Enterprise AI detection
Synthetic media risk is operational: teams need routing, evidence, policy thresholds, and auditability. ZeroTrue gives enterprise risk, security, and product teams a detection layer that can be integrated into existing review queues.
Report example
enterprise risk, security, and product escalation report
- Synthetic identity submissions
- Voice and video impersonation
- Manipulated evidence uploads
- Unlabeled AI-generated content
Search intent
Enterprise evaluation for enterprise risk, security, and product
Primary evidence
Modality-specific confidence scoring, Forensic indicators and timestamps, Metadata and provenance checks
Recommended action
Use confidence scores with source context, policy thresholds, and human review.
Risk model
The highest-value workflow is not a single score. It is a repeatable decision process that separates low-risk submissions, review-worthy anomalies, and clear escalation cases.
- Synthetic identity submissions
- Voice and video impersonation
- Manipulated evidence uploads
- Unlabeled AI-generated content
Operational controls
ZeroTrue can be used as an authenticity signal inside moderation, fraud, onboarding, marketplace, newsroom, and incident-response pipelines.
- Configurable thresholds by modality and policy area.
- Evidence summaries for reviewer queues.
- API-first integration for batch and real-time checks.
Governance
Detection must be transparent enough for appeals, internal review, and legal or compliance teams.
- Preserve score, model version, and evidence in audit logs.
- Document false positive and false negative handling.
- Use confidence bands instead of unsupported certainty claims.
Use cases
Pre-screen high-risk uploads before manual review.
Detect manipulated identity, voice, or product evidence.
Support incident response when synthetic media is reported.
Sample report preview
Media preview
Safe sample, redacted upload, or generated demonstration asset.
Public reports should only expose media that is lawful, consented, and safe to publish.
Confidence
Review priority: high
Synthetic identity submissions
Evidence item linked to score calibration, source context, and known uncertainty.
Voice and video impersonation
Evidence item linked to score calibration, source context, and known uncertainty.
Manipulated evidence uploads
Evidence item linked to score calibration, source context, and known uncertainty.
Unlabeled AI-generated content
Evidence item linked to score calibration, source context, and known uncertainty.
Evaluation table
| Criterion | What to check | Why it matters |
|---|---|---|
| Coverage | Text, image, audio, video, code. | Synthetic media risk rarely stays in one format. |
| Explainability | Score, indicators, timestamps, metadata, limitations. | Reviewers need evidence, not a black-box verdict. |
| Accuracy risk | False positives, false negatives, calibration. | High-impact workflows require documented uncertainty. |
| Workflow fit | API, batch, reports, retention, reviewer queues. | Search traffic must convert into a usable product path. |
Methodology and limitations
How to read the score
Detection output should be read as calibrated evidence. A high score means the observed signals are consistent with synthetic or manipulated media under the current model and sample conditions. It does not prove authorship, intent, or model attribution by itself.
Where review is required
Short samples, heavy editing, compression, translation, re-recording, mixed human-AI content, and unseen generators can reduce confidence. Use human review, source context, and policy thresholds before high-impact enforcement.
Next step
Match the action to the visitor intent: detector pages should lead to a scan, research pages to a downloadable report, enterprise pages to a demo, and developer pages to API keys or playground examples.
FAQ
Can ZeroTrue be used through an API?
Yes. The platform is designed for API-first deployment across text, image, audio, and video review workflows.
How should enterprise teams set thresholds?
Thresholds should reflect policy risk, review capacity, modality, user impact, and the cost of false positives versus false negatives.
Does ZeroTrue replace human reviewers?
No. It is a detection and explanation layer that helps teams triage evidence and make more consistent decisions.
What makes multimodal detection useful?
Fraud and abuse rarely stay in one format. A unified system lets teams inspect text, images, voice, video, and code with consistent reporting.