Securing Generative AI: A strategic framework for security leaders

Navigate the complexities of GenAI adoption with a comprehensive framework that integrates governance, technology, and adaptive security measures

Partner content As generative AI (GenAI) technologies rapidly evolve, security leaders face the challenge of harnessing their transformative potential while mitigating significant security, privacy, and compliance risks. To ensure the safe, responsible, and ethical use of GenAI, organizations must adopt a strategic approach that encompasses governance, technology controls, and data protection.

Securing GenAI is an ongoing journey that requires a structured approach, balancing immediate priorities with long-term maturity. Security leaders can progressively build their security posture by following several principles

1. Establishing a robust AI governance framework

The foundation of effective GenAI security lies in a well-structured governance framework. Security leaders can address potential risks in advance by aligning AI initiatives with organizational values, compliance requirements, and ethical considerations. Consider setting up a cross-functional AI governance committee to oversee projects, manage tool usage, and ensure compliance with global standards.

Define ethical use expectations and conduct regular impact assessments to evaluate organizational, economic, and societal impacts. This will make it easier to establish ongoing training programs to educate users on responsible AI use, potential risks, and security best practices. Regularly update these governance models to reflect evolving AI capabilities and regulatory changes.

2. Implementing anticipatory technology controls

To protect GenAI tools from internal and external threats, security leaders must implement technology controls. Ensure secure deployment of AI systems by incorporating robust logging mechanisms to monitor user interactions and AI-generated responses. Protecting the integrity of AI models and their underlying data is critical, requiring strict access controls, regular security audits, and the use of cryptographic hashing to detect tampering.

Equally important is the vetting of third-party components. Assess the security of libraries, external models and services integrated into the AI ecosystem. To combat the risks posted by unauthorized AI usage, or ‘shadow AI,’ organizations should limit access to only approved tools and establish continuous discovery mechanisms to detect and address unapproved deployments in real-time.

3. Strengthening data access and usage controls

Securing data access and usage is essential for preventing breaches and ensuring GenAI tools operate within organizational policies. To achieve this, security leaders should implement a granular identity security model that incorporates just-in-time access provisioning, multi-factor authentication, and least-privilege principles.

Safeguarding training data is equally critical and involves removing sensitive information, applying data classification and tagging, and using encryption to protect it throughout its lifecycle. Real-time monitoring of input data is necessary to uphold privacy, ethical, and bias standards, ensuring responsible AI use. Additionally, AI-generated outputs must be secured through automated filtering, encryption, tagging, and comprehensive logging, creating a transparent and auditable trail of system activity.

4. Using essential tools and capabilities

To effectively secure AI environments, security leaders must implement a comprehensive identity security stack that is designed to address the unique challenges posed by AI systems and the volume of machine identities requiring access to critical data and systems. This begins with Privileged Access Management (PAM) to control and monitor high-level access to critical AI assets, reducing the risk of misuse or compromise. Adopt a zero-trust AI identity security framework, emphasizing intelligent authorization, just-in-time privilege elevation, and comprehensive compliance monitoring.

Security policies should also be flexible, with adaptive policy management allowing for real-time adjustments driven by AI insights and evolving operational requirements. Lastly, protecting the integrity of AI models themselves is critical – this involves implementing safeguards against adversarial attacks and unauthorized modifications, ensuring models remain secure and trustworthy throughout their lifecycle.

Avoid common pitfalls

While securing GenAI offers immense benefits, security leaders must remain aware of potential pitfalls that can undermine their efforts. These include underestimating shadow AI, over-relying on AI outputs without human validation, implementing incomplete access controls, failing to adapt to evolving threats, and overlooking supply chain complexity.

By adopting a strategic framework for securing GenAI, security leaders can confidently navigate the complexities of AI adoption while mitigating risks. With a focus on governance, technology controls, data protection, and adaptive security measures, organizations can harness AI’s transformative potential without compromising security, trust, or compliance.

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Partner content provided by Delinea.