SecuPi has announced the launch of its AI Security Access Fabric (ASAF), a unified security architecture designed to govern and secure all AI-driven access to enterprise data, applications, and production systems in real time.
The new framework addresses emerging security gaps created by the rapid adoption of AI agents, copilots, and autonomous workflows that increasingly interact directly with sensitive enterprise environments outside traditional access control models.
SecuPi positions ASAF as a response to a growing enterprise blind spot: the rise of non-human and semi-autonomous systems accessing sensitive data. These include AI agents, copilots, and automated workflows that operate across databases, APIs, and enterprise applications.
Traditional security systems, which were built for human users and static applications, often lack the real-time enforcement needed to monitor and control these dynamic AI-driven interactions.
ASAF introduces a single control plane that unifies identity context, data sensitivity, access policies, and audit logging into one runtime enforcement layer. The system is designed to ensure that all AI-driven actions operate under strict least-privilege and need-to-know principles.
According to SecuPi leadership, this unified approach helps organizations maintain visibility and control even when AI systems interact indirectly with data through service accounts or automated processes.
“AI is bypassing traditional visibility and control mechanisms by utilizing service accounts or indirect access methods,” said Alon Rosental, CEO at SecuPi. “With ASAF, we are providing enterprises with a unified control plane that seamlessly bridges identity context with data sensitivity context, allowing organizations to embrace the AI revolution securely and at scale.”
ASAF is designed to protect multiple high-risk AI access paths across modern enterprise environments. These include analytics agents operating on data platforms, operations agents managing infrastructure, AI-generated applications, and retrieval-augmented generation (RAG) systems accessing enterprise knowledge bases.
In each scenario, ASAF applies runtime governance controls such as data masking, identity-aware tracking, and real-time authorization to prevent unauthorized or excessive data exposure.
The platform consolidates fragmented security tools into a unified architecture that supports fine-grained access control, including row-level and column-level permissions. It also introduces advanced data protection mechanisms such as dynamic masking, tokenization, and de-identification.
Additionally, ASAF provides continuous monitoring and behavior analytics to detect anomalies in real time, while maintaining tamper-proof audit trails that link users, AI agents, and data interactions within a single governance framework.
By centralizing AI access governance, SecuPi aims to reduce operational complexity and eliminate visibility gaps created by service accounts and autonomous systems. The architecture is intended to enable organizations to scale AI adoption while maintaining strict security and regulatory compliance standards.
The introduction of ASAF reflects a broader shift in enterprise security toward governing AI-native systems rather than only human-driven access. By creating a unified runtime enforcement layer, SecuPi aims to help organizations secure the expanding ecosystem of AI agents, applications, and workflows interacting with critical enterprise data.
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