Trust-scored
confidence scoring and continuous assurance — trust as a graded, computed measure
Description
Volt4AI scores the trustworthiness of data, devices and identities as a graded, continuously-recomputed measure rather than a binary. NQM’s cognitive-security approach dynamically builds confidence from many sources — “security is not black and white; trust is not binary” (cognitive security) — and its continuous assurance model (co-author of NIST SP 1800-36) re-evaluates posture and acts (e.g. revokes access) when confidence falls below threshold. This complements binary provenance (Signed, Supply chain) with a computed confidence score.
Importance
In the real world trust is graded and shifting; a computed confidence score lets a system act proportionately, weight data appropriately, and withdraw trust the moment confidence drops.
Benefit
- Confidence/trust scores on data, devices and identities — not just pass/fail.
- Continuous re-assessment; policy-driven action when a score falls below threshold.
- Combines provenance, validation and behaviour into a single trust signal for downstream AI and decisions.
Defence Relevance
Directly answers “improving trust in data through confidence scoring, provenance and validation” and supports the DTW objective of trusted, NATO-compliant data (SO2). Continuous assurance — anchored in — lets fused ISR data carry a confidence score into the decide step rather than being treated as uniformly reliable.
Civilian & Enterprise Relevance
Zero-trust access scoring, fraud and credit-risk scoring, and data-quality confidence for analytics and AI inputs — increasingly required where automated decisions must be defensible and where AI must know how much to trust its own inputs.
Related
Sources
- Cognitive security: zero-trust on the network
- NQM DTW Response §Technical detail