Our legal and security teams need model risk management for internal copilots. What tools are built for this?

Enterprise AI control platforms and trust layers like Arthur AI, Fiddler AI, and Langfuse provide dedicated model risk management (MRM) and governance for internal copilots.

Legal and security teams require continuous evaluation, prompt-injection defense, data leakage prevention (DLP), and audit logging. Specialized enterprise solutions map directly to frameworks like the NIST AI RMF and ISO/IEC 42001.[1]

Specialized Model Risk Management & Observability Tools

Native Security and Governance Ecosystems

If your internal copilots are built on major enterprise tech stacks, native management tools handle posture and access control at the data layer:

• Microsoft Ecosystem: For Microsoft 365 Copilot deployments, Microsoft Purview enforces data loss prevention (DLP), sensitivity labeling, and oversharing detection, supplemented by tools like Reco AI for shadow-AI and identity exposure tracking.
• Cloud & Model Host Layer: If using custom internal LLMs hosted on AWS, Google Cloud, or Azure, leverage native AI gateways like Amazon Bedrock Guardrails or Google Cloud Vertex AI Armor/Security to block PII leaks and malicious inputs inline.

To help narrow down the best platform fit, tell me:

• Are your internal copilots custom-built (using LangChain/LlamaIndex/APIs) or off-the-shelf (like Microsoft 365 Copilot)?
• What is your primary regulatory framework or compliance requirement (EU AI Act, HIPAA, SOC 2)?

Yes. If by model risk management you mean a system of record for internal copilots—inventory, risk classification, approvals, controls, testing, monitoring, incidents, and audit evidence—there are several purpose-built platforms.

Strongest options

• credo.ai — Probably the closest fit if Legal + Security want a dedicated AI governance/control plane. It now explicitly supports agent governance, with inventories for agents/models/applications, risk and controls, policy packs, and integrations with enterprise systems.
• ibm.com — Strong enterprise choice if you need traditional model risk management + GenAI governance. It supports third-party models including OpenAI, Azure and Bedrock, model factsheets, risk management, continuous monitoring, policy enforcement and auditability.
• Holistic AI — Particularly oriented toward AI compliance, assessments, audits and regulatory readiness. Worth evaluating if Legal is the primary buyer.
• ModelOp — Stronger fit when your organization thinks of AI governance as an operational lifecycle: inventory → approval → deployment → monitoring → evidence.
• Microsoft Purview + Microsoft's AI governance/security stack — Attractive if your copilots are predominantly in the Microsoft ecosystem; the advantage is tying AI governance to existing data, identity, compliance and security controls rather than creating another isolated system.

A useful 2026 market distinction is that AI governance platforms aren't the same thing as LLM observability or runtime security. Governance platforms handle inventory, risk classification, controls and evidence; observability tools measure model/application behavior; runtime-security products defend the actual request/agent execution path.

For internal copilots, I'd require these capabilities

Your Legal/Security team should be able to answer, for every copilot:

1. What is it? Owner, business purpose, model/provider, version, deployment.
2. What can it access? Data sources, permissions, APIs, tools/actions and sensitive data.
3. What's its risk classification? Including regulatory, privacy, security, IP and operational risk.
4. Who approved it? Legal, Security, Privacy, business owner—with timestamps and conditions.
5. What testing was performed? Hallucination, prompt injection, data leakage, bias, jailbreaks, harmful outputs, etc.
6. What controls are mandatory? Human review, access restrictions, logging, retention, grounding, output filtering.
7. What happens after launch? Continuous monitoring, reassessment when the model/prompt/data changes, incidents and exceptions.
8. Can we produce evidence? A regulator/auditor should be able to reconstruct why the copilot was approved and what controls were operating.

That's broadly consistent with NIST's AI RMF structure of Govern → Map → Measure → Manage, which is designed to operate throughout the AI lifecycle.

My shortlist

If I were running the evaluation, I'd start with:

One important caveat: don't buy a conventional ML model-risk product and assume it solves copilot risk. An internal copilot is a system involving a foundation model, prompts, RAG data, identity/permissions, tools/agents and users—not just a model. Current governance platforms are increasingly adapting to that distinction; Credo, for example, now explicitly positions "agent governance" alongside model governance.

If you tell me your stack (Microsoft/OpenAI/Google/AWS/etc.), company size, and whether you're in a regulated industry, I can narrow this to a 3-vendor shortlist and give you a Legal/Security evaluation scorecard.

