How Axoflow Really Uses AI
It runs inside the Autonomous Data Layer to classify, fix, and explain your security data – without handing your production environment to a black-box agent.
Executive summary:
How Axoflow uses AI
Axoflow uses AI to solve one problem above all: garbage in, garbage out.
We don’t ask an agent to run your production environment. We use AI inside the Autonomous Data Layer to keep your security data clean, well-structured, and explainable – so your SOC tools and teams can make better decisions.
Today in GA
AI-maintained log fingerprint and normalization database for hundreds of off-the-shelf security products.
Vendor-owned regex, parsers, and field mapping – your team doesn’t maintain them.
In early access
AI features that run where your data lives (on-prem / your cloud).
Copilot embedded in AxoConsole for:
- Natural language → queries
- Natural language → pipelines
- Regex help for custom logs
- UI-aware assistance & doc surfacing
Next
In-stream anomaly detection based on classification and pipeline metrics.
In-stream threat-focused detection closer to where data is produced.
A governed AI “data engineer” experience that improves data quality and operator experience – without ever being a black-box root user.
AI is only as good as the context it gets.
Axoflow’s Autonomous Data Layer is designed to fix the data quality problem for security operations first – then layer AI on top of that solid foundation.
Deep-dive
for practitioners
Everyone says “AI”, then it’s your team that still babysits pipelines
The AI hype problem in security data
In security data, “AI” has become a checkbox:
- “AI-native pipelines” that still rely on your team to write and maintain parsers.
- “Agentic AI data engineers” that promise automation but mostly generate todo lists.
- New “AI agents” that want production credentials to do anything useful.
These stories sound great - mostly in board decks.
But practitioners don’t want a black-box agent with admin privileges on prod. They want:
- Logs that arrive classified, parsed, and normalized.
- Pipelines that don’t break quietly at 2 a.m.
- Help understanding what’s happening in the data, not another system to babysit.
That’s the job we use AI for in Axoflow.
AI-maintained classification & normalization, vendor-owned regex patterns
Principle #1: We use AI to maintain classification & normalization – so your team doesn’t have to
Axoflow Platform is the Autonomous Data Layer for security operations, collecting, classifying, reducing, normalizing, routing, and storing your security data at scale. At the core is our classification engine:
- We fingerprint log messages and match them to an Axoflow-maintained database of log samples – think malware database, but for logs.
- We use AI to expand and maintain this fingerprint database as vendors change formats or ship new versions.
- For off-the-shelf security products, Axoflow automatically:
- Detects the source type
- Applies the right parsing and normalization to the schema required by the destination
- Keeps regex, field mappings, and schemas up to date without involving your team
So instead of:
“Here’s an AI-generated regex, please review and deploy it,”
Our promise is:
“We own the regex and normalization and use AI to keep the engine sharp –
 so your team never has to touch regex for supported products.”
We don’t pretend everything is magic: for custom logs, use our copilot to help create patterns. But the default for hundreds of commercial security products is truly autonomous operations.
Meet your AI data engineer for security pipelines
Principle #2: An AI data engineer in your console
We’re building a copilot embedded into AxoConsole, right where you manage your Data Layer.
Early access capabilities include:
- Natural language → queries
- “Show me the top 10 sources by dropped events this week.”
- Copilot generates real queries you can inspect and reuse.
- Natural language → pipelines
- “Create a pipeline that drops debug logs, masks usernames, and sends reduced data to our SIEM and raw data to AxoLake.”
- Copilot scaffolds sources, transformations, and destinations; you stay in control.
- Regex help for custom logs
- Provide sample lines and desired fields; the copilot proposes parsing expressions.
- UI-aware assistance & docs
- “What does this pipeline do?”
- “What actions are available on this page?”
- The copilot uses live metrics and documentation to explain what you’re seeing and highlight the right controls.
- Privacy and control
- Models can run locally or inside your own cloud – no need to ship sensitive payloads to a third-party service.
- You can bring your own model or choose from hundreds of supported models.
- You control which data the copilot sees; credentials and secrets never have to leave your environment.
For custom logs, copilot helps you
Principle #3: Help where you still need it – custom logs & regex
For off-the-shelf security products, Axoflow’s classification engine and zero-maintenance connectors hide all the regex and parser maintenance from your team.
But you’ll always have custom logs:
- In-house applications
- Legacy systems
- One-off integrations
Here, the copilot acts as a regex and parsing assistant. Planned functionalities include:
- You provide a few sample log lines and the fields you care about.
- The copilot proposes parsing expressions or patterns.
- The platform validates, versions, and deploys them for you.
From autonomous data quality to autonomous signal discovery
Roadmap: where we’re taking AI next
We’re careful not to over-promise. These capabilities are in active development. Timelines and specifics may change, but the direction is clear:
- In-stream anomaly detection
- Use classification and pipeline metrics to detect unusual volumes, new patterns, and rising error rates as data flows.
- In-stream threat-focused detection
- Bring detection content and models closer to where data is produced, and combine them with Axoflow’s replay and storage to accelerate investigations.
We’ll continue to prioritize AI that improves data quality, reliability, and operator experience – not AI that quietly introduces new failure modes.
How Axoflow AI compares to typical “AI data engineer” pitches
How does this differ from “AI data engineer in a box?”
Markets an “agentic AI” or “AI data engineer” that will “autonomously build and maintain pipelines.”
We use AI to maintain classification and parsing for supported products and to assist humans with pipelines and queries. No black-box agent has free rein in your prod environment.
Generates pipeline configs, parsers, and regex, then expects your team to review, debug, and maintain them.
For off-the-shelf products, we own the parser and regex lifecycle and use AI to keep it up to date. For custom logs, Copilot assists you, and the platform wraps that in safe deployment and versioning.
Often requires sending telemetry, schemas, or even credentials to a centralized AI service.
Models can run on-prem or in your cloud, with BYO model support. Sensitive data never have to leave your environment.
Talks about “AI-native pipelines”, but still assumes you’ll “manage the pipeline health.”
Axoflow’s AI is embedded in the Autonomous Data Layer that already automates classification, curation, and routing – so you get tangible reductions in hands-on pipeline babysitting.
*Based on public materials from vendors in the security / observability data pipeline space.