OpenAI’s Sam Altman Warns That Personalized AI Raises Major Security Risks

OpenAI CEO Sam Altman said that AI security will likely define the next phase of artificial intelligence development, emphasizing the growing need to safeguard advanced models from manipulation, data leaks, and malicious use. Speaking at Stanford University, Altman urged students to study AI security, calling it “one of the best areas to go into right now.”

AI Security: The Next Frontier

Altman suggested that “AI safety” concerns—once framed in terms of ethics and control—are shifting toward “AI security,” focusing on how systems can be technically protected from external attacks and internal vulnerabilities.

“We are soon heading into a world where a lot of the AI safety problems that people have traditionally talked about are going to be recast as AI security problems in different ways,” Altman said. He added that adversarial robustness—protecting models from being tricked into behaving unexpectedly—has become “quite serious.”

The remarks reflect a growing shift in the AI community. As generative systems like ChatGPT become more capable and interconnected, security experts are now focusing on threats such as prompt injections, model inversion, and data exfiltration—attacks that can manipulate or extract sensitive data from AI systems.

Personalization: A Double-Edged Sword

Altman also highlighted AI personalization as an emerging security risk. While users value personalized experiences, systems that learn from individual interactions and connected accounts could become prime targets for exploitation.

“People love how personalized these models are getting,” Altman said. “But what you really don’t want is someone to be able to exfiltrate data from your personal model that knows everything about you.”

He compared AI trust to human relationships—such as confiding in a spouse who understands social context—but noted that models lack this judgment. A personalized AI assistant handling both private and transactional data could unintentionally reveal sensitive information to third parties.

This intersection of personalization and automation introduces a complex new challenge: ensuring that AI assistants can integrate with external systems securely while maintaining user privacy.

AI as Both Risk and Defense

Altman concluded that AI will play a dual role in future cybersecurity—both as a potential attack vector and as a defense mechanism. “It works both directions,” he said. “You can use it to secure systems. I think it’s going to be a big deal for cyber attacks at various times.”

This mirrors trends across the cybersecurity sector, where AI tools are increasingly used to detect breaches, automate incident response, and defend against adversarial threats. However, the same technologies can be weaponized by attackers to generate convincing phishing attempts or probe system weaknesses at scale.

Industry Implications

Altman’s comments underscore a strategic realignment in AI development priorities. As large language models become more deeply embedded in personal, enterprise, and governmental systems, security engineering is set to become as essential as model training or alignment research.

Analysts expect rising demand for AI security specialists capable of building robust safeguards into model architecture, data pipelines, and access layers. Universities and research institutions are already expanding programs focused on adversarial AI, cybersecurity, and trust frameworks.