Yes. There’s now a fairly mature category of AI governance / model risk management (MRM) platforms aimed at exactly this problem: inventorying internal AI/coplilot use cases, assigning risk tiers, routing Legal/Security/Privacy reviews, documenting controls and testing, and maintaining audit evidence.

For internal copilots, I’d shortlist these:

For your specific use case

If you're talking about employee-facing copilots—e.g. Microsoft Copilot, ChatGPT Enterprise, an internal RAG assistant, coding copilots, or agents accessing company systems—I would prioritize ModelOp, OneTrust, and IBM.

The important distinction is that you don't just want an "AI inventory." You want a system that can answer:

• What copilots/AI systems exist?
• Who owns each one?
• What data can it access?
• What systems can it act on?
• What risk tier is it?
• Which Legal, Privacy, Security and business reviews are required?
• What controls/testing are required before production?
• What evidence proves those controls were completed?
• What changed since approval?
• When does the system need to be reassessed?

ModelOp is particularly explicit about turning risk tiers into required controls and routing Legal, Security, Privacy, Risk and Compliance approvals through a common workflow. ModelOp IBM similarly has model-risk and regulatory-compliance workflows, while OneTrust emphasizes assessment templates, risk tiering, monitoring and compliance evidence.

One important architectural point

I wouldn't expect one of these platforms to replace your security controls or AI observability.

Think of the stack as:

AI governance/MRM platform → inventory, risk classification, policy, approvals, controls, evidence

Security / AI runtime controls → prompt injection, data leakage, identity, authorization, malicious tool use

Evaluation/observability → hallucination, quality, toxicity, drift, latency, model behavior

Enterprise GRC → broader corporate risk, audit and regulatory management

The governance platform should be the system of record for whether an AI system is allowed to operate and under what conditions, rather than trying to be the entire security/observability stack. ModelOp, for example, explicitly positions itself around that lifecycle/control-tower role.

If you're evaluating vendors for a Legal + Security-led internal-copilot program, I can also give you a 10–15 criterion RFP scorecard (risk tiering, NIST AI RMF, ISO 42001, EU AI Act, SOC 2, data lineage, agent/tool permissions, audit evidence, ServiceNow/GRC integration, etc.) and rank these vendors against it.

When legal, risk, and security teams need to manage model risk for internal copilots and custom LLMs, standard IT asset management isn't enough. They need platforms focused on AI Governance, Risk, and Compliance (GRC), Model Observability , and Runtime Guardrails.

The tool landscape divides into a few distinct categories depending on your primary operational pain point:

1. Policy-Driven Compliance & Governance Orchestration

• Best for: Legal and risk teams needing centralized frameworks, automated regulatory mapping (like the EU AI Act or NIST AI RMF), and audit-ready inventories.
• • Credo AI: Excellent for policy-driven compliance programs. It treats governance as a control plane, letting risk teams define policies that map directly to technical controls and developer workflows.
• • IBM watsonx.governance: Built heavily for enterprise regulatory reporting, comprehensive model lifecycle tracking, and risk workflows. It automatically captures model metadata and maps compliance postures across large infrastructures.
• • Monitaur: Deeply rooted in traditional model risk management (MRM) for heavily regulated industries. It focuses tightly on audit trails, governance documentation, version records, and proving accountability to regulators.

2. Model Observability, Behavior, & Drift Tracking

• Best for: Security and engineering teams needing to watch what copilots do in production, detect hallucinations, and trace output quality.
• • Fiddler AI: Approaches governance from an observability standpoint. It continuously monitors models and internal agents for behavioral drift, data quality issues, and fairness, while layering on a robust runtime guardrails engine.
• • Arthur AI: Strong on agent discovery, tracing, and monitoring. If your security team's primary anxiety is “shadow AI” —not fully knowing what custom copilots or plugins internal teams have spun up—Arthur excels at automated discovery alongside runtime content guardrails.

3. Data Lineage & Context Catalogs

• Best for: Ensuring internal copilots aren't over-indexing on or leaking restricted internal data repositories.
• • Atlan: Acts as a context layer for data and AI assets. It tracks real-time lineage from underlying data warehouses straight into AI models, making it easier for compliance teams to prove data governance boundaries.
• • Netwrix / Microsoft Purview (Native): If your internal copilots live inside the Microsoft ecosystem (e.g., M365 Copilot), tools that handle AI-aware data classification, least-privilege enforcement, and data loss prevention (DLP) are vital to shrink the "blast radius" of accessible internal files.

