The Evolution of Authorization and Access Control – Implications for GenAI Applications

In cybersecurity, authorization and access control have long been cornerstones for protecting data, systems, and users. The concept of right data to right users has been a big part of how companies have tried to design their systems. These concepts were initially designed for mainframe environments, then adapted for client-server systems, on-premises big data, and later for distributed cloud computing. Each evolution addressed new challenges, but generative AI now pushes these frameworks even further, requiring significant modifications to meet its unique demands. With the rise of Generative AI (GenAI) applications, we are witnessing a shift in how access control and authorization must be approached. The introduction of AI-driven systems brings new challenges that require rethinking conventional methods.

This blog delves into the history of authorization and access control, and explores how it has evolved in the context of GenAI applications.

1. The History of Authorization and Access Control

1970s and 80s – Simple Access Control Mechanisms

The genesis of authorization and access control is rooted in the need to  secure multi-user systems. At this point, access control methods were very rudimentary and based on user credentials like usernames and passwords.

• Discretionary Access Control (DAC): One of the earliest models, DAC allowed users to control access to the data they owned. The owner could decide who else had the right to access their data, often using permissions (e.g., read, write, execute).
• Mandatory Access Control (MAC): In contrast to DAC, MAC was more advanced as it enforced stricter policies. It was commonly used in military or government applications, where classifications and clearances with predetermined access. This model introduced the concept of a central authority that dictated who had access based on predefined rules.

1990s – The Rise of Role-Based Access Control (RBAC)

As organizations and information systems became more complex, so did the need for more scalable access control models. Role-Based Access Control (RBAC) emerged in the 1990s as a solution. Instead of managing permissions for individual users, RBAC allowed administrators to assign roles to users. Depending on the role there were predefined permissions for each. For example, an HR person only was granted access to only HR data.

• Hierarchical RBAC: Over time, RBAC became more sophisticated by introducing hierarchies and inheritance, making managing permissions across complex organizational structures easier.
• Attribute-Based Access Control (ABAC): ABAC extended the idea of RBAC by introducing attributes for users, objects and environments. Decisions were made based on these attributes, providing finer granularity. For example, someone based in London, could only have access to data in the London branch.

Early 2000s – Access Governance in Big Data Systems: The Rise of Hadoop

With the proliferation of big data in the 2000s, traditional access control models were no longer sufficient. Large-scale distributed systems, such as Hadoop, emerged to handle massive datasets, and managing access to these environments introduced new challenges due to their architecture differences.

Access Control in Hadoop Ecosystems

Hadoop’s distributed nature meant data could be stored across clusters of nodes, making centralized access control more challenging. Early versions of Hadoop lacked fine-grained access controls, leading to potential data exposure risks. As enterprises increasingly adopted Hadoop for sensitive data workloads, a need for a robust governance framework became critical.

• Apache Ranger: Tools like Apache Ranger were developed to address these gaps. This framework enabled fine-grained access control and auditing for Hadoop environments. Ranger, for example, allowed organizations to define policies for who could access which datasets, offering centralized management for distributed environments.
  Illustration: Imagine a financial services company storing terabytes of transactional data in a Hadoop cluster. Using Apache Ranger, they could ensure that only data analysts with the right roles could access specific financial reports while keeping sensitive customer data restricted.

2. The Emergence of Unified Access Governance

The emergence of cloud highlighted the need for unified data governance tools capable of spanning multiple data platforms. Enterprises needed a way to consistently enforce access control and compliance policies across both on-premises and cloud-based big data systems based on metadata and data classification. This is where solutions like Privacera began to make an impact, enabling scanning and classification of sensitive data and unified access governance across hybrid data environments based on the data classifications .

• Key challenges  that are introduced by having multiple data platforms Manual Management: Manually managing data silos or piecing together fragmented cloud tools creates inconsistent security policies, complicates compliance, and inflates IT costs by demanding more resources and larger teams.
• Scalability Challenges: As data grows, manual governance breaks. One financial services client hit a wall managing 31 PB across Redshift, Spark, EMR, and Flink. Thousands of datasets became unmanageable, complicating governance, control, and security.
• Access Control Issues: Coarse grained access controls are overly permissive and hard to manage, creating security risks. Regulated industries need dynamic access management to handle user attributes and group memberships effectively.
• Operational Inefficiencies: Manually processing access requests through ticketing systems and onboarding into governance frameworks creates operational bottlenecks and increases the risk of errors. Similarly, manual encryption for data masking is inefficient at scale, delaying data access and driving up operational costs.
• Data Visibility and Redundancy: Limited visibility into data lakes made it hard to track datasets, manage environments, and monitor access. Redundant, untracked datasets drove up storage costs, while relying on tribal knowledge for cleanups was unsustainable.
• Technology Sprawl and Integration Complexities:Integrating Databricks, EMR, and Redshift added admin burdens and made seamless data access challenging. Managing governance across multi-cloud environments further complicated compliance and security.
• Governance and Compliance Risks:Federated permissions without centralized oversight hinder governance, risking non-compliance and delays in access requests. Organizations struggle with scalability, access control, inefficiencies, data visibility, and governance, driving up costs and security risks.

