The Unique Risks of Generative AI

Generative AI systems create risks that traditional AI risk management approaches do not adequately address. While a classification model might produce an incorrect prediction, a generative AI system can produce harmful content, leak confidential information, or generate outputs that infringe on intellectual property rights. These risks require specialized management approaches.

The challenge is compounded by the speed at which generative AI capabilities are being deployed across organizations. Many enterprises have moved from initial experimentation to broad deployment in months, often outpacing the development of appropriate risk management practices.

Categories of Generative AI Risk

Understanding the risk categories is essential for selecting appropriate management approaches.

Output Quality Risks include hallucination where models generate plausible but incorrect information, inconsistency where the same question produces different answers, and bias where outputs reflect or amplify biases in training data.

Security Risks include prompt injection where malicious inputs manipulate model behavior, data leakage where models reveal sensitive information from training data or user interactions, and model jailbreaking where users bypass safety guardrails.

Compliance Risks include regulatory violations particularly in regulated industries, intellectual property infringement in generated content, and privacy violations when personal data appears in outputs.

Operational Risks include unpredictable costs as usage scales, dependency on external model providers, model degradation over time, and lack of reproducibility in outputs.

Platform Approaches

Generative AI risk management platforms generally take one of several approaches.

Gateway Platforms sit between users and AI models, filtering inputs and outputs in real time. They can detect and block prompt injection attempts, screen outputs for sensitive information, enforce usage policies, and log all interactions for audit purposes.

Monitoring Platforms observe model behavior in production and alert on anomalies. They track output quality metrics over time, detect distribution shifts in prompts and responses, identify potential compliance violations, and provide dashboards for risk visibility.

Testing Platforms assess model behavior before deployment through automated red teaming, bias evaluation suites, robustness testing against adversarial inputs, and benchmark comparisons across model versions.

Integrated Platforms combine elements of all three approaches into a unified risk management solution.

Risk Assessment Framework

Develop a risk assessment framework specific to your generative AI use cases. For each use case, evaluate the sensitivity of the data involved, the consequences of incorrect or harmful outputs, the audience for generated content, regulatory requirements, and reputational risk.

Use this assessment to determine appropriate controls. A generative AI system used internally for draft generation needs different controls than one that generates customer-facing communications or makes decisions affecting individuals.

Implementation Strategy

Begin with visibility. You cannot manage risks you cannot see. Implement logging for all generative AI interactions across your organization. This reveals usage patterns, identifies high-risk use cases, and provides data for risk assessment.

Add controls progressively based on risk. Start with input and output filtering for the highest-risk use cases. Implement monitoring across all deployments. Add automated testing for critical applications.

Establish clear ownership. Each generative AI deployment should have an identified risk owner who is accountable for ensuring appropriate risk management is in place.

Review and adapt continuously. The generative AI landscape evolves rapidly, and your risk management practices need to evolve with it.

ibl.ai provides organizations with full ownership of their generative AI infrastructure, including the ability to run any LLM on their own servers. This ownership model fundamentally simplifies risk management because organizations have complete visibility into and control over their AI systems rather than managing risk through contractual arrangements with external providers.