Forensic-grade telemetry across cloud, SaaS, identity, and AI: normalized, enriched, and kept investigation-ready for 1,000+ days. So your analysts and AI agents detect and contain active attacks without blind spots.
If your data layer can't produce context, your runtime defense breaks at the point of use.
Storage is no longer the hard part. Turning raw logs into context, fast enough to matter, is.
Most enterprises already have more security telemetry than they can use. The hard part is turning raw rows from cloud providers, SaaS apps, identity systems, and AI services into context, fast enough to matter.
Analysts burn time translating schemas, stitching events together, and deciding whether scattered activity belongs to the same incident. AI agents hit the same wall even faster: point one at raw data in a generic lake and the context window collapses, token cost spikes, and the answers get worse.
Mitiga built the Cloud Security Data Lake as a working security substrate, not passive storage you bolt security onto after the fact.
The enterprise now runs on cloud, SaaS, identity, AI, and third-party services. Each force raises the value of the same thing: a full-fidelity, investigation-ready data foundation that both analysts and AI can use.
AI now finds vulnerabilities faster than the world can patch them, and exploitation often begins before a fix ships. When you can't close the window in time, you need to detect, disrupt, and stop what gets through.
The average enterprise runs hundreds of SaaS apps: sanctioned, unsanctioned, and entirely unknown. You can't defend what you can't see, and posture tools were never built to surface active SaaS misuse.
Chatbots, copilots, and autonomous agents now act with their own credentials and permissions. The fastest-growing identity on your network is often not a person, and it operates entirely outside the endpoint's view.
Modern attacks cross cloud, SaaS, identity, AI, and third-party services in minutes. Defending against them means anticipating, detecting, interrupting, and stopping active attacks across the entire modern infrastructure, in runtime.
Built for forensics, not generic storage you bolt security onto later.
A security data lake is a central, investigation-ready store for security telemetry, built for forensics rather than generic storage you bolt security onto later.
Mitiga's Cloud Security Data Lake keeps cloud, SaaS, identity, AI, and third-party telemetry in one normalized, enriched, investigation-ready model, so teams investigate from the complete, correlated record instead of jumping between raw logs, archives, and siloed tools.
Processing security logs on general-purpose engines gets expensive fast. That cost pressure pushes platforms toward the same compromise: sample the data, drop noisy events, or shorten retention.
Pouring everything into a SIEM and then into cold storage doesn't solve it. You inherit the SIEM's cost problem and hand the customer the job of making sense of what's in there. Sampling is a business decision dressed up as a security one, leaving gaps exactly where an investigation needs depth.
The Cloud Security Data Lake spans cloud, identity, productivity, SaaS apps, SaaS infrastructure, AI infrastructure, AI SaaS, and third-party services, normalized into one working model. And an agentic ingestion pipeline keeps accelerating that coverage.
AWS, Azure, GCP
Okta, Entra ID, Ping, and others
Google Workspace, Microsoft 365
Salesforce, ServiceNow, Workday, Jira, and other business-critical platforms
Control surfaces such as GitHub, Snowflake, and adjacent services
Bedrock, Vertex, Azure AI
ChatGPT, Copilot, Gemini, and embedded AI agents and copilots
Adjacent platforms and integrations across the modern infrastructure
Raw logs aren't the value. Mitiga stores raw data at full fidelity, normalizes schemas, builds aggregation and context layers that resolve entities and preserve continuity, then generates the 1 to 3 percent subset of detections and forensic events that drives runtime action.
100's of sources, every schema, full fidelity.
Normalized and deduplicated into one working model, optimized for search across sources.
Enrichment, security semantics, entity resolution, and joined evidence where it matters.
Behavioral detections and forensic events built on context, not isolated rules.
Aggregation and context are the work, and the part most data lakes never finish.
Every source has its own schema, and those schemas drift constantly. Some vendors change schema on a near-weekly basis. When detections sit directly on raw vendor formats, a quiet change breaks the logic and the search returns nothing, often unnoticed until an investigation reveals the gap.
Drift silently breaks detections and forces teams to become experts in every vendor's log format.
Config-driven ETL and central schema management normalize change continuously, sometimes repopulating a moved field for backward compatibility, so runtime defense keeps working while history stays queryable.
The Cloud Security Data Lake is not a walled garden. Mitiga exposes it through an open, documented schema, MCP and API access, bring-your-own-bucket deployment, and clean export paths into the SIEM.
