Your DLP tools can't see what's inside an image. StegaShield uses machine learning to detect data exfiltration hidden in media files before it leaves your network.
Signature-based tools scan for known patterns. A JPEG carrying exfiltrated trade secrets still passes because it looks identical to any other JPEG. Steganographic techniques leave no visible trace for rules-based systems to catch.
Generative models now allow attackers to embed secrets in images that are statistically indistinguishable from clean files. The threat is no longer theoretical.
CMMC 2.0, NIST SP 800-171, SEC cybersecurity disclosure rules, and NYDFS Part 500 are all driving scrutiny of covert channel controls. Organizations need documented evidence they're watching this attack surface.
In a documented GE insider case, an engineer exfiltrated sensitive IP by hiding it inside an image file.
StegaShield sits at egress points across your environment, including email gateways, web proxies, cloud storage, and file shares, scanning media files as they egress.
StegaShield intercepts media files at configured egress points via REST API without re-architecting your existing security stack.
→The engine analyzes noise patterns, pixel and byte-level deviations, frequency anomalies, and entropy signatures using custom ML models.
→Stream detections to Splunk, Elastic, Microsoft Sentinel, or Cortex XSOAR with a full forensic audit trail.
StegaShield addresses an ignored category of exfiltration with the integrations, audit trails, and deployment flexibility that enterprise security teams require.
Detects manipulations across compressed and transformed file formats.
Every detection generates a tamper-resistant log with anomaly scores and full chain-of-custody metadata.
Deploy as an inline proxy, background scanner, cloud microservice, or Docker container. Integrates with existing email gateways, web proxies, and cloud storage via REST API.
Detections surface directly in Splunk, Microsoft Sentinel, Elastic, and Cortex XSOAR. Outputs standard log formats (JSON) for correlation with broader threat intelligence.
StegaShield is purpose-built for security leaders and practitioners in financial services and defense, where the cost of a covert exfiltration event is existential.
Trading systems, customer PII, and M&A data are high-value exfiltration targets. SEC cybersecurity disclosure rules, FINRA, SOX, and NYDFS Part 500 demand comprehensive data protection. StegaShield closes the media file gap for tier-1 bank SOC teams.
Steganographic exfiltration is a documented nation-state technique targeting CUI and classified-adjacent data. CMMC 2.0 and NIST SP 800-171 compliance creates structured demand for exactly this detection capability.
Security leaders who already run DLP, IDS, and endpoint security, and seek to close this covert channel before it becomes a regulatory or board-level incident.
Managed security providers and cloud security platforms can embed StegaShield via API to add a differentiated detection layer to their existing managed offering.
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Your DLP tools can't see what's inside an image. StegaShield uses machine learning to detect data exfiltration hidden in media files before it leaves your network.
Signature-based tools scan for known patterns. A JPEG carrying exfiltrated trade secrets still passes because it looks identical to any other JPEG. Steganographic techniques leave no visible trace for rules-based systems to catch.
Generative models now allow attackers to embed secrets in images that are statistically indistinguishable from clean files. The threat is no longer theoretical.
CMMC 2.0, NIST SP 800-171, SEC cybersecurity disclosure rules, and NYDFS Part 500 are all driving scrutiny of covert channel controls. Organizations need documented evidence they're watching this attack surface.
In a documented GE insider case, an engineer exfiltrated sensitive IP by hiding it inside an image file.
StegaShield sits at egress points across your environment, including email gateways, web proxies, cloud storage, and file shares, scanning media files as they egress.
StegaShield intercepts media files at configured egress points via REST API without re-architecting your existing security stack.
→The engine analyzes noise patterns, pixel and byte-level deviations, frequency anomalies, and entropy signatures using custom ML models.
→Stream detections to Splunk, Elastic, Microsoft Sentinel, or Cortex XSOAR with a full forensic audit trail.
StegaShield addresses an ignored category of exfiltration with the integrations, audit trails, and deployment flexibility that enterprise security teams require.
Detects manipulations across compressed and transformed file formats.
Every detection generates a tamper-resistant log with anomaly scores and full chain-of-custody metadata.
Deploy as an inline proxy, background scanner, cloud microservice, or Docker container. Integrates with existing email gateways, web proxies, and cloud storage via REST API.
Detections surface directly in Splunk, Microsoft Sentinel, Elastic, and Cortex XSOAR. Outputs standard log formats (JSON) for correlation with broader threat intelligence.
StegaShield is purpose-built for security leaders and practitioners in financial services and defense, where the cost of a covert exfiltration event is existential.
Trading systems, customer PII, and M&A data are high-value exfiltration targets. SEC cybersecurity disclosure rules, FINRA, SOX, and NYDFS Part 500 demand comprehensive data protection. StegaShield closes the media file gap for tier-1 bank SOC teams.
Steganographic exfiltration is a documented nation-state technique targeting CUI and classified-adjacent data. CMMC 2.0 and NIST SP 800-171 compliance creates structured demand for exactly this detection capability.
Security leaders who already run DLP, IDS, and endpoint security, and seek to close this covert channel before it becomes a regulatory or board-level incident.
Managed security providers and cloud security platforms can embed StegaShield via API to add a differentiated detection layer to their existing managed offering.
StegaShield is available for download from DockerHub and GitHub.