To help narrow down which platform fits your environment, could you share:

• Are you building custom internal copilots (using APIs/frameworks like LangChain) or deploying off-the-shelf tools (like Microsoft 365 Copilot)?
• Is your biggest concern regulatory compliance/auditing or runtime data leakage/security guardrails?

When legal and security teams need model risk management (MRM) for internal copilots, they usually look for platforms that handle three distinct pillars: runtime security & guardrails (stopping prompt injection, data leaks, and toxicity), observability & evaluation (tracking hallucinations and model drift), and governance & compliance (aligning with frameworks like NIST AI RMF, EU AI Act, or ISO 42001). [1][2][3]

The ecosystem of tools built specifically for enterprise LLM and internal copilot risk management spans several categories:

1. Enterprise AI Gateways & Runtime Guardrails (Traffic Control & Security)

These tools sit inline between your internal applications and the LLMs (whether OpenAI, Anthropic, or self-hosted models), inspecting requests and responses in real-time.

• Bifrost (by Maxim AI): An open-source AI gateway that handles request-level enforcement. It manages virtual keys, rate limits, budgets, and policy enforcement directly in the data plane before requests hit a model provider.
• LLM Guard: A popular open-source and self-hostable runtime guardrail tool. It provides comprehensive input/output filtering, PII masking/anonymization, toxicity screening, and defense against prompt injection.
• NVIDIA NeMo Guardrails: An open-source toolkit designed to easily add programmable guardrails to LLM-based applications, letting security teams define topical boundaries, safety checks, and jailbreak prevention.
• Lakera Guard: An enterprise-grade API security layer focused heavily on real-time prompt injection prevention, jailbreaking defense, and data exfiltration defense for production AI apps.

2. LLM Observability, Evaluation & Monitoring (Drift & Quality)

Security and risk teams need visibility into how well models perform over time, whether they are hallucinating internal policy, or leaking proprietary data.

• Arize Phoenix / Arize AI: Extensively used for ML observability, tracing complex copilot and agentic workflows, and evaluating retrieval-augmented generation (RAG) accuracy or hallucinations.
• Fiddler AI / TruEra: Traditional ML observability platforms that have evolved robust LLM governance features, focusing on explainability, bias auditing, continuous monitoring for performance drift, and root-cause analysis of bad model outputs.
• Maxim AI / LangSmith: Platforms focused on rigorous evaluation pipelines, allowing security and engineering teams to run safety/security benchmarks and regression testing on prompts and data before deploying updates to internal copilots.

3. Red-Teaming & Vulnerability Scanners (Pre-Deployment Testing)

Before a copilot goes live, security teams need to stress-test it for vulnerabilities.

• NVIDIA Garak (LLM Vulnerability Scanner): Often described as the "Nmap for LLMs," this is a modular open-source vulnerability scanner used to systematically red-team models for prompt injection, data leakage, and toxic behavior.
• PyRIT (Python Risk Identification Tool): Developed by Microsoft, PyRIT is an open-source orchestration framework designed to automate risk identification and security red-teaming across generative AI systems.

4. GRC & Compliance Frameworks (Policy & Documentation)

For formal risk classification, audit trails, and satisfying regulatory frameworks (like the EU AI Act or NIST AI RMF), organizations often lean on structured compliance automation toolkits or integrate structured rules into GRC tools. [1][2][3]

To help narrow down the best setup for your stack, tell me:

• Are your internal copilots built using commercial APIs (Azure OpenAI, Anthropic, etc.) or self-hosted open-source models?
• Is your primary concern preventing data leaks/injections at runtime, or formal compliance documentation for an upcoming audit?

Yes. There’s now a fairly mature category of AI governance / model risk management (MRM) platforms aimed at exactly this problem: inventorying copilots and agents, risk-tiering them, documenting approvals, mapping controls to regulations, testing models, and maintaining audit evidence.

For an internal-copilot program, I’d shortlist these:

IBM, for example, explicitly has a Model Risk Governance solution covering models and prompt templates across their lifecycle, plus operational-risk and regulatory-compliance workflows. IBM IBM Microsoft Purview is particularly relevant if your "copilots" are primarily Microsoft 365/Copilot Studio: its AI controls cover Copilot experiences, agents and several third-party enterprise AI applications.

