Deepfence has unveiled new capabilities for its cloud native application protection platform (CNAPP), ThreatStryker. According to Deepfence, ThreatStryker is the first and only platform to provide full traffic and application layer visibility to large language models (LLMs) along with detection and response based on industry-standard frameworks, including OWASP Top 10 and MITRE ATLAS TTPs. Customers now gain the ability to secure and protect workloads across generative AI and API security use cases. This enables them to substantially reduce MTTD and MTTR and write proactive security policies that keep their environment secure.
ThreatStryker addresses the demand for robust security solutions by offering real-time detection and response to adversarial threats targeting GenAI applications. While other solutions are limited to AI Security Posture Management (AISPM), Deepfence is the industry’s first CNAPP to provide deep traffic analysis on full encrypted payloads entering and exiting LLM applications and then map to existing frameworks, ensuring the integrity and confidentiality of sensitive data across networks.
Ryan Smith, Head, Product, Deepfence
Generative AI is transforming business landscapes at a rapid pace, making these applications prime targets for cyberattacks. Deepfence is uniquely positioned to address the complex security challenges posed by these technologies. With deep visibility and standards-based threat detection, ThreatStryker empowers organizations to better secure their digital assets against emerging and sophisticated threats.
Pathik Patel, Head of Cloud Security, Informatica
The eBPF technology offers profound insights into system operations, yet leveraging it effectively has always posed significant challenges for security teams. With the latest advancements in Deepfence's ThreatStryker, we can now not only inspect encrypted traffic but also analyze it for active threats, providing detailed, actionable plans. This transforms ThreatStryker into an essential detection and response tool for emerging use cases, such as GenAI security and workload protection of those Kubernetes clusters. This empowers security teams to execute precise, high-confidence actions. It’s a game changer for those tasked with safeguarding complex environments.
