Topic starter
18/08/2026 2:15 pm
TL;DR: AI grounding and runtime data security gaps persist because DSPM can locate sensitive data but cannot govern how AI and machine identities use it, according to Polymer. Runtime enforcement has to limit retrieval, propagation, and downstream use once access turns operational.
Editorial analysis by NHI Mgmt Group, based on content published by Polymer: “Data security news & resources”.
Key questions
Q: Why does AI grounding create risk even when the data is already known and classified?
A: Because grounding turns static data into active input for decisions and outputs.
Q: How do security teams reduce runtime exposure in AI and cloud workflows?
A: They constrain access where the request happens, not just where the data resides.
Q: What breaks when data security only tells you where sensitive data lives?
A: You get a map of exposure without a control over use.
Practitioner guidance
- Implement runtime enforcement for sensitive data Add policy checks at retrieval and request time so sensitive data can be filtered, masked, or denied before it reaches AI prompts or automated workflows.
- Map identity-linked access paths to data sources Trace which users, service accounts, workloads, and AI agents can reach each sensitive dataset, then remove standing access that is broader than the workload requires.
- Separate discovery from enforcement in your operating model Use posture tools for classification and exposure discovery, but pair them with controls that govern actual use in production systems and grounding pipelines.
Bottom line: DSPM and similar tools can identify sensitive data, but they do not govern how that data is used once an AI workflow or machine identity reaches it.
Glossary terms: Runtime Data Security, DSPM, Grounding, Identity And Access Path
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This topic was modified 1 day ago by NHI Mgmt Group
18/08/2026 3:06 pm
Runtime data security is the missing control layer between data discovery and data use. Visibility tools can inventory sensitive information, but they cannot by themselves govern how that information moves through AI prompts, workload calls, or delegated access paths. That leaves a structural gap between classification and enforcement. Practitioners should treat runtime control as the layer that makes data security operational rather than merely descriptive.
A few things that frame the scale:
- Business leaders plan to spend $124 million on average on AI in 2026, and 91% say data security and risk will shape their AI strategy.
A question worth separating out:
Q: Should organisations treat DSPM and runtime controls as separate layers?
A: Yes. DSPM is strongest at discovery and classification, while runtime controls are needed to govern live use, especially in AI grounding and identity-linked access paths. The two are complementary, but only runtime enforcement can stop a permitted request from becoming an unnecessary exposure.
👉 Read our full editorial: AI grounding and runtime data security: why visibility is not enough