Identity-Centric AI Security: Building a Compliant and Controllable Foundation for Enterprise AI Adoption

As AI rapidly moves from experimentation into core enterprise workflows, its business value is becoming undeniable. At the same time, security and compliance risks are expanding at an equal pace. Based on extensive practices, Paraview observes that the biggest barrier to large-scale AI adoption is not technology capability, but the lack of a solid security and compliance foundation. This article examines where AI security risks originate and how an identity-first approach can ensure secure and compliant AI deployment.

Introduction

In the enterprise world, the most realistic concern shared by CIOs, CISOs, and compliance leaders is: as AI becomes deeply embedded into business processes, the value it brings is significant, but the associated security and compliance risks can multiply, even exponentially.

Through extensive practices, Paraview has seen a consistent pattern: when AI initiatives stall, it is rarely due to insufficient technology. More often, it is because the security and compliance foundation is not strong enough, leaving organizations unwilling to fully open up the AI usage.

So where do these risks come from, and how should enterprises respond?

AI Security Risks

1. Data Input: Boundary Breakdown and Data Intermixing

AI models thrive on large volumes of high-quality data. In enterprise environments, this data often originates from internal systems, external platforms, and third-party services, resulting in extensive cross-system data flows.

This fundamentally breaks the traditional assumption of “secure boundaries and trusted internal environments.” Data intermixing, sensitive information exposure, and unauthorized data collection can all occur, introducing new and systemic risks.

2. The Model Itself: A New Primary Attack Target

In traditional systems, attackers target servers. In the AI era, attackers may target the model itself， manipulating it to leak sensitive information.

Prompt injection, data poisoning, unauthorized invocation, and model inversion are no longer theoretical risks; they are real-world incidents. The AI model has become a core attack surface.

3. AI Output: Bias and Compliance Violations

Without proper access control and audit mechanisms, AI outputs may unexpectedly:

• Expose sensitive data
• Generate biased or non-compliant decisions
• Trigger cross-border compliance violations
• Output unauthorized internal information

These issues are now a global regulatory focus, directly translating into enterprise risk.

This is why AI security is no longer just about “preventing attacks.” It is about end-to-end identity and compliance management across inputs, models, and outputs.

The Solution: A Zero-Trust IAM Architecture for Enterprise AI

Taking a closer look at the global regulations, from GDPR in Europe to CCPA in the United States, we can see common focused requirements:

• Verifiable identity (ensuring the caller is trusted)
• Least privilege access (only the necessary data can be accessed)
• Full auditability and traceability (every action is accountable)

All of these requirements converge on one core capability: Identity and Access Management (IAM).

In this context, an identity-centric Zero Trust IAM architecture becomes the cornerstone of enterprise AI governance. By unifying identity management, access control, API governance, and data authorization models, enterprises can build a security foundation that covers human users, business systems, and AI agents. The overall approach can be summarized as: “Establish identities first, define boundaries second, and enforce monitoring and accountability last.”‍

1. AI Identity Governance

At the identity layer, AI is elevated from a “tool” to an independent digital subject governed under the enterprise IAM framework. Through AI identity governance, entities such as Agents and MCPs are registered or automatically discovered, assigned unique identifiers, keys, and credentials, and strongly bound to accountable human owners.

With Paraview AI IAM solution, all AI identities are managed throughout a full lifecycle, from registration and activation to authorization, privilege convergence, and decommissioning, ensuring there are no “ownerless AI” or unmanaged agents.

By mapping AI identities to organizational structures, roles, and system accounts, enterprises gain a three-dimensional identity view across people, AI, and systems.

2. Dynamic AI Business Authorization

At the authorization boundary, AI access control is rebuilt using Zero Trust principles combined with ABAC (Attribute-Based Access Control).

Each invocation is evaluated in real time based on:

• Caller identity (human, system, or agent)
• Task type
• Data sensitivity level
• Environmental risk context

The system dynamically decides whether to allow access, at what granularity, and whether human approval is required. For high-risk or novel operations, a human-in-the-loop mechanism is automatically triggered, forming a closed-loop control model where AI executes tasks with human oversight as a safeguard.

All policies are centrally orchestrated and can be differentiated by business domain, data domain, and regional regulatory requirements.

3. AI Access Interface Control

At the interface layer, all AI invocations are centrally proxied through an AI service interface control layer and an AI gateway. Whether an agent calls internal business APIs or an MCP accesses external LLM services, all requests must pass through this control plane for identity authentication, authorization, and token propagation.

The AI gateway centrally manages model keys and credentials, acts as a Policy Enforcement Point (PEP), and prevents direct bypass connections. Invocation frequency, target resources, and response data are strictly governed and audited to avoid privilege sprawl and abuse in automated scenarios.

4. AI Data Access Control

At the data layer, a dedicated AI data authorization model enables fine-grained control at the table, column, and record levels. Data is classified by sensitivity, with each level mapped to specific pre-access requirements, such as strong authentication, multi-factor authentication, or human approval, and different presentation methods, including masking, aggregation, or read/write restrictions.

When AI accesses enterprise data through RAG, vector search, or knowledge bases, Paraview’s data access proxy dynamically rewrites queries, injects filters, and masks output fields based on identity and attributes carried in tokens. This ensures that what AI can see is strictly limited to what it is authorized to access.

For data export and batch processing, watermarks and export traces are automatically applied to support post-incident accountability.

5. Security Operations and Compliance Oversight

Finally, at the operations and compliance level, an AI-focused monitoring and observability framework is established.

A unified audit platform records every critical AI action, who accessed what data, at what time, through which interface, and under which identity and policy. Observability components visualize invocation paths, response outcomes, and policy hits, helping security teams detect anomalies such as abnormal call volumes, privilege escalation trends, or signs of prompt injection.

With configurable alerts and reporting, enterprises can provide regulators and auditors with a complete, coherent, and verifiable evidence chain of AI behavior.

Conclusion

With this comprehensive approach, enterprises can ensure that AI operates within clearly defined compliance boundaries, meeting regulatory requirements such as GDPR and CCPA, for verifiable identity, least privilege, and auditability. More importantly, it enables organizations to confidently scale AI use cases, turning secure, controllable, and accountable AI into a sustainable engine of productivity.