Securing the Future of AI: New Guidelines for Developing Secure and Responsible AI Systems

Artificial intelligence (AI) promises immense benefits but also poses novel risks if not developed responsibly. To help organizations build secure, trustworthy AI, the UK's National Cyber Security Centre (NCSC) has published practical new guidelines for the entire AI system development lifecycle.

What's the aim?

These guidelines provide a vital framework for developing resilient AI systems against attacks and privacy violations, behaving as intended, and operating safely and reliably.

Specifically, they empower organizations to:

• Understand unique AI security vulnerabilities like data poisoning or model extraction attacks.
• Make security a priority from day one, not an afterthought added at the end.
• Create more transparent AI systems through thorough documentation of data sources, model limitations, etc.
• Take ownership of and mitigate risks introduced to customers and end users.
• Collaborate across industry, government and academia to advance best practices.

Ultimately, the guidelines enable AI innovation while effectively managing emerging risks - realizing immense benefits while protecting sensitive information, businesses, and the public.

How can organizations apply them?

The guidelines outline four critical phases:

• Secure Design: Threat model AI systems and make security a core requirement early on, not just an add-on. Choose supply chains, architectures, and training data wisely.
• Secure Development: Establish strong version control, monitor assets, document models thoroughly, and ensure rigorous coding practices.
• Secure Deployment: Isolate and sandbox systems, implement cryptographic controls on model access, perform extensive security testing, provide guidance to users on limitations.
• Secure Operations: Monitor for anomalies through logging and behavioral tracking, have incident response plans, and share vulnerabilities and best practices.

What does governance look like?

Effective AI security requires comprehensive governance:

• Cross-functional teams, including security, legal, and engineering.
• Executive-level oversight, such as a Chief AI Security Officer.
• Review boards assessing risks before deployments.
• Mandatory training and testing like red teaming.
• Published standards aligned with guidelines.
• Controls on data/model access, documentation, monitoring, and disclosure.
• Continual auditing and improvement as threats evolve.

With involvement across the organization and iteration as technology advances, secure AI practices become ingrained.

The ideal individual to assume this role should have much more than top technology and AI skills, says Colin Reeves , principal data and AI recruiter at ConSol Partners in Los Angeles. They should be a champion for smart AI adoption, and be able to recognize and balance AI benefits with risks. They should be able to work collaboratively with many departments in developing AI strategy and vision, and they should be able to design and target relevant business use cases, assess project outcomes, and measure ROI in each case.

First steps to take now?

Organizations developing AI should:

• Identify gaps in current practices compared to guidelines.
• Provide training to data scientists, developers, and leadership on emerging threats.
• Implement a "secure by default" mentality early in the design process.
• Protect models/data with cryptography, access controls, and anomaly monitoring.
• Establish documentation, logging, and incident response tailored to AI.
• Release AI only after extensive security testing.
• Share vulnerabilities and collaborate across the ecosystem.
• Make AI security a leadership priority.

By taking concrete steps aligned with these guidelines now while continuously reassessing, organizations can maximize AI's immense potential while minimizing novel risks.