Detecting Deepfakes and Doctored Documents
A customer needed to test AI-driven image forensics tools that could detect doctored documents, deepfakes, and synthetic images used in fraud and cyber incidents.
Outcomes
60%
Faster Incident Remediation
80%
Automated Workflow
1 Click
Forensic Analysis
50%+
Reducing Manual Workload
Technology Vendors Suited to Evaluation
Business Problem
Incident forensics in the cloud requires speed and precision to replace manual investigation and augment legacy on-premise tools.
The bank needed to validate solutions that could automatically analyse disk and memory images, streamline investigations, and integrate seamlessly into a unified incident-response platform.
Challenges
- Delayed Incident Response – Manual forensic processes slow threat mitigation.
- Visibility Gaps – Lack of automated cloud environment monitoring leads to missed threats.
- Resource Constraints – Security teams struggle with manual analysis of compromised cloud instances.
- Compliance Risks – Failure to conduct timely forensic investigations can violate regulations.
- Lack of Automation – Without AI and tagging, forensic workflows are inefficient.
From Idea to Evidence with NayaOne
The financial institution and vendors collaborated in a single secure workspace to co-develop and validate a new cloud forensics solution in just four weeks. The vendors were deployed in a controlled cloud environment to simulate real-world incident conditions. Forensic investigations were triggered using tagging and AI-based analytics, while performance was measured across speed, efficiency, compliance impact, integration, and usability – providing clear, evidence-based validation before production deployment.
Impact Metrics
PoC Timeline Reduction
4 weeks with NayaOne vs 12 – 18 months traditionally
Time Saved in Vendor Evaluation
1+ year
Decision Quality
The bank gained hard evidence on detection accuracy, speed, and integration fit - enabling a data-driven vendor choice and faster approval across risk and procurement.
KPIs
- Model detection accuracy (%) across test scenarios.
- False-positive / false-negative rate.
- Avg time from image ingestion to classification (latency).
- Time saved per incident vs. manual review baseline.
- Compliance adherence (no data exposure events).
- Number of vendors benchmarked and validated.
Detect Fraud Before It Reaches Production
Validate forensic AI vendors under real-world conditions without risk to live systems. Benchmark detection accuracy and response workflows across multiple tools.
