How do telcos handle AI audit logging at scale on Plugsky?

Key facts

Event volume	One event per model call plus platform events; size storage from request volumes
Event classes	Authentication, key lifecycle, admin changes and inference metadata
Export	SIEM platforms including Splunk, Sentinel, QRadar and Datadog
Segmentation	Per-segment workspaces and log paths matching management and operational boundaries
Residency	Region-locked planes: EU (Frankfurt), GCC (UAE), APAC (Singapore), US (Virginia)
Change evidence	Proposed changes and approvals stay in existing change-management systems
Retention	Configurable prompt retention; metadata retained per policy
Status	Audit export is live; assistants and responses endpoints are coming soon

TL;DR

• Treat AI logs as telemetry, not a special case.
• One event per call; size storage from request volume before launch.
• Keep per-segment log paths aligned with network boundaries.
• Join AI proposals to change-management records for full evidence.
• Retain metadata long, content short, per segment policy.

How it works, step by step

1. Estimate event volume per queue or segment from expected request rates and tool calls.
2. Route model-call events through a collector into the SIEM your operations and security teams already use.
3. Merge plugsky platform events — authentication, keys and admin changes — into the same pipeline.
4. Attribute events to service, queue and segment so investigations can narrow quickly.
5. Keep proposed network changes and their approvals in change management, correlated with the AI event that produced them.
6. Match log storage residency and retention to each segment's policy and classification.
7. Tune detection rules after a pilot: alert on anomalies that operations can act on, not on raw volume.

Try it yourself

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Audit as telemetry at telco scale

Telcos already operate some of the largest telemetry pipelines in any industry, so AI logging should join that estate rather than sit beside it. The pattern is familiar: emit structured events, collect and normalise, route to SIEM for detection and to storage for retention. The AI-specific fields are modest — model alias, token counts, tool name, step index and correlation ID.

Plugsky contributes platform events for authentication, key lifecycle and administrative changes through audit export. Your applications contribute model and tool activity. Aggregated, they let an investigator ask precise questions: what ran in this queue, on which model, and what did it propose.

Segments, residency and change evidence

Network segmentation should carry through to logs. Management, operational and lab segments each get their own workspaces and log paths, with residency and retention matched to their classification. That keeps evidence inside the boundary it describes and simplifies audit scoping.

• Change control: agents propose, humans approve, and change management records the outcome — correlate it with the AI event.
• Retention: retain metadata to policy; keep content capture minimal and short-lived.
• Access: separate log administration from network operations where duties allow.

Making the data useful

Volume without detection rules is expensive storage. After a pilot, define the alerts that matter: unusual activity by a key, token spikes from one queue, rising error rates after a deployment, or model usage that does not match the approved routing policy. Each should map to an operational response.

Periodic review closes the loop: confirm that routing matches policy, that retention is enforced and that change-management correlations still resolve. The goal is not maximal logging but reliable answers to the questions regulators, auditors and incident responders actually ask.

Honest comparison

Telco concern	Plugsky	Typical AI platform	In-house build
Volume handling	Structured events into your collector and SIEM	Vendor dashboards only	Your pipeline design
Segment alignment	Workspaces and log paths per segment	Shared tenancy	Your tenancy model
Platform events	Auth, keys and admin exportable	Partial	You instrument everything
Change evidence	Correlated with change management	Not addressed	Your integration
Residency	Region-locked planes plus disconnected estates	Limited	Wherever you host
Retention	Configurable metadata and content windows	Usually fixed	Fully yours

Frequently asked questions

How much log volume should we expect?

At least one event per model call, plus tool calls and platform events. Estimate from request rates per queue, and remember content capture multiplies storage far more than metadata does.

Should AI logs go to the existing SIEM?

Yes. Joining the existing pipeline gives you mature detection, retention and access controls, and keeps AI activity visible alongside network and security telemetry.

How do we keep logs within a segment?

Use per-segment workspaces and log paths, with residency and retention matched to classification. Evidence then stays in the boundary it describes.

How do we correlate AI proposals with change records?

Use a shared correlation identifier between the AI event and the change ticket, so an investigator sees both the recommendation and the human decision in one place.

What about lawful or regulatory requests?

Retention and export follow your legal process. Keep decisions in change management and evidence in the SIEM so requests resolve through documented channels rather than ad hoc searches.

Do we need content capture?

Rarely. Metadata answers most operational and audit questions; content should require a specific purpose, short retention and restricted access.

How do we know the pipeline works?

Run a pilot on one queue, then trace a specific incident or decision end to end. If the correlation breaks anywhere, fix it before scaling.