Beyond the hype: A CIO's guide to LLM risk management

Questions CIOs should ask about each LLM deployment

When approaching LLM deployments, CIOs must evaluate questions to ask internal teams across the organization, as well as potential vendors.

Questions for internal teams

1. What business decision or workflow does this LLM influence? LLM risk management requires a named business owner, a documented process map and a fallback when the model is unavailable.
2. Does the system only generate content, or can it take other actions? Risk changes materially when an LLM can send emails, trigger workflows or approve transactions. CIOs should require a precise action register and specify which actions require human approval.
3. What enterprise systems, APIs, tools or databases can the LLM access? Connectors and access capabilities define the LLM security blast radius. Shared service accounts and broad API scopes without least-privilege review are security red flags.
4. What data does the system use, and how is it being used? The baseline for AI compliance includes a data-flow diagram covering prompt inputs, embeddings, logs and downstream outputs.
5. Does the system use retrieval-augmented generation, fine-tuning, prompt engineering, tool calling or autonomous agents? Different architectures fail differently. For example, agentic AI introduces goal-hijacking risks that copilot deployments do not.
6. What happens when the model is wrong? CIOs should look for defined failure modes, safe fallbacks, escalation rules, confidence or uncertainty handling, and clear user guidance on when not to rely on the output.
7. What human approval or escalation points exist? Human oversight is only a control when it is specific, timed and enforceable. Approval gates where the agent decides when to escalate are not controls.
8. How are outputs validated before being used in downstream systems? Output passed directly into scripts or workflows without schema or business-rule validation is a critical LLM security gap.

Questions to ask vendors

1. How does the LLM handle confidential, regulated, personal or customer data? Data leakage occurs through prompts, embeddings, logs and downstream actions. Personal data included without policies and embeddings treated as non-sensitive are AI compliance red flags.
2. How are prompts, outputs and user interactions logged? Auditability is essential for incident response and AI compliance. Sensitive prompts stored without protection or linkage between requests, tool calls and final actions are red flags.
3. Where does the data go once it is in the system? CIOs should look for data-flow documentation, region details, sub-processor transparency, support-access rules and explicit statements about provider access to inputs, outputs and training data.
4. Can the vendor use data entered into the system for model training or service improvement? Training commitments are product-specific and CIOs should confirm whether the commitment covers prompts, outputs, fine-tuning data and logs.
5. What are the retention, deletion and residency controls? AI governance fails on the data lifecycle before it fails on model quality. Residency claims excluding telemetry and deletion commitments without timings are inadequate.
6. How does the tool protect sensitive data? Generic enterprise-grade security language is not a control description. CIOs should verify encryption, identity access management controls, private networking and key management.
7. What level of system access does the vendor require? Over-permissioned agents are one of the clearest paths from LLM misuse to enterprise compromise. Shared credentials with no per-request authorization checks should not happen.
8. How are prompt injection and indirect prompt injection mitigated? Prompt injection remains a common LLM security threat in agentic AI deployments.
9. How are model updates, system-prompt changes and vendor-side changes communicated? LLM systems can change behavior without a customer-side code release. Silent model swaps and no version-pinning options for regulated deployments are unacceptable.
10. What testing has been done for bias, toxicity, hallucination, leakage, jailbreaks and unsafe tool use? Benchmark scores alone, with no adversarial or red-team evidence or re-testing after configuration changes, do not satisfy AI governance requirements.
11. What audit evidence is available? AI governance fails under scrutiny when there is no evidence trail. CIOs should require architecture documents, risk assessments and independent attestations.
12. What contractual protections exist, and what is the exit plan if the vendor, model or regulatory posture changes? Contract terms must cover data ownership, breach notification, portability and audit rights, with a fallback plan that does not depend on vendor cooperation.

Building an LLM governance framework

A defensible AI governance framework should be lightweight for low-risk use cases, but strict for systems that touch sensitive data, regulated decisions or autonomous action. The most durable designs align business ownership, LLM security controls, data governance, procurement and audit evidence around the full LLM system rather than the model alone.

1. Establish ownership and accountability. The CIO owns the enterprise operating model. The CISO owns LLM security and incident response. The chief data officer and privacy teams own data controls. Legal and compliance own regulatory interpretation. Procurement owns AI-specific vendor diligence, and business owners remain accountable for the context of use and error tolerance.
2. Define policies for acceptable AI usage. Policies should cover approved data classes, permitted actions, output-use restrictions and prohibited use cases, with clear escalation paths for edge cases.
3. Classify LLM use cases by risk. A tiered classification that distinguishes content generation, decision support and autonomous actions should have proportionate controls and prevent low-risk approvals from covering high-risk agentic AI deployments.
4. Create an enterprise AI inventory. When registered, every LLM deployment, including embedded AI in SaaS tools and vendor-managed models, should include its data classification, business owner and risk tier.
5. Implement LLM security controls. Controls must address prompt injection, access scoping, output validation and secrets management.
6. Implement data governance controls. Data governance for agentic AI must specify what enters the prompt, what is retrieved, what is stored in embeddings and what flows downstream.
7. Govern agentic AI separately. Agentic AI requires its own governance layer covering goal specification, tool-use constraints and human escalation triggers distinct from those applied to copilots.
8. Build monitoring and assurance. Operational monitoring should cover output quality, cost, error rates and anomalous tool calls with a defined review cadence and clear remediation ownership.
9. Manage third-party and vendor risk. AI compliance requires service-specific vendor diligence updated when models or terms change and backed by contractual rights to audit and exit.
10. Prepare for regulation and audit. Map current controls to NIST AI Risk Management Framework, ISO 42001 and the EU AI Act, identify gaps early and build the evidence trail that regulators will require.

Kashyap Kompella, founder of RPA2AI Research, is an AI industry analyst and advisor to leading companies across the U.S., Europe and the Asia-Pacific region. Kashyap is the co-author of three books, Practical Artificial Intelligence, Artificial Intelligence for Lawyers and AI Governance and Regulation.