AI Risk Assessment & Control Matrix: How elDoc Makes AI Governance Operational

A fundamental challenge with AI Governance is moving from principles and policies to controls that can actually be implemented, tested and demonstrated.

An organization may have an AI policy stating that confidential information must be protected, AI outputs must be reviewed, and AI activities must remain accountable. But the critical governance question is:

Which specific risk does each requirement address, which control mitigates that risk, and how can the organization demonstrate that the control is operating?

This is where the elDoc AI Risk Assessment & Control Matrix becomes important.

elDoc applies a defense-in-depth model in which AI risks are mapped to Preventive, Detective, Corrective and Governance controls. Rather than treating the LLM as a trusted security layer, controls are applied throughout identity, authorization, documents, RAG, AI processing, human validation, auditability and infrastructure.

elDoc AI Risk Assessment & Control Matrix

AI Risk Domain	Key Risk	Risk Exposure	elDoc Control	Control Type	Governance Outcome
Identity & Authentication	Unauthorized person gains access to AI capabilities	Unauthorized access to enterprise knowledge and AI operations	MFA, SSO, identity authentication	Preventive	AI functionality remains within authenticated enterprise access
Authorization	User accesses information beyond authorized scope through AI	Confidentiality breach	RBAC, user/group permissions, document-level access controls	Preventive	Least privilege applies to users and AI
AI Data Access	AI retrieves information the user cannot normally access	Critical information leakage risk	Access-Aware Agentic RAG	Preventive	AI inherits the user’s authorized information scope
Context Leakage	Unauthorized documents enter LLM context	Sensitive information may appear in AI responses	Authorization validation before retrieval	Preventive	Unauthorized information is excluded before LLM processing
Prompt Injection	User attempts to override restrictions through prompts	Unauthorized retrieval or manipulation	Application-enforced authorization independent of prompts	Preventive	Prompt instructions cannot independently expand access rights
Indirect Prompt Injection	Malicious instructions embedded in documents manipulate AI	AI behavior or actions could be influenced by retrieved content	Controlled RAG Context + Permission Enforcement + Controlled AI Actions	Preventive / Governance	Retrieved content cannot independently grant additional authority
Sensitive Data Disclosure	Confidential enterprise information is exposed through AI	Data leakage / confidentiality exposure	Controlled LLM interfaces + access-aware retrieval	Preventive	LLM access to enterprise information remains constrained
AI Data Sovereignty	Enterprise information is processed outside approved infrastructure	Regulatory, sovereignty and confidentiality exposure	On-premise / sovereign AI deployment	Preventive / Governance	AI processing can remain within customer-controlled infrastructure
External AI Dependency	External AI providers create unacceptable data exposure	Third-party and sovereignty risk	Fully air-gapped AI deployment	Preventive / Governance	LLM, RAG, embeddings and vector infrastructure can operate without external AI services
Customer Data & Training	Enterprise documents become part of model training	Loss of control over enterprise information	Separation of customer data from underlying LLM training	Preventive / Governance	Enterprise documents are not used by elDoc to train or fine-tune the underlying LLM
Hallucination	AI provides unsupported or inaccurate answers	Incorrect business decisions	RAG grounding + source references + human verification	Preventive / Governance	AI responses can be validated against enterprise source information
AI Output Accuracy	GenAI incorrectly extracts document values	Incorrect data enters business processes	Field-Level Data Scoring + HITL Validation	Preventive / Detective / Governance	Low-confidence results can be identified and validated
Low-Confidence Data	Uncertain AI results progress automatically	Processing and decision errors	Configurable validation thresholds	Preventive / Governance	Defined results can be routed to human review
Excessive AI Agency	AI performs unintended consequential actions	Operational or business impact	Controlled AI Actions + Human-in-the-Loop	Preventive / Governance	Humans retain authority over consequential operations
AI Document Changes	AI incorrectly classifies, renames, reorganizes or modifies documents	Document integrity and operational risk	Supervised AI operations + HITL	Preventive / Governance	Proposed AI actions can be reviewed, corrected, approved or rejected
AI Decision Risk	Incorrect AI decision advances through a workflow	Incorrect downstream business action	GenAI-Orchestrated Pipeline + HITL + Maker-Checker	Preventive / Governance	Validation can occur before subsequent workflow stages
AI Accountability	Organization cannot reconstruct AI-assisted activity	Governance and investigation gap	Audit logging + user-linked activity	Detective / Governance	AI-assisted actions and approvals can be traced
Critical Operations	Sensitive action occurs without independent validation	Fraud, error or unauthorized operation	Four-Eyes Principle	Preventive / Governance	Critical operations can require additional approval
AI Output Handling	LLM output triggers unintended application behavior	Security / operational impact	Controlled output processing	Preventive	LLM-generated content does not independently execute privileged functionality
Vector / Embedding Security	Vector infrastructure bypasses document permissions	Hidden authorization gap	Authorization-aware retrieval	Preventive	Vector search does not become an independent authorization mechanism
Model Dependency	Enterprise becomes dependent on an unsuitable model/provider	Operational, cost, sovereignty or security dependency	LLM-agnostic architecture	Governance / Corrective	Models can be replaced according to enterprise requirements
AI Supply Chain	External AI components introduce security/dependency risks	Third-party technology exposure	Controlled AI integration + replaceable components	Preventive / Governance	Individual AI components can be controlled, replaced or upgraded
AI Consumption	Uncontrolled AI usage increases cost or degrades service	Financial and operational risk	Usage tracking + resource constraints	Preventive / Detective	AI consumption can be monitored and constrained
AI Resource Exhaustion	AI workload affects application performance	Availability risk	Independent AI infrastructure sizing + concurrency controls	Preventive / Detective	AI capacity can scale independently from the core application
Availability & Resilience	Infrastructure failure interrupts AI-enabled processes	Business continuity risk	High-availability and resilient deployment	Preventive / Corrective	AI-enabled operations can be designed for service continuity

