The previously bootstrapped company helps organizations securely and reliably operate AI agents at scale. The post Prevalent AI Raises $22 Million to Expand Data Fabric Platform appeared first on SecurityWeek.

Prevalent AI, a company focused on enabling the secure and reliable operation of AI agents at scale, has announced a $22 million funding round. This investment marks a significant milestone for the company, which had previously been bootstrapped, indicating a strategic shift towards accelerating its growth and platform development.
The core offering from Prevalent AI centers on a data fabric platform designed to support the operational requirements of artificial intelligence agents. In the context of AI, a data fabric typically refers to an architectural layer that integrates data from various sources, making it accessible and manageable for AI models and agents. This integration is critical for AI systems that often require diverse and high-volume data streams to function effectively and make informed decisions.
A key aspect of Prevalent AI's platform is its emphasis on security and reliability. Operating AI agents at scale introduces numerous challenges related to data integrity, access control, and the consistent performance of AI models. Security in this domain often involves ensuring data used by AI agents is protected from unauthorized access or tampering, while reliability pertains to the continuous availability and accurate functioning of the agents themselves, even as data sources or operational environments change.
The expansion plans, fueled by the new funding, are likely to focus on enhancing the capabilities of this data fabric platform. This could involve developing new features for data governance, improving integration with a wider range of enterprise data sources, or bolstering the platform's ability to monitor and manage the lifecycle of AI agents. For organizations deploying AI, a robust data fabric can streamline data preparation, reduce operational overhead, and provide a more secure foundation for AI initiatives.
For technical readers, the implications of such a platform are significant. As AI adoption grows across industries, the complexity of managing AI agents—from development and deployment to ongoing monitoring and maintenance—escalates. A data fabric can abstract away much of this complexity, providing a unified view and control plane for the data that feeds these agents. This can lead to more efficient AI operations, better data quality for AI models, and ultimately, more trustworthy AI outcomes.
The investment underscores a growing market demand for specialized infrastructure that addresses the unique challenges of enterprise AI deployment. As companies move beyond pilot projects to integrate AI deeply into their operations, the need for platforms that can manage AI agents securely, reliably, and at scale becomes paramount. This funding round positions Prevalent AI to capitalize on this expanding need, contributing to the broader maturation of the AI operational technology landscape.
A weakness has been identified in Tenda CP3 27.5.57.101. This issue affects some unknown processing of the file Net/NetCheckPing.cpp. This manipulation of the argument interface_name/host causes os command injection. The attack can be initiated remotely.
A security flaw has been discovered in Tenda CP3 27.5.57.101. This vulnerability affects the function SystemAsh of the file Apis/system.c of the component Kylin. The manipulation of the argument AlarmVoiceURL results in os command injection. It is possible to launch the attack remotely.

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In BPF instructions that load/store a value from/to a scratch memory register the register index is an unsigned 32-bit integer and must not exceed 15, but libpcap BPF interpreter does not validate the value. In particular uncommon use cases a crafted filter program can cause the interpreter to try reading and writing the OS process memory in the 16GiB starting at the current stack frame on 64-bit architectures and in the entire address space on 32-bit architectures.

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