Private AI by profession

Private AI for Cybersecurity & IT

Security workflows often contain sensitive logs, incident context and internal infrastructure details. STACKELIER matches the task to a configuration while keeping privacy location, inference and hardware eligibility explicit.

Workflows

Private AI workflows for Cybersecurity & IT

The useful configuration depends on what the work actually contains, not only on the profession label.

Use case

Security log analysis

Process internal telemetry and incident context without silently moving sensitive operational data outside the approved environment.

Use case

Malware report analysis

Analyze reports and technical artifacts using a model/runtime profile that fits both the privacy boundary and the available compute.

Use case

Sensitive internal technical analysis

Work with architecture notes, configurations and incident material while keeping the end-to-end data path inspectable.

Decision priorities

What changes the recommendation

Priority 01

Operational telemetry boundaries

Use this as a constraint when comparing reference stacks and when completing the Finder.

Priority 02

Local or controlled inference

Use this as a constraint when comparing reference stacks and when completing the Finder.

Priority 03

Context size for technical evidence

Use this as a constraint when comparing reference stacks and when completing the Finder.

Privacy boundary

Self-hosted is not enough for Cybersecurity & IT.

A configuration is evaluated by its actual data path. Local storage does not make a workflow fully local if inference still leaves the controlled environment.

Application

Where does the interface or service run?

Data

Where are documents, embeddings and indexes stored?

Inference

Where does the model actually process the request?

Evidence

Which authoritative sources support those claims?

Verified baseline

Reference stacks for Cybersecurity & IT

These are verified launch configurations already used by the deterministic Finder. They are reference profiles, not universal recommendations.

Hard constraints win: if a mandatory privacy or hardware requirement is incompatible, the stack is excluded rather than merely ranked lower.

FAQ

Questions about private AI for Cybersecurity & IT

Why does private AI matter for cybersecurity workflows?

Security data can include internal logs, hostnames, incident details and infrastructure context. STACKELIER evaluates where that data is stored and where inference occurs before a stack can qualify.

Can a local security assistant still send data outside the environment?

Yes. A local interface can call an external model or service. That is why application location and inference location are evaluated separately.

What hardware factors matter for log and report analysis?

Model size alone is insufficient. Context length, quantization, runtime overhead, available RAM or VRAM and the amount of concurrent work can materially change practical fit.