SecuPi has launched its Enterprise AI Access Fabric, a new solution aimed at addressing runtime security and governance challenges for AI agents and AI-generated applications. The platform is designed to give organizations greater control over what AI systems can access and do, while also protecting sensitive data.
As enterprises deploy more autonomous AI agents into production, many are discovering that traditional governance policies alone are not enough. AI agents can interact with multiple systems, access large volumes of data, and take actions at machine speed. This creates new risks around unauthorized access, data exposure, and compliance violations that existing tools often struggle to manage effectively.
The Enterprise AI Access Fabric combines identity controls with data protection capabilities. It is intended to help organizations enforce policies in real time, rather than relying only on pre-deployment reviews or static rules. According to the company, the solution supports compliance, privacy, and data sovereignty requirements while enabling more secure use of AI agents and applications.
“Organizations today need more than AI governance policies—they need unified runtime enforcement that combine identity and data controls,”
“Our Enterprise AI Access Fabric for AI Runtime Security, AI Agent Security and AI Access Governance enables organizations to control what AI agents, AI-generated applications, and users can access and do, while protecting sensitive data across the enterprise and meeting compliance, privacy and sovereignty requirements.”
Alon Rosenthal, CEO of SecuPi
The launch reflects growing demand for security solutions that can operate while AI systems are actively running, rather than only before deployment.
Conditions Driving the Change
Enterprises are deploying AI agents into production environments where they can access sensitive data and take actions across multiple systems with limited human oversight.
Traditional governance policies are often static and do not provide real-time enforcement when AI agents interact with enterprise resources.
AI agents can move quickly across systems, making it difficult for existing security tools to monitor or control their behavior in real time.
Organizations face increasing regulatory pressure to demonstrate strong access controls, data protection, and accountability around AI systems.
Many current security solutions focus on pre-deployment reviews but lack strong runtime capabilities for AI agents and AI-generated applications.
The combination of identity management and data protection is becoming more important as AI systems gain broader access to enterprise data.
Security teams are being asked to secure AI agents without having tools specifically designed to monitor and control their actions at runtime.
Data sovereignty and privacy requirements are becoming stricter, requiring more granular control over what AI systems can access and where data can flow.
As AI adoption grows, the risk of unauthorized access, data leakage, and policy violations through AI agents is increasing.
There is rising demand for solutions that can enforce consistent security and governance policies across both human users and autonomous AI systems.
What AI Security Looked Like Before
Before dedicated runtime security solutions for AI became more common, organizations largely relied on existing identity, access management, and data protection tools to secure AI systems. These tools were typically designed for human users or traditional applications rather than autonomous AI agents.
As a result, many organizations had limited ability to monitor what AI agents were accessing or what actions they were taking once deployed. Access controls were often broad, and it was difficult to enforce fine-grained policies that followed the behavior of AI systems in real time. This created gaps in visibility and control, especially as AI agents began interacting with sensitive data and critical business systems.
Governance was also mostly focused on pre-deployment stages. Organizations would review models and define policies before launch, but had fewer tools to enforce those policies while AI systems were actively operating. This made it harder to prevent issues such as unauthorized data access or actions that violated internal policies or regulatory requirements.
Overall, AI security in this area was largely reactive and depended on tools that were not purpose-built for the dynamic, autonomous nature of modern AI agents.
What AI Security Looks Like Now
AI security is shifting toward solutions that can provide runtime enforcement and governance for AI agents and AI-generated applications. Platforms like SecuPi’s Enterprise AI Access Fabric are part of a growing category focused on combining identity controls with data protection to manage AI activity while systems are running.
This approach allows organizations to move beyond static policies and toward real-time control. Instead of only defining rules before deployment, companies can now monitor AI behavior, enforce access policies dynamically, and better protect sensitive data as AI agents interact with enterprise systems.
There is also increasing focus on unified controls that apply across both human users and AI agents. This helps organizations maintain consistent security and governance standards as AI becomes more deeply embedded in business processes. Solutions in this space often emphasize the ability to track what AI systems access, what actions they take, and whether those actions align with defined policies.
As AI agents gain more autonomy, runtime security and access governance are becoming essential parts of enterprise AI security strategies.
Our Take
AI Security Take
The launch of SecuPi’s Enterprise AI Access Fabric highlights an important shift in how organizations need to think about AI security. As AI agents move into production and begin taking real actions across enterprise systems, governance policies alone are no longer sufficient. Organizations need the ability to enforce controls while AI systems are actively operating.
For security and governance teams, this means investing in solutions that combine identity management with data protection and can operate at runtime. It also requires clearer ownership of AI access decisions and stronger integration between security, data, and AI teams.
At the same time, many organizations are still early in their journey of securing AI agents. Success will depend on building practical controls that balance security with the need for AI systems to remain useful and effective. Overly restrictive controls can limit the value of AI, while weak controls create significant risk.
Teams evaluating solutions in this space should assess how well any platform can enforce policies in real time, integrate with existing identity and data systems, and provide visibility into AI activity without creating excessive operational friction. As agentic AI continues to scale, organizations that establish strong runtime governance capabilities will be better positioned to manage both the opportunities and the risks that come with autonomous AI systems.
