The Runtime Data Access Control Platform for
The AI Era

SecuPi is the Enterprise AI Access Fabric deployed at major Swiss banks, two of the world’s largest financial services organizations, and leading global telecommunications providers. The platform secures AI agents, AI-generated applications, and privileged users by enforcing identity and data security controls directly where data is accessed. SecuPi combines AI Identity Brokering, AI Access Governance, fine-grained access control (PBAC/ABAC), data discovery and classification, de-identification, AI Runtime Security, real-time monitoring, and tamper-proof auditing to enable secure, compliant, and least-privilege access to enterprise data.

SecuPi eliminates months of engineering work – SecuPi’s Enterprise AI Access Fabric for AI Runtime Security, AI Agent Security, and AI Access Governance enables organizations to securely deploy AI agents, AI-generated applications, and autonomous workflows. The platform combines verified MCP servers, API gateways, AI Identity Brokering, fine-grained access control, data discovery and classification, de-identification, real-time monitoring, and tamper-proof auditing. Built-in tokenization, masking, encryption, authentication management, observability, and security controls reduce deployment time from months to minutes while protecting sensitive enterprise data.

No. Agentic AI refers to AI systems that can autonomously plan, reason, and take actions across tools and workflows to accomplish goals. MCP (Model Context Protocol) is a technical standard that enables those agents to securely connect to external systems and tools. In short, agentic AI is the behavior and capability of the AI, while MCP is the infrastructure layer that makes real-world tool access and action possible. SecuPi uses MCP as part of its agentic AI solution for enterprises.

No. Agentic AI refers to AI systems that can autonomously reason, plan, and take actions to achieve goals. An AI API Gateway is the security layer that enables those systems to securely access enterprise data, APIs, and applications. In short, agentic AI is the intelligence, while the AI API Gateway provides authentication, authorization, monitoring, and governance required for secure enterprise deployment.

All AI clients support MCP such as Claude Desktop, Claude Code, ChatGPT, Cursor and custom applications built with the MCP SDK. SecuPi provides one-click configuration that works across all MCP-compatible clients, eliminating the need to manually configure each tool and ensuring consistent access policies regardless of which AI interface users prefer.

SecuPi enforces least-privilege access on every AI application and tool call using conditional access policies based on user attributes (e.g., role, purpose, location) and data attributes (e.g., sensitivity, classification). The platform includes OAuth, SSO/SAML/SCIM support, thousands of sensitive data classifiers, integrations with all major data catalogs and DSPM tools, password vaults (for identity account brokering), HSM/KMS, audit logs with SIEM integration, and blocks risky actions like lateral movement, privilege escalation, and unauthorized data modifications.

AI Runtime Security protects AI agents, AI-generated applications, copilots, and autonomous workflows while they actively access enterprise systems and data. Unlike traditional AI governance, which focuses on policies and model development, AI Runtime Security continuously enforces identity, authorization, monitoring, and data protection controls during AI execution. It helps organizations prevent unauthorized access, sensitive data exposure, and risky AI behavior in production environments.

AI Agent Security focuses on securing autonomous AI agents that interact with enterprise systems, applications, APIs, and data. It ensures agents operate with least-privilege access, follow approved policies, and cannot access or expose unauthorized information. Key controls include identity verification, fine-grained authorization, activity monitoring, data protection, and auditing of all actions performed by AI agents.

AI Access Governance is the discipline of controlling and auditing what AI agents, applications, copilots, and users can access and do across enterprise environments. It combines identity management, authorization policies, data protection, and auditing to ensure AI systems only access appropriate information. AI Access Governance helps organizations meet security, privacy, compliance, and operational requirements while scaling AI adoption.

AI-generated applications often connect directly to enterprise data using privileged service accounts with broad permissions. This can expose sensitive information beyond what the requesting user should see. Because these applications are frequently developed rapidly, they may lack mature security controls, creating risks related to data leakage, excessive privileges, compliance violations, and insufficient auditing.

Organizations secure AI agents by implementing identity brokering, least-privilege access controls, data classification, de-identification, runtime monitoring, and comprehensive auditing. Fine-grained authorization policies determine exactly what data an agent can access. Real-time monitoring and policy enforcement help detect suspicious activity, prevent data overexposure, and maintain compliance across AI-enabled workflows and enterprise systems.

An Enterprise AI Access Fabric is a unified security architecture that governs how AI agents, AI-generated applications, and users access enterprise data. It combines identity management, fine-grained authorization, data discovery, classification, de-identification, monitoring, and auditing into a single control and de-identification layer. The goal is to enable secure AI adoption while maintaining visibility, compliance, and control over sensitive information.

