Generative AI and Cybersecurity: A
revisited classic

Security does not have to be a barrier to tapping into generative AI’s power. When done well, it can accelerate value creation.

Integrating generative AI (Gen AI) into business environments involves addressing critical needs for robust data security, ethical considerations, and effective risk management. It’s essential to protect sensitive information, ensure compliance with regulations, and maintain the integrity of AI models to prevent issues like data leaks, biased outcomes, and model hallucinations. Governance frameworks, secure infrastructure, and continuous monitoring play vital roles in mitigating these risks. A balanced approach is necessary to harness the potential of Gen AI while safeguarding against its inherent challenges.

In this PoV, our expert, Michael Wasielewski, discusses the challenges and strategies for securely integrating Gen AI into business operations, emphasizing data protection, ethical considerations, risk management, and regulatory compliance.

Key takeaways

• Data protection: Learn how to safeguard your data against leaks and unauthorized access.
• Risk management: Understand the importance of governance and risk frameworks.
• Model integrity: Discover strategies to ensure the trustworthiness of AI outputs.
• Infrastructure security: Get insights into securing your AI applications and infrastructure.

Capgemini’s AI and cybersecurity experience

At Capgemini, we combine our expertise in AI and cybersecurity to help you navigate the complexities of Gen AI. Our solutions are designed to accelerate innovation while ensuring robust security and compliance. Our experts and partners deliver advice, systems, tools, and operations to help you achieve the promise of Gen AI quickly and securely.