Enable AI Threat Detection

The challenge

When attackers use AI, seconds matter – and most pipelines can’t keep up

Agentic AI is collapsing attacker dwell time from days to minutes – reconnaissance, exploitation, and lateral movement now happen at machine speed. SOC teams have a much smaller window to detect and respond, and that window only stays open if the data underneath is fast, structured, and complete. Most security pipelines weren’t built for that: inconsistent formats, coverage gaps, and parsers that silently break when vendors change their schemas. Before AI defenders can keep pace with AI attackers, the data infrastructure underneath them has to be right.

The Solution

DataStream – a security data foundation for AI-era defense

Real-time collection at machine speed

High-throughput, low-latency collection from endpoints, cloud, identity, network, OT, enterprise applications, and AI systems – processed and routed in real-time, with the lowest latency in the market.

Schema-first normalization and enrichment at ingestion

Every event is automatically mapped to a unified schema – ASIM, OCSF, ECS, UDM, and others – and enriched with context (threat intel, asset, identity, geo) before it reaches your detection stack. Analysts and AI agents get decision-ready data, not raw logs. Schema drift detection surfaces format changes before they silently break your detections.

Complete source coverage – 250+ vendor packs

Agentless or agent-based collection across cloud, on-premise, identity, endpoints, OT, enterprise applications, and AI systems, with 250+ ready-to-deploy vendor packs out of the box. Every relevant source is connected, so there are no gaps for attackers or AI agents acting on their behalf to move through undetected.

Automatically maintained pipeline templates

DataStream Content Hub templates span security, cloud, identity, OT, enterprise applications, and AI systems, and are maintained and updated by VirtualMetric’s engineering team as vendors evolve. No manual parser rebuilds when schemas change.

Key benefits

Why this approach works

Faster
response

Analysts and AI agents act on threats in seconds, not minutes, because the data arrives in real time and already in context.

Higher detection
quality

Consistent, normalized data means fewer false positives and sharper correlation across sources.

No blind
spots

Complete coverage ensures there are no gaps for attackers or AI-generated exploits to move through undetected.

Future-proof
pipeline

As AI tooling evolves, DataStream’s schema support and content updates keep your data infrastructure aligned.

a benchmark for data pipelines | virtualmetric

Speed proof

The fastest security data pipeline on the market. Openly benchmarked.

Speed is a security feature, and we can prove it. PipeBench is an open, reproducible benchmark for security data pipelines. Methodology is public, results are reproducible, and any vendor can add their system. DataStream leads across throughput and latency in every measured scenario.

Highest EPS throughput – across all tested scenarios
Lowest latency – real-time processing at scale
Open methodology – published on GitHub, reproducible by anyone

See the full benchmark results

Frequently asked questions

Why does the AI era change what a SOC needs from its data pipeline?

Agentic attackers execute reconnaissance, exploitation, and lateral movement at machine speed, shrinking the SOC’s response window from hours to seconds. At that tempo, defenders (whether human analysts or AI agents) cannot afford to wait on batch ingestion, broken parsers, or post-hoc enrichment. The pipeline has to deliver clean, normalized, enriched data in real time, or the rest of the stack is too late.

What makes data “AI-ready”?

AI detection models, AI agents, and the analysts working alongside them need data that is structured consistently, enriched with context at ingestion, delivered in real time, and complete across every relevant source. DataStream addresses all four: schema-first normalization for structure, in-pipeline enrichment for context, high-throughput collection for speed, and broad source coverage to eliminate blind spots.

Is there AI inside the DataStream pipeline?

No – and that’s intentional. The data path itself is fully deterministic: security teams know exactly how every event is collected, normalized, enriched, and routed, with no opaque AI decisions in between. That keeps the pipeline predictable, auditable, and safe to feed into the AI detection tools your team has chosen. (VirtualMetric does use AI elsewhere, for example, in tooling that helps customers build pipelines faster – just not in the runtime data path.)

How does DataStream handle schema changes when vendors update their log formats?

DataStream includes schema drift detection that surfaces format and field changes before they silently break your detections. Content Hub templates (which cover major security vendors) are maintained and updated by VirtualMetric’s engineering team, so your pipeline adapts as the landscape evolves without requiring manual parser rebuilds.

Does DataStream work alongside existing AI detection tools?

Yes. DataStream is not a detection tool; it is the data infrastructure layer that sits upstream of your detection stack. It feeds clean, normalized, real-time data to your SIEM, AI agents, data lake, or analytics platform. It works alongside Microsoft Sentinel, Google SecOps, Splunk, Elastic, and any other platform your team uses.

What security schemas does DataStream support?

DataStream supports ASIM, OCSF, CEF, ECS, UDM, CIM, and other major security schemas. This ensures that normalized data is immediately compatible with your SIEM and AI tooling without additional transformation work.

How does DataStream help with the speed requirements of AI threat detection?

DataStream is openly benchmarked as the fastest security data pipeline on the market, measured by events per second throughput and processing latency. The full methodology and results are publicly available at benchmark.virtualmetric.com.