AI Governance defines policies, standards, risk frameworks, and accountability for AI use. AI Runtime Security enforces those policies when AI systems operate in production. Governance determines what should happen, while Runtime Security ensures it actually happens. Together they provide a complete approach for managing AI risk, security, privacy, compliance, and operational controls.

Enterprises control AI agent access using fine-grained authorization policies that enforce permissions at the object, file, column, row, and cell level. Identity brokering maps AI actions to real users, while contextual policies consider role, location, purpose, and regulatory requirements. Runtime enforcement ensures AI agents only access the specific data necessary to perform approved tasks.

Preventing sensitive data exposure requires discovering and classifying sensitive information, applying de-identification controls such as quantum-resilient tokenization, Format Preserving Encryption, generalization, filtering or masking, and enforcing fine-grained access policies (PBAC/ABAC). Runtime monitoring helps identify risky behavior before data is exposed. Organizations can also restrict access based on user context, business purpose, and regulatory requirements to minimize unnecessary data disclosure.

The largest risks include unauthorized data access, excessive privileges, service-account misuse, sensitive data leakage, compliance violations, and lack of accountability. AI agents may also perform unintended actions, access information beyond their business purpose, or expose regulated data to external systems. Without runtime controls, organizations often have limited visibility into AI activity.

AI-generated applications should be secured using identity-aware access controls, least-privilege credentials using Identity Account Brokering for identity minimization, runtime access authorization, data de-identification, and run-time activity monitoring. Because vibe-coded AI-generated applications are often developed quickly, security should be enforced centrally rather than relying on each application developer or the data platform native policies. Auditing and policy enforcement help ensure secure access to enterprise systems and sensitive data.

AI Identity Brokering ensures AI agents and applications act on behalf of authenticated users for access authorization minimization rather than shared service accounts. The broker maps each request to the actual human identity and grants only the permissions required for that specific task. This improves accountability, reduces privilege exposure, supports Zero Trust principles, and strengthens auditing across AI environments.

Enterprises audit AI activity by recording every interaction between users, AI agents, accounts, data sources, and applications. Effective auditing captures who initiated the request, what data was accessed, what actions were performed, and whether policies were enforced. Tamper-proof audit trails support investigations, compliance reporting, risk management, and governance of AI operations.

AI Model Security focuses on protecting models from threats such as model theft, poisoning, adversarial attacks, and unauthorized modification. AI Runtime Security focuses on protecting data, identities, and business systems accessed by AI during operation. Organizations typically need both capabilities to secure the full AI lifecycle from development through production deployment.

Securing RAG systems requires controlling access to source documents, vector databases, and retrieval pipelines. Organizations should apply identity-aware authorization, data classification, de-identification, and monitoring controls throughout the retrieval process. Runtime enforcement ensures users and AI agents only retrieve information they are authorized to access, reducing the risk of sensitive data exposure.

Yes. Many AI applications use service accounts, NHI or shared credentials with permissions that exceed those of the requesting user. Without identity-aware account brokering and runtime access policy enforcement, AI agents can potentially access data that users themselves could not access directly. This makes identity brokering and fine-grained authorization critical components of secure AI deployment strategies.

Recommended controls include identity verification, least-privilege authorization using Identity Account Brokering, fine-grained access control, data de-identification, tokenization, monitoring, auditing, and real-time policy enforcement. Organizations should also implement risk-based analytics and anomaly detection. Together, these controls help ensure AI agents operate safely while protecting sensitive enterprise information and meeting regulatory obligations.

Zero Trust for AI agents require continuous verification of identities, strict least-privilege access with Identity Account Brokering, contextual authorization runtime access policies, and continuous monitoring of activity. Access should never be assumed trustworthy based solely on network location or application identity. Every AI request should be evaluated dynamically based on user identity, purpose, risk, and policy requirements.

AI Runtime Security is relevant wherever AI systems access enterprise data. This includes Snowflake, Databricks, BigQuery, Microsoft Fabric, Redshift, Oracle, SQL Server, PostgreSQL, data lakes, lakehouses, vector databases, and enterprise applications. As organizations integrate AI into business workflows, runtime controls become increasingly important across both cloud and on-premises environments.

The market for AI Runtime Security and AI Access Governance is emerging rapidly. Organizations evaluate vendors based on capabilities such as AI identity management, runtime authorization, data protection, monitoring, and auditing. Solutions differ significantly in scope, with some focusing on model protection while others emphasize securing AI access to enterprise data and business systems.