Validation & Benchmarking

What Changes, and by How Much?

7200core emphasizes objective data when evaluating the reliability and practical value of AI-assisted audits.

The objective is not simply to run an audit.

How does audit quality change? How does the human review workload change?

We intend to assess both under documented comparison conditions and with clear supporting evidence.

01 | Evaluating Audit Quality

We assess how accurately AI identifies issues.

Key evaluation measures include:

  • True Positives (TP): Issues correctly identified where a problem actually exists
  • False Positives (FP): Issues incorrectly reported where no problem exists
  • False Negatives (FN): Existing issues the audit failed to identify
  • Detection Metrics: Comparisons with clearly stated evaluation conditions and calculation methods

Reporting more issues does not, by itself, make an audit better.

We assess accuracy while accounting for both false positives and missed issues.

02 | Evaluating Human Effort and Cost

We also evaluate practical changes for users, not just audit quality.

Human Work Time

Measure time spent by people on operations, evidence review, and organizing results.

System Processing Time

Measure AI and system execution time separately from human work time.

Total Cost

Compare costs under aligned usage conditions and with a defined cost scope.

Re-Verification Effort

Assess the effort required to retrieve and re-examine previous audit results and evidence.

03 | Defining Fair Comparison Conditions

We plan to compare workflows using 7200core with one-off reviews conducted in AI chat, among other approaches, under aligned conditions.

Comparisons will document:

  • Evaluation subjects and sample size
  • AI models used and input conditions
  • Criteria for determining ground truth
  • Dates on which tests were conducted
  • Human work time and system processing time
  • Scope of cost measurement
  • Verified findings and unresolved or unverified matters

Beyond the outcome, the conditions under which results were obtained also matter.

04 | Publishing Validation Results

7200core is currently in development and validation.

Once shareable measurements are available, we intend to present methods, comparison conditions, confirmed findings, and limitations together.

At present, we do not have finalized public figures supporting claims of superior audit accuracy, reduced work time, or cost savings.

We aim to build a body of verifiable evidence rather than lead with unsupported numbers.

Report What Has Been Verified, Within Its Verified Scope.

7200core does not treat AI-assisted audit results as unconditionally correct.

It does not guarantee detection of every issue or improved efficiency in every workflow.

What has been established, what has not, and what remains unverified.

We will assess reliability and usefulness while clearly distinguishing those categories.

Current status: In development and validation

This does not indicate the availability of public trials or a commercial release.