Who needs EVIDE. And why now.
EVIDE is not a logging system. It is an external evidentiary infrastructure that transforms AI-assisted decisions into verifiable, attributable and legally defensible records. These are the sectors where the absence of that infrastructure is already creating measurable risk.
Technical depth: → Understand the architectureInsurance & Underwriting
AI is reshaping underwriting, claims assessment and fraud detection in insurance. Each of these decisions carries regulatory exposure, customer dispute risk and potential litigation. The question regulators and courts are beginning to ask is not whether AI was used — but whether the human who reviewed the AI output was operating within a documented, defensible structure.
- A denied claim litigation cannot produce a structured record of human review against a defined criterion
- The underwriting taxonomy exists internally but was not anchored externally at the time of the decision
- Different underwriters applied different interpretations of the same risk threshold — no audit trail
- Regulatory review of AI-assisted claims processing finds no independent evidentiary record
- Each underwriting or claims decision is anchored externally with taxonomy_reference and threshold_reference at the moment of review
- Procedural shield: defense is built on a verifiable record, not on internal reconstruction
- Consistency across underwriters becomes measurable and documentable over time
- Regulatory compliance is demonstrated through external evidence, not internal attestation
Banking & Financial Services
Credit decisions, risk assessments, fraud detection, KYC/AML reviews — every day, financial institutions make thousands of decisions partially or entirely driven by AI. Regulators are asking a question that most institutions cannot yet answer:
- Supervisory review finds no structured record of human oversight on credit decisions
- Regulatory audit cannot reconstruct which version of a risk taxonomy was in use at time of decision
- A disputed loan rejection cannot be defended without a documented intervention trail
- Internal logs exist but are self-declared — they cannot survive independent scrutiny
- Every AI-assisted credit or risk decision carries a taxonomy_reference and threshold_reference anchored at the moment of review
- Classification replay: auditors can reconstruct the exact governance context of any past decision
- Procedural shield: defense is built on documented structure, not on reconstructed narrative
- not_defined signals show where policy gaps exist — before regulators find them
HR & Recruitment — AI Act High-Risk Category
Under the EU AI Act, AI systems used for recruitment, promotion and personnel decisions are classified as high-risk. This means mandatory human oversight — but the Act does not define what "demonstrable" oversight looks like. EVIDE does.
- AI Act audit finds no evidentiary trail of human supervision on AI-assisted screening decisions
- A discrimination complaint cannot be rebutted because the internal taxonomy is not externally verifiable
- Reviewers applied different standards to similar cases — no record exists to demonstrate consistency
- The override was logged internally but the rationale is not structured or replayable
- Each candidate decision links the human reviewer to a specific taxonomy and threshold — both externally verifiable
- threshold_status: not_met documents exceptions explicitly — turning compliance risk into documented process
- Inter-reviewer consistency becomes measurable over time through classification_status and rationale_type
- AI Act Article 14 compliance: demonstrable human oversight, not declared oversight
It cannot demonstrate:
which taxonomy was active, which threshold was defined, whether the reviewer operated within a structure or not.
Decision tracked. Not defensible.
"taxonomy_reference": "HR_Eval_v3.2",
"threshold_reference": "AI_ACT_ART14",
"threshold_status": "met"
}
threshold_status: not_defined does not accuse the reviewer. It demonstrates that at the moment of the decision no upstream threshold was defined — a governance gap, not individual failure. Healthcare & Clinical Decision Support
AI is increasingly present in diagnostic support, treatment recommendations and triage prioritization. In every case, clinical responsibility remains human. But the evidentiary gap between "a doctor reviewed it" and "a doctor reviewed it against a defined clinical protocol under documented authority" is enormous.
- An adverse outcome review cannot demonstrate that the clinician reviewed against a defined protocol
- Liability is attributed to the individual reviewer rather than to a governance gap in the institution
- Different clinicians applied different thresholds to equivalent cases — no consistent record exists
- The clinical AI vendor provides system logs — but those logs are internal and self-declared
- Each clinical decision carries taxonomy_reference (internal protocol) and threshold_reference (external guideline) — both anchored independently
- Institutional liability is separated from individual clinician liability through structured attribution
- Classification replay allows any past decision to be re-examined against the exact protocol version in force at the time
- not_defined signals reveal where clinical governance is missing — before an incident occurs
Legal, Compliance & Professional Services
Law firms, DPOs and compliance consultants operate across multiple organizations, multiple regulatory frameworks and multiple risk profiles. For them, EVIDE solves a structural problem: the need for a single evidentiary layer that works identically regardless of the sector, the client or the applicable regulation.
- Each client uses different internal systems — creating inconsistent evidentiary standards across the portfolio
- AI governance documentation is self-declared and lives inside the client's own systems — it cannot be independently verified
- A regulatory inquiry requires demonstrating oversight across multiple decisions — reconstruction takes weeks
- Different clients have different levels of governance maturity — no structured way to measure or communicate this
- A single EVIDE integration works across all clients, all sectors, all regulatory frameworks — the schema is domain-agnostic
- External evidentiary anchoring: records exist outside the client's own systems and cannot be altered after the fact
- threshold_status across a client portfolio reveals governance maturity — structurally, not anecdotally
- When regulators ask: the answer is not a document. It is a verifiable record with a hash and a timestamp.
The common pattern across all sectors
In every sector, the same structural gap exists: governance defines what should happen. Logs record that something happened. But neither can demonstrate, independently and verifiably, that a decision was made within a defined structure by an identified human authority.
EVIDE does not replace governance. It makes governance defensible.
If your organization uses AI to support decisions that carry legal, financial or human consequences — the question is not whether these decisions need evidentiary anchoring. The question is whether you want that gap to emerge in an audit, a dispute, or after deployment.