3. Early 2020s –  The Emerging Challenge: Authorization for GenAI Applications

In the early 2020s, we saw an emergence in various Generative AI models like GPT-4, Mistral,LLama, and other large language models (LLMs). However, they introduce unique challenges for access control. 

What Makes GenAI Different?

Unlike traditional applications with access control designed to regulate static data and predefined workflows, GenAI systems are dynamic, sometimes unpredictable, and evolving.

Key characteristics that set GenAI apart:

• Unpredictable Behavior: AI models generate content in real-time based on input data, which can be highly variable. The output is not always predictable, making it difficult to predefine access rules relating to access and protecting sensitive data. 
• Complex Data Interactions: GenAI applications often use vast datasets, including proprietary, personal, and public data. Understanding and regulating how the AI interacts with different data types becomes crucial.
• Collaborative Workflows: Many emerging agentic models support multiple users interacting with the same AI model. Managing access controls for these interactions is complex, as it requires governing not just access to the model, but also to the specific system and enterprise data needed by AI

Authorization and Access Control Challenges in GenAI

Let’s explore the key differences between access control models for regular analytical workloads versus those required for GenAI applications.

1. Data Access Granularity:
   • Analytical workloads: Access control for traditional applications focuses on regulating access to well-defined resources such as files, APIs, and databases.
   • GenAI: In contrast, GenAI requires controlling access at different touchpoints, example agents, training data, model parameters, and output data. These systems need to ensure that sensitive data used during model training is not inadvertently exposed through AI outputs. Fine-grained control over which data subsets the agents or a model can access, generate, or modify is essential.

Example: Imagine an AI model trained on both public and private datasets. Users should have access only to the output based on the public dataset unless explicitly authorized to view private data.

2. Dynamic Data Interaction:
   • Analytical workloads: In most traditional systems, access control rules are static and tied to specific resources.
   • GenAI: The AI’s interaction with data is far more dynamic. For example, a user might request an AI to summarize sensitive documents.. Access control must dynamically adjust based on the type of data requested, who is asking, and the AI’s response generation process. This necessitates contextual and adaptive access control mechanisms.

Example: A company builds an application to help employees understand company policies, including HR policies. When a user tries to get data about company compensation, only data relevant to the role must be presented back and other sensitive data should be filtered out

3. Role of Human-in-the-Loop:
   • Analytical workloads: Once access is granted, users operate independently, with limited oversight.
   • GenAI: In AI systems, the human-in-the-loop paradigm has become the norm for ethical decision-making. Access controls must incorporate real-time oversight to ensure that sensitive data is not misused by the AI or by users interacting with the AI. This includes continuous monitoring and intervention when needed.

Example: In a healthcare setting, an AI might generate treatment recommendations based on patient data. Real-time oversight and access control can ensure that sensitive patient data is not exposed to unauthorized personnel.

4. Data Provenance and Auditing:
   • Analytical workloads: Auditing typically tracks user access to data and changes made to resources.
   • GenAI: Auditing for GenAI must extend beyond basic tracking. It needs to cover data provenance, i.e., what data was used to train the model, what data was stored in a vector database and generate a specific output. This transparency is crucial for troubleshooting issues like biased AI behavior and hallucinations, ensuring compliance with regulations, and controlling data sprawl.

4. The Future of Access Control for GenAI

The evolution of access control in GenAI applications will require new approaches that combine traditional models with AI-specific capabilities:

• AI-Driven Access Control: Future systems will likely use AI to learn about user context, intent and adapt access control rules in real-time, enhancing both security and user experience.
• Federated Access Management: GenAI applications often span across multiple organizations and data sources. Federated access management, where different entities can retain control over their own data while collaborating with shared AI models, will be crucial.
• Explainability in Access Decisions: GenAI systems must offer explainable access control decisions. Users and administrators should be able to understand why the AI granted or denied access to specific resources, helping build trust in AI-driven security.

Start Your AI Governance Journey With Privacera

As we embrace the potential of GenAI, traditional authorization and access control models are proving insufficient to address the unique challenges posed by dynamic AI interactions, complex data use cases, and unpredictable model behavior. The industry must rethink access governance to ensure that GenAI systems are secure, compliant, and trustworthy.

Organizations building GenAI solutions must prioritize adaptive, contextual access control mechanisms that go beyond static permissions and embrace the dynamic, evolving nature of AI models. In doing so, we can unlock the full potential of Generative AI while safeguarding the data and users it impacts. Privacera AI Governance (PAIG) is the industry’s first comprehensive security, safety and governance solution for GenAI applications. PAIG operates independently from your choices of models, libraries and RAG approaches to truly provide a holistic security and observability platform. You can start your journey with PAIG today by scheduling a demo here or you can get started on the open source version of PAIG.

Stay tuned for our next post, where we explore practical frameworks and tools that can help secure AI-powered systems.