General-Purpose AI in GRC: Coverage and Factual Accuracy.

A general-purpose assistant can answer GRC questions and produce drafts in seconds. It can:

• Summarize policies and regulations
• Explain concepts in plain language
• Brainstorm controls and approaches
• Draft responses for later review

That helps with early exploration and learning. The drafts still need verification before anyone treats them as finished work.

GRC work needs:

• Full coverage of applicable obligations
• Clear mapping between requirements and evidence
• Accurate applicability decisions
• Traceability to primary sources
• Outputs that stand up to audits, regulators, and customers

A fluent summary that omits obligations leaves work hidden from the reviewer.

We ran an internal benchmark of Sorena AI against a leading general-purpose AI across 43 independent GRC tasks, each reviewed by domain-expert auditors. The tasks included:

• GDPR, CCPA, and CPRA privacy audits
• EU AI Act, Data Act, and sustainability readiness
• Regulatory timelines and applicability analysis
• Framework crosswalks across ISO, NIST, PCI, ETSI, and IEC
• Clause-by-clause delta analyses
• Audit-ready compliance plans

Each task was scored against auditor-defined requirements in two independent passes, with coverage, accuracy, and factual errors tracked explicitly. The results describe this benchmark set and rubric; teams should still validate performance on their own documents and workflows.

Define the method before running the benchmark. Record the task set, expected source universe, scoring rubric, coverage denominator, baseline model and date, and reviewers. Publish the misses as well as the wins.

A fluent answer can still skip an obligation or fail to cite the source. Measure whether each system found the required obligations, cited them, and gave an auditor enough evidence to verify the result.

The benchmark produced a consistent pattern.

Sorena AI

• Reached full coverage against the auditor checklists used in the evaluation
• Grounded answers in primary sources
• Flagged gaps instead of guessing
• Produced reviewable, source-linked outputs

The general-purpose baseline

• Typically covered a fraction of the required obligations
• Missed large portions of the auditor checklists
• Introduced factual errors and unverifiable claims
• Could not prove completeness or coverage

In this evaluation, the baseline often missed obligations, timelines, or controls that auditors expected, even when the answer sounded plausible.

Below is the full breakdown: coverage by category, and every one of the 43 sessions scored by two independent auditors. Sorena coverage is the copilot column; the baseline column is the average of both scoring passes.

Use the table to check whether each answer found the required obligations, cited the source, avoided factual errors, and left a reviewer enough evidence to verify the result.

A general-purpose assistant is designed to generate conversational answers quickly. Sorena AI is designed to track obligations, enforce coverage, ground statements in sources, and produce outputs auditors can verify.

The systems optimize for different jobs.

General-purpose answers can hide errors in audit-critical work:

• Missing obligations are not obvious
• Partial answers look complete
• Errors are hard to detect
• Coverage cannot be proven

Teams may believe the work is complete until an audit, regulator, or customer finds the gap.

A general-purpose assistant still has a role in GRC. It is useful for learning and education, early exploration, drafting ideas that will be validated later, and general assistance outside audit-critical workflows.

When compliance must be complete, grounded, and defensible, execution matters more than answers. Sorena AI provides the system of record and the execution layer GRC requires.

GRC scales when:

• Humans make judgments and decisions
• Systems handle coverage, mapping, tracking, and evidence
• Execution is continuous
• Outputs are verifiable

Benchmark the tools against your own tasks before relying on them.