A clear architectural pattern is beginning to emerge as enterprises adopt large language models (LLMs). It’s a pattern that mirrors how IT systems have been deployed for decades: separation by function, purpose, and risk domain.
Just as organizations run distinct systems for finance (ERP), human resources (HRIS), customer relationship management (CRM), and more, the next generation of AI adoption will see companies deploying individual LLMs tailored to specific departments and business functions.
This segmentation isn’t just practical – it’s essential.
Why separate LLMs make sense inside the enterprise
- Security and compliance
Different departments handle different classes of data. Financial systems process sensitive accounting records; HRIS systems manage personally identifiable information (PII); legal teams handle privileged communications. Applying a single, monolithic LLM across all departments introduces significant risk.
Segregated LLMs allow for granular access control, role-based permissions, and alignment with data governance and regulatory requirements. They make it easier to enforce policies for compliance such as SOC 2, GDPR, HIPAA, and internal separation-of-duties controls.
- Domain-specific fine-tuning
Each business unit has its own language, documentation, and workflows. Sales and marketing teams want their LLM to understand leads, campaigns, and product messaging. Finance wants a model that’s fluent in spreadsheets, journal entries, and audit trails. Legal needs redlining and precedent-aware drafting.
Rather than fine-tuning a single massive model to serve all these needs (which introduces risk of cross-contamination and poor performance), teams will run separate, smaller LLMs that are tuned and optimized for their unique domain language and context.
- Internal control and observability
With multiple LLMs running in parallel, enterprises gain clearer observability and auditability. Each system can log prompts, monitor usage, and assess accuracy against different metrics, aligned with that department's KPIs or compliance benchmarks. This is especially critical in regulated industries.
But who gets access? Humans, agents, and the new frontier of access management
One of the most complex challenges in this future state is managing access to these departmental LLMs; not just for human users, but increasingly for autonomous AI agents acting on their behalf.
Human access
Traditional access management systems weren’t built with LLMs in mind. Who should be able to query the HR model? Should finance analysts have access to procurement-related insights? How do you prevent data leakage across departments? To maintain trust and compliance, you need:
- Role-based access control (RBAC) and attribute-based access control (ABAC) must be extended to LLM usage.
- Relationship-based access control (ReBAC) provides additional granularity by defining permissions based on dynamic relationships between users, resources, and contexts—crucial for LLMs where access decisions often depend on complex organizational hierarchies and data relationships.
- Prompt-level access restrictions will be required (e.g., "user can query salaries, but not disciplinary history").
- Identity-aware proxies and auth layers will need to be embedded directly into LLM endpoints.
Agent access
The rise of AI agents introduces a whole new dimension. Agents may need to query multiple LLMs, orchestrate tasks across departments, or relay results between systems. This raises critical questions:
- How do you authenticate and authorize agents the same way you do users?
- What guardrails exist to ensure agents don’t access sensitive data unintentionally?
- How do you log and audit every action taken by an autonomous agent?
To solve this, enterprises will need to:
- Treat AI agents as first-class identities within IAM systems.
- Assign them scoped, signed tokens and permission sets.
- Monitor usage continuously for anomalies and policy violations.
Without robust access controls, the promise of LLM segmentation collapses into a security nightmare. This is just as important as training the models is protecting them.
The enterprise LLM mesh: A federated intelligence model
In the background, enterprises can maintain a larger, centralized LLM or AI data lake that receives learnings, anonymized data patterns, and outcomes from the individual departmental models.
This creates a federated LLM architecture, where each sub-model acts like a spoke contributing to a central hub:
- HR’s LLM notices an uptick in resignation reasons and contributes anonymized trends.
- Finance’s model detects emerging risk in vendor payment data.
- Customer support’s model surfaces common product issues and sentiment drift.
These insights roll up to a larger, more general-purpose model (or analytics platform) that allows leadership, data scientists, and product teams to observe cross-organizational trends and feed them into broader AI initiatives, all while preserving compliance and internal boundaries.
Benefits of a federated, segregated LLM architecture
- Data minimization & privacy: LLMs only process what they need.
- Risk isolation: A compromised model in one department doesn’t expose the entire organization.
- Optimized performance: Smaller, focused models run faster and more accurately.
- Compliance ready: Easier to audit and demonstrate access controls.
- Knowledge sharing without leaking: Cross-functional patterns can be aggregated safely into a learning data lake without exposing raw data.
- Secure, auditable access for humans and agents: Access policies and identities are enforced consistently across all models.
A future-ready enterprise AI stack
This shift isn’t just hypothetical. We’re already seeing enterprise AI deployments evolve toward LLM-as-a-service models, where internal APIs serve different business units with tailored experiences, governed under enterprise security and compliance frameworks.
Vendors are starting to offer fine-tuned LLMs for finance, HR, legal, and IT, while allowing those models to plug into broader knowledge graphs or organizational memory. The enterprise of the future will not run on a single, omnipotent AI brain. It will run on a network of specialized, compliant, high-context LLMs… each supporting the business in a way that’s secure, observable, and fit for purpose. And, at the heart of it all, identity and access management will determine the trustworthiness of the entire AI layer.
For more info on how Ory can help you secure access to LLM systems and manage human and AI identities at scale, get in touch with our team.
