Other AI-generated threat security rely on multiple, isolated tools for each media channel, creating data silos and leaving gaps in your organization’s threat landscape.
They myopically see only one element of the threat. More importantly, they are unable to bring together the critical data across media necessary to detect deepfakes.
Our single, unified platform brings together data from every form of communication—email, messaging, file transfers, and more—eliminating the blind spots and fragmented insights caused by point solutions. This integration delivers continuous, organization-wide awareness, helping you maintain vigilance over your entire environment.
Most point solutions analyze only a single channel or context, missing correlations and failing to provide a holistic picture. By consolidating all security-relevant data into one platform, you gain a comprehensive perspective—connecting dots across diverse data streams and communication vectors for powerful, real-time insights.
Complete Data Consolidation:
All communication and event data converge in a single location, erasing information gaps between systems and sources.
Cross-Channel Threat Detection
Unified visibility allows detection of attack patterns or risks that span multiple communication types, which point solutions can easily miss.
Continuous Awareness:
Real-time aggregation ensures your security posture is always based on the latest data, regardless of where or how threats emerge.
Elimination of Data Silos:
By unifying information from every communication channel, the platform prevents the blind spots and delays common with fragmented toolsets.
Built to aggregate and normalize inputs from all monitored channels, our platform provides an always-current picture of your organization’s security landscape.
Integrated Data Streams:
Captures and combines emails, chats, file activity, and more, making every relevant data point instantly available for analysis.
Centralized Event Repository:
All events and alerts are normalized and stored in a unified repository, enabling holistic threat assessment.
Unified Monitoring Interface:
Review and analyze security events across all channels from a single dashboard, removing the need to shift between disconnected tools.
Consistent Intelligence Application:
Detection models and policies are enforced across every data stream, ensuring no threat goes unnoticed due to siloed processing.
Imagine a scenario where a deepfake video is being circulated, purportedly showing a high-ranking executive making controversial statements. Detecting this deepfake requires analyzing data from multiple communication channels and media types to identify inconsistencies and verify authenticity.
The system uses advanced AI models to analyze the video itself. It examines:
Facial Movements:
Identifying unnatural facial expressions or mismatched lip-syncing.
Lighting and Shadows:
Detecting inconsistencies in lighting that suggest tampering.
Pixel-Level Artifacts:
Spotting irregularities in the video compression or rendering.
Step 2: Audio Analysis
The audio track is separated and analyzed independently:
Voice Matching:
Comparing the voice in the video to known samples of the executive’s voice using voice biometrics.
Background Noise:
Identifying anomalies in ambient sounds that don’t align with the supposed environment.
Speech Patterns:
Detecting irregularities in tone, pitch, or cadence that deviate from the executive’s typical speech.
The system integrates data from other communication channels to validate the context:
Email Records:
Checking if the executive sent any emails or messages referencing the event in the video.
Calendar Data:
Verifying if the executive was scheduled to be in the location shown in the video at the time.
Social Media Activity:
Analyzing posts or interactions to see if they align with the claims made in the video.
The system examines metadata from the video file:
Timestamp Analysis:
Ensuring the creation date matches the claimed timeline.
Geolocation Data:
Verifying if the video was recorded in the stated location.
Editing History:
Detecting signs of post-production or manipulation.
Step 5: Behavioral Analysis
The system cross-references behavioral data:
Communication Patterns:
Identifying if the executive’s recent communications align with the tone or content of the video.
Network Activity:
Checking for unusual activity on the executive’s accounts that could indicate a compromise.
By integrating data from these diverse sources, the system identifies multiple red flags:
The voice in the video doesn’t match the executive’s known voiceprint.
Metadata reveals the video was edited and uploaded from an unverified source.
Cross-channel data shows the executive was in a different location at the time.
The system flags the video as a deepfake, preventing its spread and protecting the executive’s reputation.
