Key Assumptions Check

Every analytical judgment rests on assumptions — often unstated. This technique surfaces them and evaluates whether the analysis holds if assumptions are wrong.

Procedure

Step 1: State the Analytical Line

Write the current assessment or conclusion in one clear sentence.

Step 2: List All Assumptions

Brainstorm every assumption underlying the assessment. Include:

• Assumptions about threat actor intent or capability
• Assumptions about data completeness or accuracy
• Assumptions about the relevance of historical patterns
• Assumptions about the target environment
• Assumptions about timing or sequencing

Step 3: Walk the Claim Table (when one exists)

If the assessment was built on graded evidence items from /quality-of-information-check, go through the claim table row by row. Assumptions are usually hiding in judgements that the analysis treats as observations. If there is no claim table, skip this step and leave the Claim column as "n/a".

For each claim, compare its claim_type with how the analytical line uses it:

Rules:

• Every assessment and attribution claim with load_bearing: true gets a row in the matrix, even when the assumption looks safe.
• Carry the claim_id into the matrix. One claim can produce more than one assumption, and an assumption can rest on several claims.
• Do not regrade claims here. The grade informs the Confidence column: claim support tentative or worse on a claim used as fact is Low or Medium confidence, never High.
• Assumptions from Step 2 that trace to no claim are kept, with "none" in the Claim column. These have no evidence behind them at all, which is worth stating.

Step 4: Evaluate Each Assumption

Importance: How much does the conclusion depend on this assumption?

• High: If wrong, the conclusion changes significantly
• Medium: If wrong, the conclusion weakens but may still hold
• Low: If wrong, the conclusion is largely unaffected

Confidence: How sure are we this assumption is correct?

• High: Strong evidence supports it
• Medium: Some evidence, but not fully validated
• Low: Little or no evidence; assumed by default

Step 5: Flag Critical Assumptions

Any assumption that is High Importance + Low Confidence is critical. These are the assumptions most likely to invalidate your analysis.

Step 6: Determine Impact

For each flagged assumption:

• What would the conclusion be if this assumption is wrong?
• Can this assumption be validated through additional collection?
• Should the confidence level of the overall assessment be lowered?

Output Template

## Key Assumptions Check: [Assessment Title]

### Analytical Line
[The assessment being checked]

### Assumptions Matrix
| # | Assumption | Claim | Claim type | Access level | Claim support | Importance | Confidence | Status |
|---|-----------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| 1 | ... | C04 | attribution | indirect | tentative | H/M/L | H/M/L | ... |

### Flagged Assumptions (High Importance + Low/Medium Confidence)
1. **[Assumption]** (C04): If wrong, [impact on conclusion]. Collection gap: [what would validate this].

### Impact on Assessment
[Does the assumptions check change the confidence level? Should the conclusion be qualified?]

### Recommended Actions
- [Validate assumption X through Y collection]
- [Lower confidence from High to Moderate because of assumption Z]