Published and usable directly by customers.
Query the lake in place, programmatically or in natural language, no SQL required.
Your storage, your retention policy, your control.
Send downstream only the contextualized subset that adds value.
Don't send an AI agent into a data swamp. Give it a lake with layers and context.
An MCP is, in the end, an API. Point one at raw data in a generic lake and it bogs down: context windows fill, token cost climbs, and results degrade.
Mitiga's MCP was built on top of real-world IR and threat-hunting know-how. It starts at the forensic-event and aggregation layers and only digs into raw data where needed. Analysts and AI get the same thing they both require: pre-processed, normalized, investigation-ready context that's correlated across your entire modern infrastructure.
Because the lake already holds enriched, full-history data, Mitiga builds and validates detections faster than traditional engineering allows. The Agentic Detection Factory maintains a repository of over 5,000 detections, growing by hundreds monthly.
Thousands of production detections across the modern infrastructure, with deep per-platform coverage.
The Agentic Detection Factory pipeline lifts output far beyond hand-written rates, so new platforms and sources are covered quickly.
Query-based, pre-tested, and tailored to your environment.
A large enterprise had its Salesforce logs streaming into Splunk at significant cost, then ran a red-team exercise against that environment. The SOC missed it, because raw logs no one can interpret are useless whether they sit in a SIEM or a lake.
Mitiga, working from the same data enriched and contextualized in the lake, caught nearly all of the red team's scenarios and techniques. Salesforce raw logs are notoriously difficult to work with; the difference wasn't more data, it was data made usable.
For any log, the value isn't collecting it, it's making it understandable enough to act and defend on in runtime.
The data layer isn't just the storage tier beneath Agentic Runtime Security, it's the operating model that makes it credible. Mitiga runs detections on live data in parallel with writing it to the lake, driving the entire pipeline: collect, enrich, aggregate, detect, triage.
Normalized, enriched, queryable, investigation-ready.
Severity and context update automatically as the attack story builds.
Verdict-backed context flows into incident handling, SIEM alerts, ticketing, and automated response.
Mitiga runs Agentic Runtime Security on top of the lake. The lake is what makes runtime defense trustworthy.
Investigate from the complete record. Full-fidelity, long-horizon history instead of sampled evidence.
Contain without waiting on data. Move from signal to evidence to action without rebuilding context on the fly.
Report with confidence. Forensic-grade history that supports compliance, disclosure, and executive explanation.
Lower downstream log pressure. Send only contextualized, high-value data into the SIEM instead of brute-forcing everything through it.
Create the foundation for Agentic Runtime Security. Because the goal isn't cheaper storage, it's faster detection, faster containment, and more defensible investigations.
A generic lake lowers your storage bill and hands you back the SIEM's real problem: someone still has to understand what's in there, manage schema change, and build detections that survive it.
Just pointing an AI agent at a generic lake fails in practice. On raw data, the context window collapses and token cost makes it both expensive and ineffective. Sampling and short retention save money exactly where investigations need depth, so the gap shows up at the worst possible moment.
You don't close that gap by storing more data more cheaply. You close it by making the data usable, for analysts and for AI, at runtime.
A central, investigation-ready store for security telemetry, built for forensics rather than generic storage or short-retention alerting.
SIEMs are optimized for alerting and shorter retention. The Cloud Security Data Lake keeps normalized forensic depth across 1,000+ days, and you can send the contextualized 1 to 5 percent subset on to your SIEM to cut its cost.
Yes. Keep your existing SIEM or Snowflake-based lake and use Mitiga to hunt and investigate across it, rather than forcing all logs into a vendor-owned environment.
No. An MCP lets you query the lake in natural language. It knows to start at the aggregation and forensic-event layers and only go deeper when needed.
You can. With bring-your-own-bucket, Mitiga still does all the processing, but the data, stored as open-format files, lives in your bucket under your retention policy and your control.
Over 1,000 days of investigation-ready history, with longer cold-storage retention available for compliance at a fraction of the cost.
Logs are collected at full fidelity, then normalized, enriched, and contextualized, so investigators and AI work from a timeline-ready foundation rather than raw fragments.
Most lakes store data. Mitiga's makes data actionable: cross-domain detection, AI triage, attack timeline reconstruction, and fast containment across cloud, SaaS, identity, AI, and third-party services.
