Securing AI: Similar or Different?
The paper discusses the importance of securing AI systems, focusing on the development lifecycle, role of CISOs, and the role of customers and developers. It highlights the vulnerability of AI systems to attacks, such as malicious data, hacked systems, and data breaches. The paper also emphasizes the need for regular security assessments and validation. The approach to securing AI depends on the type of AI, use cases, and role in the AI ecosystem. The paper does not cover consumer-focused AI, security products, attacker use, or ethical implications.
Unlocking the Future: A Blueprint for Securing AI - Tailored for Innovators Harnessing AI Power:
- AI is a rapidly growing technology, impacting our lives and work. Security implications of AI technologies include safety and privacy concerns. AI systems can be vulnerable to attacks, including manipulation of decision-making and gaining access to sensitive data. Securing AI systems involves ensuring they are part of cyber governance, protected from malicious attacks, and regularly reviewed.
Scope of the Paper
- Covers cybersecurity topics related to developing, deploying, and utilizing AI systems by businesses and organizations. Does not cover use of consumer-focused AI delivered via the web, security products powered by AI-based features, use of AI by attackers, and ethical implications of AI for humanity.
AI Development Process
- The AI development life cycle consists of three steps: opportunity discovery and problem definition, data collection and preparation, and model design and development. The paper does not cover the use of consumer-focused AI, security products powered by AI-based features, use of AI by attackers, or ethical implications of AI for humanity.
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