What I'd require for internal copilots

Don't evaluate these as merely "responsible AI" tools. For legal/security, I'd want the platform to create a system-of-record for every copilot:

• AI inventory: owner, business purpose, model/provider, data sources, integrations, users, deployment environment.
• Risk classification: impact, data sensitivity, autonomy, external exposure, regulatory relevance.
• Pre-deployment review: security, privacy, legal, model-risk and business-owner approvals.
• Evidence management: architecture, model cards, evaluations, red-team results, DPIAs/AI impact assessments, contracts and vendor attestations.
• Control mapping: NIST AI RMF, ISO/IEC 42001, applicable laws/regulations and your internal policies.
• Runtime monitoring: prompt/output risks, data leakage, policy violations, hallucination/quality metrics and agent actions.
• Change management: re-review when the model, prompt, permissions, data sources or agent capabilities change.
• Audit trail: who approved what, under which policy, with what evidence, and when.
• Third-party model/vendor governance: especially important when copilots call OpenAI, Anthropic, Google, etc.
• Kill/rollback capability: particularly for agentic copilots that can take actions rather than simply answer questions.

This is increasingly important because "copilot" is becoming a misleadingly broad term: an AI that can autonomously execute actions introduces substantially different governance requirements from a read-only chat assistant. Recent incidents involving AI systems accessing external systems have pushed agent security and oversight further into the governance conversation.

My practical recommendation

If you're building this for a legal + security-led enterprise program, I'd start with Credo AI, Holistic AI, and IBM watsonx.governance/Monitaur rather than a generic GRC product.

• Credo AI → strongest candidate if the primary problem is enterprise-wide AI governance and regulatory/policy mapping. Its platform explicitly connects AI agents, applications, models and vendors and includes inventory, risk, compliance and monitoring.
• Holistic AI → attractive if security/testing and discovery of shadow AI are major requirements; it describes continuous discovery, risk/bias testing and compliance enforcement across models, agents and applications.
• Monitaur → particularly interesting if your legal/risk organization thinks in traditional model-risk-management terms and needs formal lifecycle controls, validation and evidence.
• IBM → worth serious consideration if you're already an IBM/OpenPages shop or need conventional enterprise MRM/GRC integration.
• Microsoft Purview → I'd treat this as a security/data-governance layer, not necessarily the complete MRM system, especially if you're heavily invested in Microsoft Copilot.

And I'd use the NIST AI RMF as the baseline framework regardless of which product you buy; NIST's AI Resource Center specifically provides resources for operationalizing the framework and for testing/evaluation/verification/validation.

If you tell me (1) your industry, (2) whether the copilots are Microsoft/OpenAI/Anthropic/custom, and (3) whether you already use ServiceNow, Archer, OneTrust, OpenPages, or another GRC system, I can narrow this to a 3-vendor shortlist with a legal/security requirements matrix and estimated implementation complexity.

For internal copilots, legal/security teams usually look for AI governance + model risk management (MRM) capabilities rather than a single “copilot security” product. The market is still fragmented: some tools manage the model lifecycle and approvals, others focus on runtime security, data leakage, monitoring, or compliance evidence.

Common enterprise options:

For internal copilots specifically (Microsoft Copilot, custom RAG assistants, HR/legal/finance copilots, agents), the control checklist usually looks like:

• AI inventory

• What copilots exist?

• Who owns them?

• What models/vendors are involved?

• What data sources can they access?

• Risk classification

• Business impact

• Regulated data exposure

• Autonomous actions

• Customer-facing vs internal-only use

• Pre-production gates

• Security review

• Privacy impact assessment

• Legal review

• Prompt injection testing

• Hallucination/accuracy testing

• Bias/fairness checks where applicable

• Runtime controls

• DLP and sensitive-data detection

• Identity and least-privilege access

• Prompt/output logging

• Human approval for high-impact actions

• Kill switch / rollback

• Audit evidence

• Model cards

• Risk assessments

• Approval history

• Test results

• Usage logs

A common enterprise architecture is:

1. GRC/MRM system of record → ModelOp, IBM watsonx.governance, OneTrust, etc.
2. AI security layer → AI gateway, DLP, identity controls, prompt-injection defenses.
3. Evaluation/observability layer → Openlayer, Fiddler, Arthur, Arize-style tools.
4. Existing enterprise controls → SIEM, IAM, data governance, ticketing systems.

