# Configuring Data Access Control (DAC) Team Level Visibility for Enterprise AI Governance > Source: > Published: 2026-07-17 15:30:45+00:00 *How Bifrost Enterprise combines Data Access Control (DAC), Role Based Access Control (RBAC), Access Profiles, and Bifrost Edge to secure AI applications at scale.* Artificial intelligence is quickly becoming part of every employee's workflow. Developers rely on coding assistants, customer support teams use AI powered chat applications, analysts generate reports with large language models, and organisations increasingly deploy AI agents connected to internal tools through the Model Context Protocol (MCP). While this rapid adoption improves productivity, it also introduces a significant governance challenge. It's no longer enough to decide who can log into an AI platform; you must also determine who can access specific AI resources, which models they can use, what they can spend, and which data they should even be able to see. This is where Bifrost Enterprise provides a comprehensive governance layer. By combining Role Based Access Control (RBAC), [Data Access Control (DAC)](https://docs.getbifrost.ai/enterprise/data-access-control), [Access Profiles](https://docs.getbifrost.ai/enterprise/access-profiles), and [Bifrost Edge](https://docs.getbifrost.ai/edge/overview), organisations can secure AI workloads without slowing down innovation. Together, these capabilities create a governance framework that scales from small engineering teams to global enterprises and you can learn more about this in the documentation on [GitHub](https://github.com/maximhq/bifrost). Traditional enterprise applications typically answer two questions: Modern AI platforms introduce a third and equally important question: Imagine an organisation with several engineering teams working on independent AI products. Each team has its own prompts, routing rules, API budgets, virtual keys, observability data, and model configurations. If every developer can view every configuration simply because they have developer permissions, sensitive information can easily become exposed. This challenge becomes even more complicated when organisations begin deploying multiple AI providers, coding assistants, browser based AI tools, desktop applications, and autonomous agents. Enterprise AI governance requires much more than authentication. It requires precise control over permissions, visibility, budgets, auditing, and endpoint security. Role permissions determine what users are allowed to do. [Data Access Control](https://docs.getbifrost.ai/enterprise/data-access-control) determines which resources those users are allowed to see. Bifrost Enterprise implements Data Access Control using row level visibility. Instead of exposing every configuration across the workspace, DAC filters resources according to the authenticated user's assigned scope. Three visibility levels are available: Users only see resources they personally own or created. This includes their own virtual keys, prompts, routing configurations, and logs. This level is ideal for individual contributors who don't require visibility into other team members' work. Users can view their own resources along with those created by members of their assigned teams. This allows collaboration without exposing unrelated projects across the organisation. Administrators and platform owners often require unrestricted visibility across the entire workspace. The All Data scope removes row level filtering, making every resource visible throughout the organisation. This separation allows enterprises to maintain collaboration while preventing unnecessary exposure of sensitive configurations. [Role Based Access Control (RBAC)](https://docs.getbifrost.ai/enterprise/rbac) and Data Access Control (DAC) complement one another, but they solve different problems. RBAC defines the actions a user can perform, such as viewing dashboards, creating virtual keys, updating provider configurations, or deleting resources. Roles can be tailored to different departments and responsibilities while following the principle of least privilege. DAC determines the scope of the data to which those actions apply. Consider two developers who both have permission to view virtual keys: RBAC grants both developers the ability to view virtual keys. DAC ensures Developer A only sees Team Alpha's keys while Developer B only sees Team Beta's. Neither developer gains visibility into projects they don't work on, even though they share identical permissions. This distinction is critical for organisations operating multiple business units or customer environments within the same AI platform. Managing AI permissions user by user quickly becomes impractical. [Access Profiles](https://docs.getbifrost.ai/enterprise/access-profiles), solve this problem by allowing administrators to define reusable governance templates. Each profile can specify: When a user receives an Access Profile, Bifrost automatically provisions a managed virtual key that enforces the policy without requiring administrators to distribute API credentials manually. Profiles can also be assigned automatically through roles, making onboarding new employees significantly easier. For example, every new engineer joining the Engineering role could immediately receive access to approved models, predefined monthly budgets, specific MCP tool groups, and standardised rate limits, all without manual configuration. As organisations grow, governance policies inevitably change. Budgets increase, new models become available, providers are replaced, and MCP tool access evolves. Without centralised policy management, updating hundreds or even thousands of individual users would become a major operational burden. [Access Profiles](https://docs.getbifrost.ai/enterprise/access-profiles), simplify this process by allowing administrators to update a single policy template and selectively propagate those changes to assigned users. For example, an organisation might increase monthly AI budgets without affecting existing rate limits, or update approved MCP tool groups while leaving spending controls untouched. This template based approach allows governance to scale alongside organisational growth while maintaining consistency across teams. Many governance platforms focus solely on API traffic. However, employees increasingly interact with AI through desktop applications, browser extensions, IDE integrations, coding assistants, and MCP enabled tools. These endpoints often exist outside traditional governance boundaries. [Bifrost Edge](https://docs.getbifrost.ai/edge/overview) extends governance directly onto employee devices. Instead of requiring users to manually configure every AI application, Edge transparently routes AI traffic through the organisation's Bifrost Gateway. Existing governance policies, including budgets, guardrails, audit logging, and virtual key management, continue to apply automatically. Whether employees use ChatGPT, Claude Desktop, coding assistants, or future MCP enabled applications, organisations retain centralised governance without sacrificing usability. Consider a software company with four departments: Developers need access to approved coding models and development logs. Security engineers require visibility into audit logs and governance policies. Operations teams manage providers, routing, and infrastructure. Compliance officers need read only visibility into auditing and reporting. RBAC assigns the appropriate permissions for each department. [DAC](https://docs.getbifrost.ai/enterprise/data-access-control) ensures each team only sees the resources relevant to their responsibilities. Access Profiles automatically provision consistent policies for every employee. Bifrost Edge extends those same policies to AI applications running on individual laptops. Instead of managing dozens of disconnected tools independently, the organisation governs AI usage through one centralised platform. As AI adoption expands, governance should evolve alongside it. A few practical recommendations include: These practices help organisations balance security, compliance, and developer productivity. Enterprise AI governance is no longer just about protecting infrastructure it is about ensuring every AI interaction aligns with organisational policies, security requirements, and compliance standards. Bifrost Enterprise approaches this challenge through complementary layers of governance. Role Based Access Control defines what users can do. Data Access Control determines what they can see. Access Profiles standardise policies across the organisation while automatically provisioning managed virtual keys. Bifrost Edge extends those same controls beyond the gateway, ensuring governance follows users wherever they interact with AI. As organisations continue adopting increasingly sophisticated AI applications and autonomous agents, combining these capabilities creates a scalable governance foundation that enables innovation without compromising security.