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🤖 AI Access Controls

Control Core applies Policy-Based Access Control (PBAC) to AI systems: who can use which AI capabilities, what data can be sent to models, and how usage is governed and audited. This page outlines use cases for generative AI, RAG, agent tooling, and enterprise AI applications—with compliance references and prospect-oriented examples.

📌 Definition

AI Access Controls with Control Core means:

  • Defining and enforcing policies at the Policy Administration Point (PAP), evaluated at the Policy Enforcement Point (PEP/Bouncer), using context from Policy Information Points (PIPs) and decisions from the Policy Decision Point (PDP).
  • Controlling access to models and endpoints, which prompts and data are allowed, and how usage is logged for security, governance, optimization, and audits—without exposing internal architecture.

How it flows

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Request flow: the Bouncer (PEP) intercepts every AI request, evaluates policy using context from PIPs, then allows or denies access to the model. The Control Plane (PAP) supplies policies and receives audit logs.

🤖 Generative AI

Use case: Organizations want to control who can call which models, which prompts and data are allowed, and enforce guardrails while optimizing token usage and cost.

How Control Core helps: Policies define which identities (users, roles, or AI agents) can access which model endpoints. You can restrict or allow prompts and request payloads by policy, apply data masking so sensitive fields never reach the model, and log decisions for compliance. Central policy management (PAP) and enforcement at the Bouncer (PEP) give a single place to update rules and enforce them in real time. Token usage visibility and policy-driven routing support optimization and cost control.

Compliance: Aligns with common frameworks such as SOC 2, ISO 27001, GDPR/UK GDPR, and HIPAA where AI touches personal or sensitive data. See Regulatory Compliance for regional details.

🤖 RAG (Retrieval-Augmented Generation)

Use case: Organizations deploy RAG over internal knowledge bases and need real-time permission on what can be retrieved and sent to the model, without over-exposing internal documents.

How Control Core helps: Policies can restrict which documents or data sources a given user or session can retrieve and send to the model. Real-time context from PIPs (e.g. user role, data classification, or business rules) drives allow/deny and filtering. The Bouncer enforces these rules at the edge so only permitted content reaches the model. This supports data classification (e.g. internal vs. customer-facing) and reduces risk of leaking sensitive or confidential content.

Prospect tie-in: Organizations deploying AI RAG tools and seeking real-time permission or access control can use Control Core to govern retrieval and model input by identity and context.

📌 MCP (Model Context Protocol) / Agent Tooling

Use case: AI agents that use tools and data sources must be constrained so sensitive data or cross-tenant data never leaks.

How Control Core helps: Policies define which tools and data sources an agent (or identity) can use. Enforcement at the Bouncer (PEP) applies these rules before requests reach external tools or context providers. You can isolate data by tenant or user and prevent agents from accessing out-of-scope resources. Audit logs support governance and incident review.

🤖 Enterprise AI Applications

Use case: Internal AI applications (chatbots, copilots, analytics) need governance, access control, data protection, and audit.

How Control Core helps: A single policy layer (PAP) defines who can use which AI applications and what data they can send or receive. The Bouncer enforces these rules for all AI traffic. PIPs supply real-time context (e.g. employment status, training completion, risk flags) so policies adapt to current state. Decision logs and audit trails support compliance and optimization (e.g. token usage, cost).

Prospect tie-ins:

  • Financial institutions with data insights but worried about data leakage: Control Core can enforce data masking and access rules so only permitted data reaches AI models, with full audit.
  • Large and medium enterprises optimizing AI token usage, protecting sensitive data from LLMs, or preventing data from being shared across users via AI: policies can restrict model access, mask or filter inputs by user/role, and log all decisions for review.

🔒 Compliance and Certifications (Summary)

Control Core can support compliance with widely used frameworks when AI systems process sensitive or regulated data. This page does not claim specific certifications; align with your legal and compliance team. Commonly relevant frameworks include:

  • SOC 2, ISO 27001 — Security and governance
  • GDPR, UK GDPR — Personal data and purpose limitation
  • HIPAA — Health data (where applicable)

For regional and industry-specific details (Canada, USA, South America, EU, UK, Asia), see Regulatory Compliance.

📌 Next Steps