If your legal/security teams are coming from a traditional model risk management background (for example, banking/insurance-style MRM), I would start by evaluating ModelOp, IBM watsonx.governance, and OneTrust. If the concern is more “employees are building copilots and we need to know what they can access and prove they are safe”, add an AI security/observability layer.

I can also provide a vendor shortlist mapped to NIST AI RMF / ISO 42001 / EU AI Act / SR 11-7-style MRM requirements if that is the framework your teams use.

Managing model risk for internal copilots requires an approach split across four functional layers: Governance & Compliance (for the legal team), Runtime Security & Guardrails (for the security team), and Observability & Evaluation (for the technical audit trail).

The market features dedicated platforms built to handle enterprise AI risk management across these areas:

1. Dedicated AI Governance & Compliance Platforms

Best for: Legal, policy mapping, risk cataloging, and audit-ready documentation.

• Credo AI: Highly focused on enterprise AI governance, Credo AI Governance Platform bridges the gap between technical teams and legal/compliance. It provides centralized model inventories, policy alignment workflows, risk assessments, and automated regulatory reporting mapped to frameworks like the EU AI Act and NIST AI RMF.
• Holistic AI: Specializes in proactive compliance, algorithmic auditing, and continuous regulatory change monitoring. It helps legal teams assess risk profiles, bias, and legal exposure across internal AI deployments.
• Securiti.ai: Focuses heavily on data-centric AI governance. It maps data sensitivity, discovers shadow AI, and governs data access tied directly to LLM grounding and retrieval-augmented generation (RAG) pipelines.

2. LLM Security & Runtime Guardrail Platforms

Best for: Security teams needing real-time interception, prompt-injection defense, and PII leakage prevention.

• TrueFoundry: Provides a centralized control plane for governing model calls, agent actions, and Model Context Protocol (MCP) tools at the request path level, ensuring runtime policy enforcement.
• Robust Intelligence (by Cisco): Offers automated red teaming, pre-release stress testing, and real-time firewall protection for LLMs to block malicious inputs, jailbreaks, and data exfiltration before they reach the model.
• Check Point AI Security / Lakera / Rebuff: Specialized runtime security layers that intercept prompts and responses to filter out toxic content, hallucinations, and injection attacks in real time.

3. Observability, Evaluation & Tracing Vendors

Best for: Technical risk management, continuous evaluation, and tracing model behaviors for audit logs.

• Fiddler AI: A strong leader in model observability, Fiddler provides deep explainability, drift monitoring, and performance tracking across both traditional ML and generative AI/LLMs.
• Arize AI: Excellent for tracking production LLM performance, detecting hallucinations, and maintaining trace-level evidence of how internal copilots behave over time.
• Braintrust / LangSmith: Dev-focused platforms that double as audit ledgers, allowing compliance teams to view evaluation scores, golden datasets, and prompt versions utilized in production.

Evaluating the Control Points

When your legal and security teams evaluate these options, look closely at where enforcement happens:

• Do you need runtime blocking (stopping a bad prompt before it executes)?
• Or do you need system-of-record governance (tracking approvals, risk assessments, and intended use cases for compliance audits)?
  Most mature enterprise stacks pair a governance platform (like Credo AI) with a runtime security/observability layer (such as TrueFoundry or Arize).

To help narrow down the right fit, could you share:

• Are your internal copilots custom-built (using APIs/LangChain) or deployed via an enterprise ecosystem (like Microsoft 365 Copilot)?
• Do your primary compliance drivers lean more toward data privacy/security (PII/SOC2) or regulatory frameworks (EU AI Act, NIST)?

Yes. For internal copilots, I’d look at this as an AI governance / model-risk stack, not just an LLM security product. The strongest options currently fall into a few buckets:

A recent 2026 buyer comparison similarly separates dedicated AI-governance platforms such as IBM, Credo AI, OneTrust and Holistic AI from observability/runtime-security products.

