accelerates.
03
Cloud, SaaS, identity, and AI – past where an EDR agent reaches.
04
Expose the same substrate to your agentic SOC and SOAR over MCP and API.
Why other approaches fall short
A better AI analyst pointed at your data assumes the substrate exists. For cloud, SaaS, identity, and AI it usually doesn't, so the agent inherits every gap.
A detection engine on a generic lake, or a pipe on a detection tool, shows its gaps exactly where a machine-speed investigation can't afford them.
Ingest-everything platforms storing raw signals trade depth for breadth, precisely where the attacks you care about move.
You don't transform the AI SOC by adding a smarter model on top of the same uncontextualized data. You transform it by engineering the context the model reasons from.
Mitiga is a critical element of the AI SOC: strong AI SOC capabilities, including Helios AIDR, built on context engineering across cloud, SaaS, identity, AI, and third-party services. It works alongside your existing stack, and feeds your agentic SOC over MCP and API rather than competing for universal ingestion.
Giving an agent the smallest, highest-signal, most trustworthy context it needs for a reliable verdict, instead of raw noise. Mitiga applies it to security – in runtime – so reliability comes from input quality, not model size.
It is Mitiga's Cloud Security Data Lake, described by the job it does by design for AI: normalized, attributed, retained context, exposed MCP- and aggregation-first. A generic lake stores raw logs; the substrate makes them investigation-grade by construction.
No. Mitiga is the deepest substrate for cloud, SaaS, identity, and AI, and exposes that context to your existing agentic SOC and SOAR over MCP and API.
No, by design. Mitiga goes deep on cloud, SaaS, identity, and AI – the modern infrastructure where EDR can't reach – and federates with the tools that own the other surfaces.
Investigation-grade input is why the output holds: Helios AIDR triages 100% of alerts with 95% fewer false positives, at scale, today.
The agentic SOC is a data problem you can solve now. Mitiga is deterministic, correlated, investigation-grade context across cloud, SaaS, identity, and AI, withHelios AIDR already reasoning over it in production. That's the Agentic Substrate, for everything EDR can't see.
Let them come.