From Risk Assessment to Operational AI Governance

The matrix highlights an important distinction between AI Governance on paper and AI Governance embedded into enterprise operations.

A policy may define what AI should or should not do. Operational AI Governance goes further by connecting each identified risk to a specific control, enforcement mechanism, human authority and auditable evidence.

The elDoc AI Governance Chain

AI Use Case → Risk → Business Impact → Control → Enforcement → Human Oversight → Audit Evidence

This creates a traceable governance chain from the initial risk assessment through to the technical and organizational controls used to manage that risk.

AI Risk	elDoc Control	How It Is Enforced	Governance Outcome
AI exposes confidential information	Access-Aware Agentic RAG	User authorization is validated before document information is retrieved and supplied to the LLM	AI cannot independently expand the user’s authorized information scope
AI extracts an incorrect value	Field-Level Data Scoring + Validation Thresholds	Defined results can be routed to Human-in-the-Loop validation before progressing	Uncertain AI output does not automatically become trusted business data
AI performs an incorrect consequential action	Controlled AI Actions + HITL + Maker-Checker	Human authorization and independent validation can be introduced at defined workflow stages	AI can assist with operations while consequential decisions remain under organizational authority
AI activity cannot be investigated	Audit Logging + User-Linked Activity	Relevant AI-assisted actions, user activity and approvals are recorded within the applicable audit framework	Organizations maintain traceability and accountability for AI-assisted operations

A Simple Example: From Risk to Evidence

Consider the risk of confidential information being exposed through an AI response.

01 — Identify the Risk
AI could potentially retrieve information that the requesting user is not authorized to access.

02 — Establish the Control
elDoc applies Access-Aware Agentic RAG and existing document-level authorization.

03 — Enforce the Control
Authorization is evaluated before enterprise information is retrieved and supplied as context to the LLM.

04 — Maintain Human and Organizational Authority
The LLM does not become an independent authorization layer and cannot grant itself or the user additional document permissions.

05 — Establish Evidence
Applicable document and system audit mechanisms provide traceability of relevant user and document activity.

The result is a complete governance relationship:

Risk Identified → Control Established → Control Enforced → Activity Governed → Evidence Maintained

This is the core of the elDoc approach to AI Governance by Design.

AI Governance is therefore not limited to policies describing acceptable AI behavior. It becomes a combination of technical enforcement, security controls, human oversight, monitoring and auditability embedded directly into the enterprise AI environment.

Four Layers of elDoc AI Governance Controls

The elDoc model can therefore be understood through four complementary control layers:

Control Layer	Purpose	elDoc Examples
Preventive	Prevent unauthorized or unsafe activity before it occurs	MFA, RBAC, Access-Aware RAG, encryption, controlled AI actions, authorization-aware retrieval
Detective	Identify, monitor and reconstruct activity	Audit logs, document audit trails, user-linked AI activity, data scoring, consumption monitoring
Corrective	Recover, adapt or replace when conditions change	Version control, resilient architecture, replaceable LLMs/components
Governance	Keep consequential AI activity under organizational authority	HITL, Maker-Checker, Four-Eyes Principle, validation thresholds, sovereign AI policies

No single layer should be considered sufficient.

Prevent → Detect → Correct → Govern

This defense-in-depth model means that if one control does not completely eliminate a risk, additional controls provide further protection, visibility and human oversight.

AI Governance Must Be Demonstrable

For regulated organizations, saying “our AI is secure” is not enough.

Organizations increasingly need to answer much more specific questions:

What AI risks have you identified?
Which controls address them?
Where are those controls enforced?
Who can override or approve an AI action?
What happens when AI confidence is insufficient?
Can an AI action be reconstructed afterward?
Can you demonstrate that unauthorized information never became part of the AI context?

This is why elDoc approaches AI Governance as an operational control framework rather than simply an AI policy.

The objective is to establish a traceable relationship between:

Risk → Control → Enforcement → Human Oversight → Evidence

That relationship is what turns AI Governance from documentation into an operational enterprise capability.

AI Governance by Design with elDoc

AI Governance should not be added after an AI solution has already been deployed.

It should be designed into the architecture from the beginning.

elDoc brings together enterprise document security, Access-Aware Agentic RAG, controlled LLM interfaces, Human-in-the-Loop validation, Maker-Checker controls, AI data scoring, auditability, sovereign deployment and controlled AI actions within the same governance architecture.

This allows organizations to move beyond the question:

“Can we use GenAI?”

toward the more important enterprise question:

“Can we demonstrate that our GenAI operates within defined risks, permissions, controls, human authority and auditability?”

That is the foundation of AI Governance by Design.