Clearview AI Builds Deepfake Detector as Trump Administration Ramps Up Quantum Strategy

Deepfakes and Post-Quantum: Two Moves Executives Can't Ignore

Two signals worth your attention: Clearview AI is developing a deepfake detection tool for its law enforcement customers, and the Trump administration is weighing new actions to accelerate U.S. quantum strategy and post-quantum security. The timelines are tight, the stakes are high, and the operational impact will hit budgets, roadmaps, and vendor contracts.

Clearview AI moves into deepfake detection

Clearview AI, known for assembling a massive face image database and selling facial recognition to law enforcement and governments, says it's building a tool to tag AI-generated images. Co-CEO Hal Lambert noted deepfakes aren't yet a major issue for its customers, but the company aims to ship detection capabilities by year-end. Clearview frequently references its performance in NIST testing, which many buyers use as a benchmark.

Why it matters for strategy: as synthetic media spreads, identity verification, evidence handling, and fraud controls are exposed. A single weak link in image provenance or model training data can create legal and operational risk across customer onboarding, content moderation, and investigative workflows.

• Vendor due diligence: Ask for false positive/negative rates on synthetic media, test data composition, red-team results, and how models handle partially edited faces.
• Standards alignment: Confirm support for C2PA provenance signals and watermark readers; require audit logs for every decision.
• Operations: Decide fail-open vs. fail-closed behavior when detection is uncertain; define escalation paths for manual review.
• Privacy and policy: Validate data sources, retention, and jurisdiction handling; ensure consent and lawful basis are documented.
• Benchmarking: Map vendor claims to public evaluations such as NIST's FRVT where applicable (NIST FRVT).

White House signals broader quantum push

The administration is considering executive orders, a national plan on quantum similar to the AI Action Plan, and faster federal timelines for migrating to post-quantum protections. Industry leaders and former officials say interest is serious; announcements could arrive soon, or slip into the coming months.

Why it matters: "Harvest-now, decrypt-later" risk turns today's sensitive data into tomorrow's breach. Expect compliance pressure on crypto agility, procurement checklists that name specific algorithms, and customer demands for concrete migration plans.

• Inventory cryptography: Map where and how your systems use public-key crypto (TLS, VPNs, code signing, PKI, backups, devices). Classify by data sensitivity and lifespan.
• Adopt algorithm agility: Require upgradable crypto in new contracts and architectures. Insist on hybrid modes (classical + PQC) in pilots.
• Align to NIST PQC: Track standardized algorithms and FIPS timelines; start testing endpoints, HSMs, SDKs, and certs with PQC-ready stacks (NIST PQC).
• Governance and budget: Stand up a crypto steering group, define KPIs (coverage, latency, error rates), and earmark 2026 funding for rollout.
• Third parties: Add PQC clauses to vendor contracts; verify roadmaps from cloud, CDN, SASE, MDM, and device suppliers.

90-day execution checklist

• Deepfake readiness:
  • Select 1-2 detection vendors for pilots; define thresholds and escalation rules.
  • Add provenance checks (C2PA) to image/identity workflows; log decisions end-to-end.
  • Update incident response to cover synthetic media misuse and evidence handling.
• Post-quantum track:
  • Complete crypto inventory and risk ranking; pick two systems for PQC pilot (e.g., internal service mesh and external API).
  • Test hybrid key exchange in staging; measure performance impact and certificate chain issues.
  • Insert algorithm-agility requirements into new RFPs and renewals.

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