Accuracy & Confidence in Findings

Confidence Levels

BTP assigns confidence levels to findings based on the detection method and validation process. These confidence levels help users understand the reliability of each finding. Confidence levels are shown the findings grids and in the details of each finding. They can also be found in the majority of the reporting and SBOM outputs.

Confidence Level Scale

Confidence	LevelPercentile	ValueNumerical	ValueDescription
certain	81% - 100%	0.81 - 1.0	Highest confidence, thoroughly validated findings
somewhat_certain	61% - 80%	0.61 - 0.8	High confidence with minimal false positive potential
somewhat_uncertain	41% - 60%	0.41 - 0.6	Moderate confidence, may require manual validation
uncertain	21% - 40%	0.21 - 0.4	Lower confidence, higher false positive potential
very_uncertain	1% - 20%	0.01 - 0.2	Lowest confidence, requires thorough manual verification

Calculation Methods

The confidence levels are determined through different methods depending on the type of detection:

Known Vulnerabilities: Tested on large datasets with verified results, typically achieving “certain” confidence levels
Secret Scanning: Uses regex-based rules, typically assigned “somewhat_uncertain” due to potential false positives
UEFI Unknown Detection: Validated through large-scale analysis of Dell firmware, achieving ~95% precision after manual analysis

JSON Example

{
  "component_id": "7e0a0a8e-2bd5-4400-97b6-de1125562f67",
  "name": "suspicious/posix/entrypoint",
  "confidence": 0.8,
  "kind": {
    "kind": "finding",
    "value": {
      "severity": "low",
      "identifiers": [],
      "classifications": [],
      "metrics": [],
      "predicates": [],
      "description": "The entry point of the ELF binary has been potentially modified",
      "evidence": []
    }
  }
}

Key Considerations

The confidence levels are assigned based on:

Detection method reliability
Historical accuracy of the detection mechanism
Potential for false positives
Need for manual validation
Complexity of the detection process

These confidence levels are continuously refined based on feedback and validation results to improve accuracy over time.

Detection

Findings

Risk Assessment

Analysis Engine

Compliance

Confidence Levels

Confidence Level Scale

Calculation Methods

JSON Example

Key Considerations

​Confidence Levels

​Confidence Level Scale

​Calculation Methods

​JSON Example

​Key Considerations

Confidence Levels

Confidence Level Scale

Calculation Methods

JSON Example

Key Considerations