FedRAMP 20x Deterministic Telemetry: Where AI Summaries Fit

When evaluating an evidence platform, ask to see the observation behind the explanation. A generated summary may help a reader navigate records, but it should not become the only surviving account of what a system actually did. FedRAMP 20x's definition of deterministic telemetry makes that boundary concrete.

Start with the authoritative observation

The official Deterministic Telemetry definition, FRD-DTM, describes verifiable data taken directly from an authoritative source that represents a factual, reproducible observation of system attributes, such as state, configuration, or behavior.

The accompanying note excludes probabilistic inferences, generative outputs, and predictive assessments from the factual system-state record. It says those outputs must not be used to generate deterministic telemetry. A plausible explanation is therefore not a substitute for the observation the definition describes.

This definition addresses deterministic telemetry. Treating it as a statement that every possible AI use is prohibited would go beyond its stated subject. The workflow suggestions below concern how to keep explanations distinct from observations; they do not establish approval for a particular AI system or deployment.

Preserve a trace from the summary to its inputs

A practical design is to retain the original observation with its source, collection time, and resource identity, then keep any generated explanation as a separate derived item. Link the explanation to the records it used and label it clearly. These are implementation suggestions, not fields prescribed by FRD-DTM.

Consider a firewall example. An exported configuration describes a setting at a particular point in time. A generated paragraph interpreting that configuration is another object. Reviewers should be able to open the original record and assess the explanation against it, including its limitations.

Do not let a summarization step silently replace a missing observation. If a collection fails, record the collection failure in your workflow. A generated reconstruction of what the system probably looked like does not become the direct observation described by FRD-DTM.

Test the boundary during a product demonstration

Use a concrete demonstration instead of asking whether a tool is “AI powered.” Suggested questions include:

• Which system supplies the original observation?
• Can a reviewer inspect the input without relying on a generated explanation?
• How are the resource, collection time, and scope identified?
• What happens when the input is missing or conflicts with the summary?
• Can the team correct an explanation without altering the original record?

Ask the demonstrator to show a failed or incomplete collection as well as a successful one. That makes the division between observed state and interpretation easier to assess. These are buyer evaluation questions, not FedRAMP certification criteria for vendors.

Keep review conclusions separate from generated prose

Use summaries to help your team locate and understand records, subject to your own security and data-handling decisions. Have the responsible reviewer check conclusions against the original observations before relying on them. The useful operating test is whether the evidence remains understandable when the generated paragraph is removed.

For the broader collection process, see the KSI evidence workflow. Keep FRD-DTM's narrower question visible throughout that process: is this item a direct, verifiable observation, or an interpretation of one?