Topic starter
11/11/2026 6:04 pm
TL;DR: 53% of security leaders expect AI to run major portions of their infrastructure autonomously within three years, according to Apica's survey evidence. Observability has to adapt from application-only telemetry to runtime monitoring for autonomous systems, or cost and blind spots will rise together.
Editorial analysis by NHI Mgmt Group, based on content published by Apica: “Apica Named in the 2026 Gartner® Magic Quadrant™ for Observability Platforms”.
Key questions
Q: Why do observability costs rise so quickly in high-volume environments?
A: Observability costs rise because high-cardinality metrics, verbose logs, trace spans and long retention quickly increase ingestion, indexing and storage volumes.
Q: How does autonomous AI change monitoring requirements?
A: Autonomous AI changes monitoring because teams must watch the agent's runtime decisions, tool use, policy violations and delegated actions, not just system health.
Q: How should teams filter telemetry before it reaches observability platforms?
A: Filter telemetry at ingestion by dropping redundant, noisy and low-value events, normalising schemas, redacting secrets and routing only actionable logs, metrics and traces into the observability platform.
Practitioner guidance
- Implement first-mile telemetry filtering Apply processing, masking and sampling before telemetry reaches expensive analytics or storage layers.
- Separate hot and cold telemetry tiers Keep high-value signals queryable in performance tiers while moving long-retention data to lower-cost object storage.
- Instrument autonomous AI separately Track agent tool use, decision paths and runtime drift with dedicated telemetry instead of relying on generic application dashboards.
Bottom line: Observability economics now depend on whether telemetry is filtered and governed before it reaches costly downstream systems.
Glossary terms: Observability Platform, Telemetry, Autonomous AI system, Telemetry Filtering
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11/11/2026 6:06 pm
Telemetry governance is becoming a control plane issue, not a storage optimisation exercise. The cost problem in observability usually starts before analytics, when raw telemetry is allowed to enter expensive systems without policy. That makes collection, filtering and retention decisions part of security governance, because uncontrolled telemetry volume creates both financial waste and operational blind spots. Practitioners should treat telemetry policy as an access and lifecycle problem for data itself.
A few things that frame the scale:
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between hot storage and cold storage for telemetry?
A: Hot storage is the fast, frequently queried tier for recent telemetry, usually kept on low-latency media for searches, dashboards, and alerting. Cold storage is the lower-cost archive tier for older telemetry, optimised for retention rather than speed. The distinction is about retrieval performance, indexing cost, and how quickly evidence must be available.
👉 Read our full editorial: Observability governance shifts upstream as ingest costs surge