Securing generative AI platforms and adopting AI for cybersecurity

Generative AI presents enterprises with transformative opportunities but also introduces critical security risks that must be managed effectively. The adoption of AI-driven technologies increases concerns around data privacy, unauthorized access, adversarial threats and governance complexities. As organizations integrate AI into their workflows, a structured security approach is necessary to mitigate risks while maximizing the benefits of AI.

Here we examine AI security from two key reference points:

• Strengthening the security of generative AI platforms: This involves addressing security risks inherent to adopting generative AI platforms, defining security roles across key stakeholders, implementing strong cloud-native security controls and enhancing security operations.
• Adopting AI for cybersecurity: AI itself is a powerful tool for enhancing security posture. From automating threat detection to improving vulnerability management, AI is redefining how enterprises approach cybersecurity challenges.

Exploring both these reference points helps provide a comprehensive guide to minimizing the security risks of generative AI platforms while harnessing AI to bolster cybersecurity defenses. Organizations should adopt a multilayered security strategy, align with regulatory requirements and use AI-driven automation to stay resilient against evolving cyberthreats.

The rapid adoption of generative AI brings both innovation and security challenges that organizations cannot afford to overlook. Understanding AI security from both defensive (enhancing the security of AI platforms) and offensive (adopting AI for cybersecurity) perspectives is critical for several reasons:

• Protection against emerging threats
• Data privacy and compliance
• Mitigating business and operational risks
• Shared responsibility in cloud AI security
• Enhancing cybersecurity capabilities
• Future-proofing against AI-driven regulatory changes
• Promoting ethical and responsible AI usage

By considering these perspectives, organizations can securely harness the power of AI while mitigating risks, maintaining compliance and strengthening their overall cybersecurity posture.

Minimizing the security risks of generative AI platforms, particularly LLMs, is essential to help prevent significant security breaches and protect the privacy, accuracy and legitimacy of generated content. Based on real-world experience from IBM's work with clients across Europe, several critical risks have been identified, along with countermeasures to mitigate these threats:

• Prompt injection: Attackers manipulate user input to influence the model's behavior or steal sensitive data. To mitigate this risk, implement strict input validation and educate users on safe online practices.
• Insecure output handling: Improper sanitization of LLM-generated output can lead to vulnerabilities. Maintain robust error handling and sanitize inputs before use.
• Training data poisoning: Malicious data that corrupts the training set can cause unpredictable or biased LLM behavior. Use trusted data sources, regularly sanitize inputs and review model outputs for discrepancies.
• LLM lifecycle issues: Risks occur during training, model deployment and the use of plug-ins. Validate model origin, maintain secured deployment and handle output interpretation with care.
• Sensitive information disclosure: Direct or indirect leakage of training data or model knowledge can lead to privacy violations. Apply access control, federated learning and data anonymization to reduce risks.
• Excessive privileges: Granting LLMs too much autonomy can result in significant security risks. Limit privileges to essential tasks and enforce strong access controls.

These six critical security risks, along with the associated countermeasures, form a vital part of enhancing the security of generative AI platforms. By proactively addressing these concerns, organizations can help prevent data breaches, protect sensitive information, and enhance the overall integrity and reliability of their AI applications.

Large enterprises developing generative AI applications often use prebuilt LLM services hosted on cloud platforms such as Microsoft® Azure, IBM Cloud®, Google Cloud and Amazon Web Services.

After addressing six critical security risks associated with LLMs, it is crucial to review the infrastructure for vulnerabilities. A proposed risk management approach should involve mapping any identified risks to threats and linking them to specific security controls for preventive and detective measures across cloud platforms. This process includes listing risks, mapping them to threats, categorizing controls into five security domains and establishing compliance mechanisms, often automated through cloud services. The final output is a RACI matrix that details team responsibilities for implementing and validating security controls.

To effectively govern an enterprise AI program, chief information security officers (CISOS) should adopt five key principles:

·      Manage compliance with evolving AI regulations

·      Protect managed LLM platforms by understanding the shared responsibility model

·      Define clear roles for stakeholders such as business teams and data processing officers

·      Establish AI incident management processes to address new cyberthreats

·      Run awareness campaigns to educate employees on the responsible use of generative AI tools, fostering a culture of security and collaboration across the organization

Generative AI is revolutionizing the cybersecurity landscape by offering advanced tools for proactive defense and vulnerability management. While concerns about AI becoming malicious, such as the concept of Skynet, can seem exaggerated, the potential of generative AI to reshape cybersecurity is undeniable.

Generative AI introduces novel capabilities that go beyond traditional defense methods. It enhances attack simulations, automates security tasks, generates realistic training data and supports vulnerability management. By analyzing complex data patterns and generating actionable insights, generative AI provides significant efficiency in combating evolving cyberthreats. Although generative AI can contribute to various aspects of cybersecurity, some key areas where it plays a significant role are:

• Accelerated response: Generative AI can help organizations streamline their response to vulnerabilities by integrating diverse information sources and offering real-time insights into exploitability and attack vectors, helping to prioritize and remediate vulnerabilities faster.
• Impact on security workflows: Modern cybersecurity challenges such as vulnerability backlogs and supply chain risks require integrated workflows across departments. Generative AI enables better collaboration by linking workflows, enhancing the speed and effectiveness of vulnerability remediation.
• Cloud-native vulnerability management: Generative AI addresses challenges in cloud-native environments by tracking vulnerabilities, maintaining accurate vulnerability assignment and adapting to dynamic cloud landscapes to enhance security and meet regulatory requirements.

For example, organizations operating in cloud environments can use generative AI to link vulnerabilities to services, thus prioritizing and assigning remediation tasks more efficiently. Generative AI’s dynamic learning capabilities also support continuous adaptation to the evolving cloud landscape, making it an indispensable tool in managing vulnerabilities in cloud-native applications. The image below depicts how generative AI can transform vulnerability management and security workflows.

GenAI and Security Workflows in Vulnerability Management

Generative AI empowers organizations to improve their cybersecurity efforts by streamlining workflows, accelerating response times and enhancing vulnerability management, particularly in complex cloud environments.

Prioritizing research and development in AI for cybersecurity is critical for innovation and resilience against emerging threats. LLMs have proven valuable in decision-making processes, and there is potential for them to play a role in policy enforcement as well. By comprehending vast amounts of data, LLMs might enhance cyberdefense mechanisms in real time. However, this shift must be approached with caution, considering ethical, privacy and regulatory implications.

Collaboration between industry, government and academia is key to unlocking the full potential of generative AI in cybersecurity. This unified approach can help safeguard digital assets and promote a secure cyber landscape. As we continue to navigate the complexities of the digital age, embracing advancements in generative AI offers promising avenues for strengthening cyberdefense capabilities and ensuring a safer, more resilient future.

The best practices around enhancing the security of generative AI are evolving, needing a comprehensive approach that covers authentication, data privacy, adversarial defense, ethical compliance and continuous monitoring. Security must be viewed holistically, not just from a technological standpoint, but also with attention to ethical considerations and regulatory compliance. As the threat landscape evolves, cybersecurity measures must adapt to effectively safeguard generative AI and LLMs against emerging risks.

Learn how to combat key security concerns