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18/08/2026 2:15 pm
TL;DR: AI grounding problems and runtime data security gaps persist because visibility tools show where sensitive data lives without controlling how it is used in live workflows, according to Polymer. The governance challenge is moving from post hoc discovery to runtime enforcement across AI, cloud, and identity-linked access paths.
NHIMG editorial — based on content published by Polymer: Data security news and resources on runtime data security and AI grounding
Questions worth separating out
Q: What breaks when runtime data security is not in place for AI workflows?
A: When runtime data security is absent, organisations can know where sensitive data exists without controlling how it is used by AI, connectors, or delegated identities.
Q: Why do AI agents make non-human identity governance harder?
A: AI agents make governance harder because they can request tools, act autonomously, and change behaviour across sessions while still relying on machine credentials.
Q: How do teams know if runtime controls are actually working?
A: They should be able to trace a decision from source event to applied rule to final outcome without gaps.
Practitioner guidance
- Map all runtime data paths Inventory every place sensitive data can be retrieved, transformed, or forwarded by humans, service accounts, and AI workflows.
- Scope workload identities to each data flow Assign distinct workload identity and short-lived credentials to grounding pipelines, retrieval services, and downstream automation.
- Enforce request-time policy checks Require policy evaluation before queries, downloads, or model calls that touch sensitive data.
What's in the full article
Polymer's full blog covers the operational detail this post intentionally leaves for the source:
- Concrete examples of how runtime data security is applied across AI, cloud, and SaaS workflows
- Implementation detail on controlling data access in live sessions rather than only discovering exposure
- Practical guidance on grounding-related data risks and where visibility tools stop short
- Operational context for teams deciding how to move from monitoring to enforcement
👉 Read Polymer's analysis of runtime data security and AI grounding →
Runtime data security vs DSPM: are your controls keeping up?
Explore further
18/08/2026 3:06 pm
Runtime data security is now an identity problem, not only a storage problem. Once AI systems and SaaS workflows can retrieve data on demand, the question shifts from where data lives to which identities can move it. That makes delegated access, workload identity, and session-scoped authorisation central to data security governance. Practitioners should treat runtime access paths as a core IAM control surface, not an adjacent concern.
A question worth separating out:
Q: Who is accountable when AI-driven automation touches sensitive personal data?
A: The organisation remains accountable, even when access is executed by workloads, service accounts, or automated workflows. Governance must cover the identity behind the action, the data touched, and the evidence produced. If automation can access personal data, it must sit inside the same access and audit model as human users.
👉 Read our full editorial: AI grounding and runtime data security: why visibility is not enough
