Mitigating AI's new risk frontier: Unifying enterprise cybersecurity with AI safety

Mitigating AI's new risk frontier: Unifying enterprise cybersecurity with AI safety AI security vs. AI safety AI security AI safety Examples of security and safety risks The new risk frontier: Demystifying “safety” Should the AI model get all the attention? Unifying AI security with enterprise cybersecurity Moving forward Conclusion Learn more Red Hat Product Security About the authors Ishu Verma Florencio Cano Gabarda More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share These are exciting times for AI. Enterprises are blending AI capabilities with enterprise data to deliver better outcomes for employees, customers, and partners. But as organizations weave AI deeper into their systems, that data and infrastructure also become more attractive targets for cybercriminals and other adversaries. Generative AI (gen AI), in particular, introduces new risks by significantly expanding an organization’s attack surface. That means enterprises must carefully evaluate potential threats, vulnerabilities, and the risks they bring to business operations. Deploying AI with a strong security posture, in compliance with regulations, and in a way that is more trustworthy requires more than patchwork defenses, it demands a strategic shift. Security can’t be an afterthought—it must be built into the entire AI strategy. AI security and AI safety are related but distinct concepts. Both are necessary to reduce risk, but they address different challenges. AI security focuses on protecting the confidentiality, integrity, and availability of AI systems. The goal is to prevent malicious actors from attacking or manipulating those systems.