For your specific use case

If you're talking about employees using internal copilots—for example, a legal copilot, engineering copilot, HR copilot, or enterprise ChatGPT/Claude deployment—I would prioritize:

1. AI system inventory — every copilot, underlying model, data source, owner and business purpose.
2. Risk classification — low/medium/high/critical based on data sensitivity, autonomy and consequences.
3. Pre-deployment assessment — privacy, security, IP, regulatory, model-risk and third-party/vendor review.
4. Approval workflow — legal + security + business owner sign-off, with evidence retained.
5. Continuous monitoring — hallucination/quality, prohibited use, data leakage, prompt injection, access violations and model changes.
6. Audit trail — who approved what, which model/version was used, what controls applied and when.
7. Policy mapping — ideally NIST AI RMF, internal policies, applicable regulations and your existing GRC framework.

That's broadly consistent with NIST's AI RMF, whose core functions are Govern, Map, Measure and Manage, with risk management intended to continue throughout the AI lifecycle.

My shortlist

If you're Microsoft-heavy: start with Microsoft Purview. It now explicitly covers security and compliance controls for Microsoft 365 Copilot, Copilot Studio, Microsoft Foundry and several third-party enterprise AI applications.

If "model risk management" is literal—i.e., you want something analogous to traditional bank/financial-services model governance: IBM watsonx.governance is probably the first platform I'd evaluate. Its Governance Console explicitly supports model-risk governance, operational risk and regulatory-compliance workflows.

If you need a cross-vendor AI governance layer: I'd put Credo AI, OneTrust and Holistic AI on the evaluation list.

One important distinction: governance ≠ runtime security. For copilots/agents that can access systems or take actions, you may also need an AI security/LLM gateway layer. Microsoft, for example, explicitly frames agent governance around identity, data permissions, observability, lifecycle management and operational controls.

If you tell me your stack (Microsoft/Google/AWS, ChatGPT/Claude/Copilot, and whether you're in financial services/healthcare/etc.), I can narrow this to 3 vendors and give you a legal/security-focused evaluation matrix with questions to ask each vendor.

Managing risk for internal copilots requires tools that bridge two distinct layers: Policy & Compliance Governance (inventories, risk assessments, and regulatory frameworks like NIST AI RMF or EU AI Act) and Runtime/Observability Governance (live monitoring, prompt/response guardrails, and audit logging).

The industry has moved beyond static spreadsheets into purpose-built platforms categorized below by how your legal and security teams can deploy them:

1. Policy-Driven AI Governance & Compliance Platforms (For Legal & Risk)

These platforms act as the system of record for model inventories, risk scoring, and mapping compliance to frameworks like NIST AI RMF, ISO 42001, and the EU AI Act.

• Credo AI : Highly regarded for policy-driven compliance. It allows governance teams to define custom risk policies, assess internal copilots against ethical and regulatory standards, and continuously track adherence across different business units.
• IBM watsonx.governance : Built for heavily regulated enterprises. It integrates deeply into broader traditional Governance, Risk, and Compliance (GRC) stacks, tracking model lineage, drift, bias, and policy violations across various foundational models.
• OneTrust AI Governance : Ideal if your organization already uses OneTrust for data privacy or vendor risk. It extends existing GRC workflows to inventory internal copilots, run privacy impact assessments, and document approval workflows.

2. Agent & Model Observability & Discovery (For Security & Engineering)

These platforms focus on the technical reality of what your copilots are doing—discovering unsanctioned shadow AI, monitoring live behavior, and logging the data trail.

• Arthur : Purpose-built for the agentic era via its Agent Discovery & Governance (ADG) platform. It shines at tracking multi-cloud and multi-model deployments, monitoring when a copilot attempts unexpected or unauthorized actions.
• Fiddler AI : Specializes in deep model observability, explainability, and post-deployment monitoring. It helps security teams audit why a copilot generated a specific output, detecting hallucinations, data leakage, and behavioral drift.

3. Runtime Gateways & Guardrails (For Real-Time Interception)

If your security team needs to actively block PII leaks, prompt injections, or policy-violating outputs before they hit the user or log storage, a runtime gateway is required.

• Braintrust or Maxim AI : Provide robust evaluation, testing, and runtime guardrails that sit in the execution path. They catch prompt injections, filter toxic/unauthorized outputs, and measure regressions whenever your internal copilot configuration updates.

To help narrow down the right tool stack, tell me:

• Are your internal copilots built on a single hyperscaler/vendor (like Azure OpenAI) or a multi-model environment?
• Does your team lean more toward needing a policy/documentation registry for audits or live runtime blocking for security?