Verification Audit

Verification Audit

Systematically cross-verify every major finding in an intelligence dossier against independent sources. Downgrades confidence, retracts unverifiable claims, and documents source reliability. Ensures the dossier is trustworthy enough to inform real decisions.

When to use

• After completing a major intelligence gathering round
• Before delivering a dossier to a decision-maker
• When findings from data aggregators (ZoomInfo, RocketReach, Prospeo, SignalHire) need validation
• When a critical finding seems too good (or too bad) to be true
• After an agent reports entity registrations, corporate relationships, or financial data

The problem

Intelligence dossiers built from OSINT sources accumulate claims from data aggregators, people-intelligence platforms, and AI research agents. These sources have different reliability levels:

The biggest risk: Aggregator claims treated as facts without independent verification.

Workflow

Phase 1: Inventory all major findings

Create a table of every finding that was integrated into the dossier, noting:

• The finding itself
• The original source(s)
• Whether it was verified against a second independent source
• Current confidence level

Phase 2: Verify against independent sources

For each finding type, use the appropriate verification method:

Phase 3: Confidence tiering

Assign every finding a confidence level:

Phase 4: Document retractions

When retracting a finding:

1. Strike through the original text (don't delete — preserve the audit trail)
2. Add "RETRACTED (date)" with the reason
3. Note what verification was attempted and what failed
4. Update every file where the retracted finding was placed

Phase 5: Methodology note

Document the systemic patterns discovered during verification:

• Which source types proved reliable vs. unreliable
• Which aggregators fabricated data
• What verification methods were most effective
• Recommendations for future research rounds

Output template

# Verification Audit

## RETRACTED FINDINGS
| Finding | Source | Verification attempt | Result |
|---------|--------|---------------------|--------|
| ... | ... | ... | RETRACTED — [reason] |

## VERIFIED FINDINGS (high confidence)
| Finding | Sources | Confidence |
|---------|---------|------------|
| ... | ... | Confirmed/High |

## CONFIDENCE DOWNGRADES
| Finding | Was | Now | Reason |
|---------|-----|-----|--------|
| ... | High | Medium | [reason] |

## METHODOLOGY NOTE
[Systemic observations about source reliability]

Real-world example

In a June 2026 dossier audit:

• 4 entity registrations retracted (Brogav Properties LLC, Brogav Books LLC, Break The Ice LLC, BROGAV AI Builders Foundation) — reported by a research agent, zero results on IRS EOS, ProPublica, or any public database. Likely data-aggregator hallucinations.
• 1 finding partially rehabilitated — Brogav Books LLC was later discovered to be plausible when a real published book ("Technoville Adventures") was found on Amazon with BROGAV Solutions LLC as publisher. The retraction was updated to note this.
• Salary data downgraded from "new intelligence" to "low-medium confidence" — Indeed reported a $154K "Strategic Project Manager" role from a single anonymous self-report with no corroboration.

The audit caught the AI research agent's tendency to treat aggregator output as confirmed fact — the same error that put 4 fictional entities into 4 separate dossier files before correction.

Integration with other skills

• intelligence-dossier — verification audit is the QA step before delivery
• conversation-review — complementary; conversation-review checks completeness, verification-audit checks accuracy
• oem-partner-verification — specific application of this methodology to supplier claims