IBM Guardium

As generative AI tools become more integrated into the enterprise landscape, their benefits are impressive—unlocking new efficiencies, creating sophisticated content, and transforming customer experiences. But along with these advantages come unique security and governance challenges. In fact, 84% of CEOs see cybersecurity, privacy, and accuracy as barriers to adoption of generative AI.¹ Yet, only 24% of current generative AI projects have a component to secure the initiatives.²

To ensure generative AI implementations are secure, compliant, and accurate, enterprise developers and security teams must take a proactive stance in managing the risks associated with these technologies. Below, we’ll explore why security and governance are critical for generative AI and offer practical tips for securely deploying and governing these tools in your organization.

Why Security and Governance Matter 

Generative AI models are unlike traditional applications because they can generate new data, imagery, code, and even conversational responses based on patterns in the vast datasets they’re trained on. This complexity comes with challenges:

1. Data Security Risks: Generative models are often trained on large datasets, including potentially sensitive or proprietary data. Improper handling of training data or outputs could lead to data leaks, unintended sensitive data exposure, or compliance breaches.

2. Bias and Ethical Concerns: Generative AI models can reflect biases present in their training data. Unmonitored use could result in biased or unethical outcomes, damaging brand reputation and leading to regulatory scrutiny.

3. Model Misuse and Access Control: Models themselves are an attack surface and unauthorized users may want access for misuse. Monitor to ensure only the right people have permissions and access.

4. Regulatory Compliance: Laws governing AI systems such as the EU AI Act have been enacted and data protection laws, like GDPR and CCPA, still impact generative AI. Non-compliance can lead to fines and reputational damage.

Practical Security Tips for Generative AI

Implementing robust security measures can help enterprises mitigate the risks associated with generative AI. Here are key domains to consider:

1. Secure Model Training and Deployment Environments: Ensure secure infrastructure for model training and deployment. Use encrypted storage for sensitive data and secure access protocols. Leverage virtual private clouds, firewalls, and network isolation to reduce exposure.

2. Establish Data Governance Policies: Discover and classify sensitive data to understand what data sources may be used for your AI use cases and who can access those data sources. Define clear policies on what data should be used for training and grounding into generative AI systems. Adopt anonymization techniques for sensitive data and apply protection to policies that are violated. 

3. Utilize Security Monitoring and Logging: Deploy monitoring and logging tools to track model activity, flag unusual behavior, and ensure models operate as expected. Anomaly detection and logging also provide an audit trail, which can be crucial for troubleshooting and compliance.

4. Mitigate Security Vulnerabilities: Use tools to identify and detect security vulnerabilities throughout the interactions of applications, models, and data sources. Apply remediation tactics such as enforce least-privilege access to prevent unauthorized model training or usage.

Governance Best Practices for Generative AI

Governance is essential in ensuring responsible AI use and adherence to compliance requirements. Here’s how to create a framework for governing generative AI in the enterprise:

1. Develop Transparency and Explainability Protocols: Provide mechanisms for users and auditors to understand how generative AI models make decisions. Using automated metadata capture, create documentation that describes model behavior, data provenance, and any limitations.

2. Regular Model Audits and Bias Testing: Periodically audit models to detect and mitigate potential biases. This is especially crucial for customer-facing applications, where biased outputs could impact user trust and brand image.

3. Document and Track AI-Generated Content for Compliance: Keep thorough records of all AI-generated content, including data sources, generation dates, and modifications. Tracking content lineage helps in defending against IP claims and ensures that outputs meet internal quality standards.

4. Risk Management and Guardrails: Implement robust risk management protocols to minimize unintended consequences of generative AI. This includes:

• • Usage Policies: Define and enforce clear policies on acceptable use, ensuring AI is not used for harmful or unethical purposes.
  • Human Oversight: Require human review for critical or sensitive outputs, especially in regulated industries like finance or healthcare.
  • Content Moderation: Deploy tools to identify and flag inappropriate, misleading, or harmful content generated by AI.

Evolving Your Generative AI Security and Governance Strategy

Generative AI will continue to evolve, introducing both new capabilities and risks. Enterprises that invest in a robust security and governance framework now will be better positioned to adapt to these changes, and more resilient in the face of evolving regulatory landscapes.

A tightly integrated approach to AI security and governance is crucial. Security and governance teams must operate in sync, with:

• Security Teams Understanding Business Criticality: Security teams should prioritize AI use cases based on their importance to the organization, ensuring robust safeguards are in place for high-impact applications.
• Governance Teams Understanding Security Posture: Governance teams should have visibility into the security risks and mitigations associated with each AI deployment, enabling informed oversight of compliance and ethical considerations.

Governance is not the sole responsibility of IT or security. Involve stakeholders from legal, compliance, HR, and product teams to create a cross-functional AI governance committee. This team can guide policies on acceptable AI uses, regulatory compliance, and ethical concerns.

By embracing these integrated practices, enterprises can maximize the transformative potential of generative AI while safeguarding their data, models, and reputation. With a well-executed strategy, your organization can confidently innovate, stay ahead of compliance requirements, and build trust with stakeholders in an AI-driven future.

To learn how IBM can help your organization operationalize security and governance for AI, join our webinar on Feb. 26, 2025.

¹Source: IBM Institute for Business Value. 2023 IBM IBV Generative AI CEO Pulse

²Source: IBM Institute for Business Value. Securing Generative AI