AI-POWEREDDATA SECURITYDATA PRIVACY
Security and privacy are the two faces of the same coin
- Seeing is not believing but proving is
- Consent you cannot prove is consent you never had
- The face you trust is no longer proof of anything
- Data you cannot find is data you cannot protect
- A perfect forgery still belongs to a stranger
- The safest record is the one you never kept
FaceOff proves what your systems process —verify what is real, where data lives, and evidence it on demand
Advanced AI Breakthroughs In Security
We've distilled complex multimodal intelligence into robust core capabilities. Experience the next generation of real-time synthetic media defense.
Trust Factor Engine (TFE)
Faceoff's engine uses 8 models with dynamic weighting to score trust (1–10), offering nuanced insights beyond binary deepfake detection.
Deepfake Detection
Faceoff is Multi-AI Fusion Architecture, where 8 AI models perform cross-modal trust analysis, outperforming single-modality systems.
Facepay
FaceOff's AI-powered behavioral biometrics ensures real-time, secure fraud prevention across PayTM, BharatPe, GPay, UPI 123Pay, NEFT, and RTGS.
Core platform architecture
Data sources
Multimodal AI analysis
Evidence correlation
Risk assessment
Decision intelligence
Explainable output
Services
DPDP obligations owned
One privacy fabric for the DPDP Act — consent, discovery, DPAR, masking, assessments, breach and audit evidence, all resolving against the same record of where personal data lives.
Consent Management
Collect and honor consent everywhere
Cookie Compliance
Scan, categorize, and block trackers
Intelligent Data Mapper
Find and map personal data automatically
PIA / DPIA Assessment
Assess privacy & AI risk with sign-off
DSAR Management
Fulfill data subject requests on time
Audit & Evidence Management
Turn compliance into audit-ready evidence
Model inventory, EU AI Act assessment and algorithmic due diligence.
Inventory your models, assess them against the EU AI Act and DPDP, and evidence the algorithmic due diligence the Act now requires.
Model Inventory & Lineage
Complete registry of production models, training data provenance & decision workflows.
9 Regimes Assessed Continuously
Automated scoring against EU AI Act, DPDP Sec. 10, NIST AI RMF, ISO 42001 & more.
Explainability & Consensus
Multi-layer feature attribution, bias detection & hallucination guardrails.
Continuous Audit Trail
Cryptographically verifiable records of model decisions for board & regulatory defense.
Data Security & Multimodal AI Ecosystem
Centralized behavioral intelligence, deepfake detection, and sovereign continuous authentication across 7 specialized defense domains.
Core capabilities
What the platform actually does
Key benefits
What changes once it is running
Fraud stopped before it completes
Impersonation is caught at onboarding, in the call and at the payment — while the transaction can still be refused, not in the reconciliation that follows it.
Regulatory exposure reduced
The DPDP Act carries up to ₹250 Cr for a failure of reasonable security safeguards, assessed per instance. Controls that run continuously are what stands between you and that number.
Investigations that hold up
Forensic reports, per-decision detection chains and provenance tiers on every output mean a finding can be defended to a board, a regulator or a court.
One programme, many regulators
One catalogue, one control library and one assessment engine serve every regime you are in scope for, instead of a separate project each time a jurisdiction moves.
Certified & Compliant
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DPDP is not a project. It is a configuration
The Rules were notified in November 2025 and full compliance falls due in May 2027. FaceOff delivers continuous architecture over audit-time scrambles — so controls stay active as laws evolve.
Detection and privacy on one platform
Most vendors sell you one or the other, and you integrate the seam yourself. The estate map that answers a DSAR is the same one your fraud team queries when a face fails a check.
Enforced, not logged
Downstream systems check consent before they process, and acknowledge it. A consent record that nothing reads is a log file, and a log file is not a control.
Evidence as a by-product
The audit trail is produced by running the controls, not assembled the week a regulator writes. Every hop is timestamped, append-only and replayable.
Explainable by construction
Every verdict exposes which of the eight models produced it and which dismissed it, with the per-decision detection chain retained. A finding you cannot defend is not a finding.
Multi-jurisdictional from day one
DPDP, GDPR, CCPA/CPRA, LGPD and PIPEDA run on one control library, so what you build for the 2027 deadline still works when the next law lands.