Fraud Taxonomy and Incident Playbooks for Faster Hiring MTTR

A privileged role is moving fast. The hiring manager wants an offer out today. Midway through a live technical interview, the candidate's voice pattern changes, the face on camera intermittently desynchronizes, and the code submission telemetry looks unlike the candidate's earlier screening. Recruiting pauses, Security gets pinged, and suddenly you have an incident with no playbook. If you cannot resolve it quickly, you lose time-to-offer and risk offer-to-start fallout. If you resolve it inconsistently, you create legal exposure. If you resolve it without a defensible record, you create audit liability. 31% of hiring managers report they have interviewed a candidate who later turned out to be using a false identity. That makes hiring fraud a repeatable operational risk, not an exception. And when a mis-hire happens, replacement cost can land in the 50-200% of annual salary range depending on the role. The cost clusters around uninstrumented decisions and slow incident resolution.

Most hiring stacks were not designed as controlled access systems. ATS workflows assume identity. Background checks happen late and sequentially. Coding challenge vendors optimize for scoring, not chain-of-custody. Interview tooling captures video, but not tamper-resistant logs or standardized incident evidence. Operationally, this creates four predictable failure modes: sequential checks that slow everything down, no unified evidence packs, no review-bound SLAs, and shadow workflows that break auditability. If it is not logged, it is not defensible. A decision without evidence is not audit-ready.

Recruiting Ops owns workflow orchestration: stage gates, routing rules, queue hygiene, and SLA monitoring. Security owns access control policy: identity gating requirements, incident severity definitions, evidence retention rules, and escalation paths. Hiring Managers own scoring and rubric discipline: what pass means, structured notes, and technical evaluation consistency. Analytics owns dashboards: time-to-event, SLA breaches, false positives, and segmentation by role and region. Source-of-truth rules: ATS for candidate state and disposition, verification service for identity events, interview and assessment systems for telemetry and rubrics, and Security ticketing for incident handling that references immutable event IDs.

A fraud taxonomy shrinks MTTR only when it is plugged into an instrumented workflow with identity gates, event-based triggers, automated evidence capture, analytics dashboards, and standardized rubrics. Treat interviews and assessments like privileged access. Use step-up verification when risk increases. Route incidents based on concrete signals such as voice mismatch, mismatch-to-ID, capture anomalies, device changes, and plagiarism signals. Track time-to-event, not anecdotes: queue entry, first touch, step-up request, and disposition timestamps.

IntegrityLens AI supports this operating model by acting as the hiring pipeline layer where identity, screening, assessment, and auditability converge: Biometric identity verification establishes an identity gate using liveness checks, face match, and document authentication, with verification typically completed in 2-3 minutes and available before interviews begin. Fraud prevention signals help Security route incidents using corroborated indicators such as deepfake detection, proxy interview detection, and behavioral signals, anchored to an immutable event log. AI-powered screening interviews run 24/7, capturing structured rubric evidence and time-stamped artifacts so decisions are reproducible. AI coding assessments support 40+ languages and add plagiarism detection and execution telemetry to reduce high-score low-ownership outcomes. ATS-anchored audit trails keep approvals, escalations, and disposition reasons in a single source of truth with compliance-ready evidence packs.

Treating a single anomaly as a verdict. One signal should trigger step-up verification, not accusation. Allowing manual exceptions without evidence. Manual review without evidence creates audit liabilities. Letting incidents live in chat. Shadow workflows are integrity liabilities and destroy chain-of-custody.

Step 1 (Security, 48 hours): Define 6-10 incident classes and version the taxonomy. Log taxonomy version, effective date, and approver. Step 2 (Security + Recruiting Ops, 72 hours): Map entry criteria and required corroboration per class. Log rule changes and assumptions to manage false positives. Step 3 (Recruiting Ops owns, Security approves): Set review-bound SLAs by severity tier and route to named queues. Log queue entry timestamp, assignee, and SLA clock reasons. Step 4 (Security owns): Define evidence pack templates per class. Always include triggering event IDs, verification outcomes, rubric notes, and final approver with timestamps. Link evidence packs to ATS records. Step 5 (Recruiting Ops): Implement step-up verification paths for P0-P1 within 2 hours. Log step-up request and outcome. Step 6 (Security + Analytics, monthly): Run a calibration loop. Review MTTR by class, time-in-queue by owner, and escalation clear rate. Log retro notes and playbook changes.

If you want to implement this tomorrow: Assign owners in writing: Recruiting Ops for workflow and queues, Security for policy and evidence, Hiring Managers for rubrics. Pick 6-10 incident classes and define entry criteria plus corroboration rules. No single-signal verdicts. Create review-bound SLAs (P0-P2) and instrument time-to-event: queue entry, first touch, step-up request, disposition. Standardize evidence packs per class and require immutable event IDs in every escalation. Implement step-up verification paths so suspicious routes to re-auth, not ad hoc rejection. Dashboard MTTR and SLA breaches by incident class and owner. Fix queue bottlenecks before tuning detection. Expected outcomes: reduced time-to-hire volatility, defensible decisions via ATS-anchored audit trails, lower fraud exposure through defense in depth, and standardized scoring through structured rubrics.