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Control evaluation · Practical playbook

Measure the delay employees experience when submitting to AI

The time until an AI answer appears includes more than a security check. Separate policy-decision delay, provider acceptance and model response time so an evaluation identifies the part of the workflow that employees actually experience.

For IT performance owners and security pilot leads

Synthetic example

A repeatable synthetic summary request

An IT team compares how long a fictional document-summary request takes to reach a decision in several approved pilot conditions. No production security controls are disabled to create a baseline.

What you are working with

  • A short benign prompt, a longer benign prompt and a synthetic restricted fixture with expected decisions.
  • Representative synthetic files within the vendor-confirmed size and format scope of the pilot.
  • A timing worksheet identifying device, network, browser, product version and the events being measured.

A safer approach

  • Define start and finish events that can actually be observed, such as clicking Send and receiving a policy decision.
  • Repeat each scenario under comparable conditions and record timeouts rather than discarding slow runs.
  • Use an isolated, approved comparison environment if a baseline is needed; keep required production protection intact.

Expected outcome: The evaluator can describe typical and slow-case submission behavior for the tested conditions without attributing all provider or network delay to DLP.

Put it into practice

Work through the procedure

  1. Choose observable timing boundaries

    Define the employee action that starts the timer and the event that ends it. A policy popup, attachment completion and first model token are different endpoints. If internal processing timestamps are unavailable, report the user-observed interval without inventing a breakdown.

  2. Control the test conditions

    Record device load, file size, network conditions and service account before each series. Use the same content for comparisons and note whether the application is freshly opened or already active. These details make an unexpected slow result easier to reproduce.

  3. Repeat and retain slow runs

    Run several repetitions for each agreed condition and keep the individual timings. Summarize the distribution only when there are enough observations to support it. Record blocked, allowed, failed and retried actions separately because their timing boundaries may differ.

  4. Review the employee impact

    Compare the results with criteria defined before the test. Observe whether users understand a pending inspection and whether a delay encourages repeated clicking. Agree a remediation or narrower rollout where the experience is unclear, even if the median timing looks acceptable.

Evidence before approval

What to check before proceeding

1. Timing attribution

Ready when
Reported measurements state exactly which user-observed events begin and end the interval.
If the check fails
Relabel the measurements and avoid claims about unobserved internal processing time.

2. Slow-case behavior

Ready when
Recorded slow runs and timeouts meet the agreed operational handling requirement.
If the check fails
Investigate their conditions and define a safe user response before expansion.

3. Repeatability

Ready when
Another evaluator can reproduce the scenario from its content and environment notes.
If the check fails
Fill gaps in the test record before treating a result as a product comparison.

Common mistakes to avoid

  • Presenting time to the first AI answer as the security engine's processing latency.
  • Discarding retries or timeouts and publishing only the fastest successful demonstration.
Workforce AI Security

Evaluate this workflow with Aona

Where Aona can help

Agree observable timing boundaries and supported fixture sizes with Aona engineering during the pilot.

What to confirm

This guide supplies a measurement method, not a latency guarantee; performance must be assessed on your actual supported workflow.

Evaluating a control for your organization?

Bring your target AI tool, device and acceptance criteria. Review the supported control path, the evidence you need and any limitations before deciding on a pilot.

FAQ

Questions about this workflow

Not necessarily. A clearly labeled user-observed timing can support an initial evaluation. More detailed attribution requires appropriate instrumentation and agreement about what each timestamp represents.
Technical evaluation

Evaluating a control for your organization?

Bring your target AI tool, device and acceptance criteria. Review the supported control path, the evidence you need and any limitations before deciding on a pilot.

Measure AI DLP Submission Latency | Aona AI