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02/08/2026 12:28 pm
TL;DR: As AI, autonomous agents, and APIs push security operations toward faster telemetry decisions, DataBahn argues that selective, low-latency, structured collection is now central to SIEM, data lake, and AI model usefulness. The deeper issue is that telemetry governance is increasingly tied to identity and access control, not just collection volume.
NHIMG editorial — based on content published by DataBahn: Black Hat 2025, community innovation, and the role of telemetry in the AI era
Questions worth separating out
Q: How should security teams decide what telemetry to collect in AI-driven environments?
A: They should collect telemetry based on the security, detection, and compliance decisions the data must support, not on source availability alone.
Q: What problem does ownership attribution solve for service accounts and API keys?
A: It closes the gap between exposure detection and accountable remediation.
Q: What breaks when telemetry is enriched only after ingestion?
A: When enrichment happens after ingestion, the SIEM already absorbs the full cost and the analyst gets context too late.
Practitioner guidance
- Define telemetry scope by use case Create collection policies that specify which assets, events, and fields are required for each detection, response, or compliance objective.
- Separate context capture from central retention Capture identity, environment, and risk context as close to the source as possible, then route telemetry based on that enrichment before it reaches the highest-cost platform.
- Treat collection methods as control choices Use agent-based collection where local policy enforcement or richer context is needed, and agentless methods where footprint matters more than depth.
What's in the full article
DataBahn's full article covers the operational detail this post intentionally leaves for the source:
- Why the Smart Agent is positioned as a lightweight collection layer for reducing agent sprawl and storage waste
- How the vendor describes the agent versus agentless decision across asset type, risk profile, and latency needs
- What pre-SIEM enrichment and routing look like in practice for telemetry cost control
- Which Black Hat 2025 context and examples the source uses to frame the market shift
👉 Read DataBahn's Black Hat 2025 commentary on telemetry in the AI era →
AI-era telemetry and SIEM sprawl: what security teams need to change?
Explore further
02/08/2026 1:26 pm
Telemetry governance is becoming a first-class identity problem. Once AI agents and automation consume security data, the question is no longer only what gets collected, but who or what is authorised to influence downstream decisions. That makes telemetry pipelines part of the trust boundary, especially when service accounts, API keys, and workload identities are involved. The practitioner conclusion is simple: telemetry controls now need identity controls around them.
A question worth separating out:
Q: How do organisations know if telemetry governance is working?
A: Look for fewer unnecessary ingestions, higher-value events reaching the SIEM, and clearer ownership of collection rules and routing logic. A working programme can explain why data is collected, who can change it, and how enrichment improves both cost and detection outcomes.
👉 Read our full editorial: Telemetry in the AI era is becoming an identity and governance problem
