Use Cases
AI Fabrix use cases describe how enterprises safely use AI under real constraints: identity, permissions, auditability, and regulation.
They are platform-enabled capabilities, not products or agents.
Each use case exists because AI Fabrix removes structural failure modes that block traditional AI deployments.
Microsoft 365 Knowledge Assistants
Problem
Organizations want AI assistants that can answer questions using Microsoft 365 content: SharePoint, OneDrive, Teams, and Outlook.
Traditional approaches fail because:
- Permissions differ across sites, libraries, and tenants
- Search indexes flatten security context
- AI answers cannot be trusted to respect access rules
- Audits cannot explain why a result was shown
As a result, assistants are limited, disabled, or blocked by security teams.
What This Use Case Enables
AI Fabrix enables permission-aware knowledge assistants that:
- Answer questions across Microsoft 365 content
- Respect per-user permissions automatically
- Never expose content the user is not allowed to see
- Produce answers suitable for regulated environments
The assistant does not "search everything". It only sees governed, identity-filtered context.
Why This Is Hard
Microsoft 365 permissions are:
- Highly granular
- Distributed across sites and workloads
- Dynamic over time
Most AI tools:
- Use service accounts
- Flatten permissions during indexing
- Rely on post-filtering in application code
These approaches break at scale and fail audits.
Why AI Fabrix Works
AI Fabrix integrates Microsoft 365 through the Dataplane and CIP:
- Identity is preserved end-to-end via Entra ID
- CIP pipelines enforce permissions before data reaches AI
- Metadata and lineage are preserved for auditability
- Retrieval is permission-aware by design
AI never receives raw documents. It receives scoped, explainable context.
Typical Scenarios
- Internal policy assistants
- IT and HR knowledge bots
- Engineering documentation assistants
- Executive briefing assistants
Secure Internal Copilots
Problem
Enterprises want internal copilots that help employees:
- Navigate systems
- Summarize information
- Assist with operational tasks
Copilots often fail security review because:
- They operate with elevated privileges
- They blur system boundaries
- They introduce undocumented access paths
As a result, copilots remain pilots or demos.
What This Use Case Enables
AI Fabrix enables secure internal copilots that:
- Act strictly on behalf of authenticated users
- Follow the same access rules as humans
- Operate across systems without bypassing governance
- Produce auditable, explainable outputs
There are no AI exception paths.
Why This Is Hard
Most copilots:
- Use shared credentials
- Re-implement permission logic in code
- Embed business rules inside prompts or workflows
This creates hidden risk and operational fragility.
Why AI Fabrix Works
With AI Fabrix:
- Identity is the execution context
- Policies are enforced centrally (Controller layer)
- CIP supplies governed tools and data
- Orchestration cannot bypass the dataplane
Copilots become first-class enterprise actors, not privileged systems.
Typical Scenarios
- Internal operational copilots
- IT service desk assistants
- Finance and procurement copilots
- Knowledge worker productivity assistants
Policy-Aware Enterprise Search
Problem
Enterprise search systems struggle to balance:
- Relevance
- Security
- Explainability
AI-powered search often makes this worse by:
- Mixing data from multiple systems
- Ignoring source-system permissions
- Returning unexplainable results
Security teams cannot certify these systems.
What This Use Case Enables
AI Fabrix enables policy-aware enterprise search that:
- Retrieves data across systems safely
- Applies identity and policy filtering automatically
- Preserves business metadata and lineage
- Produces explainable search results
Search results are governed outputs, not guesses.
Why This Is Hard
Traditional search stacks:
- Index data outside its security context
- Apply filtering after retrieval
- Lose lineage during vectorization
AI adds probabilistic behavior on top of already weak foundations.
Why AI Fabrix Works
AI Fabrix enforces governance before retrieval:
- Data is normalized and tagged with metadata
- Retrieval engines apply identity-aware filtering
- CIP pipelines ensure consistent enforcement across sources
- Audit trails exist by default
Search becomes a governed dataplane function, not an app feature.
Typical Scenarios
- Enterprise-wide knowledge search
- Policy and compliance search
- Cross-system document discovery
- AI-assisted analytics exploration
Case and Deal Workspaces
Problem
Case and deal work requires AI assistance, but involves:
- Highly sensitive data
- Context-specific permissions
- Strict audit and traceability requirements
Traditional AI tools cannot safely operate in these environments.
What This Use Case Enables
AI Fabrix enables secure, case-scoped AI workspaces that:
- Restrict AI visibility to a specific case or deal
- Enforce role- and context-based access
- Support human-in-the-loop workflows
- Produce full audit trails
AI becomes an assistant, not a decision maker.
Why This Is Hard
Case data:
- Spans multiple systems
- Changes over time
- Is governed by contextual rules, not static roles
Embedding this logic in applications does not scale.
Why AI Fabrix Works
AI Fabrix scopes data access structurally:
- CIP pipelines enforce case context
- Metadata defines ownership and scope
- Policies apply automatically across systems
- Every AI interaction is logged and explainable
This makes AI usable in high-trust workflows.
Typical Scenarios
- Legal and compliance cases
- M&A and deal rooms
- Public sector case management
- Regulated customer support
Regulated Data AI Scenarios
Problem
In regulated industries, AI must be:
- Explainable
- Auditable
- Predictable
- Governed
Most AI platforms fail these requirements and are blocked entirely.
What This Use Case Enables
AI Fabrix enables regulated AI usage where:
- Data lineage is preserved end-to-end
- AI actions are fully auditable
- Governance is structural, not procedural
- Compliance reviews are deterministic
AI can be used where it was previously prohibited.
Why This Is Hard
Regulators require:
- Clear justification of outputs
- Traceability to source data
- Proof of policy enforcement
Most AI systems cannot provide this without manual reconstruction.
Why AI Fabrix Works
AI Fabrix embeds compliance into the platform:
- Controller layer enforces policy centrally
- Dataplane preserves lineage and context
- CIP pipelines eliminate opaque integrations
- Audit evidence exists by design
Compliance becomes verifiable, not negotiated.
Typical Scenarios
- Financial services decision support
- Healthcare administration
- Government and public sector AI
- Risk and compliance analytics
Why These Use Cases Matter
These use cases:
- Do not rely on specific AI tools
- Do not require security exceptions
- Do not compromise governance
They are possible only because AI Fabrix removes structural failure modes.
This is the difference between experimenting with AI and deploying AI at enterprise scale, under control.