ai-risk
The DeepSeas AI Security Model
October 8, 2024
In the video below, cybersecurity experts dive into how DeepSeas approaches AI risk management. Pat Joyce, VP of Portfolio Management, and John Matis, CISO Advisory Leader, discuss rapid AI adoption, use cases, challenges, opportunities, and the first steps you can take toward AI risk management.
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Rapid Adoption of AI Demands Cybersecurity Transformation
The rapid rise of AI adoption and integration, particularly large language models (LLMs) like ChatGPT, presents significant challenges for cybersecurity professionals, demanding that security frameworks and risk management strategies be in continous state of transformation. The sheer speed of adoption has left many organizations scrambling to keep up, often without fully understanding the risks involved or new vulnerabilities that need immediate attention.
Common AI Use Cases and the Security Implications
AI’s versatility is evident in its widespread use across various organizational functions. From product development to sales analytics, the applications of AI are extensive. Marketers are testing AI tools to generate content, while sales teams use AI-driven insights to streamline the sales processes. Developers are using AI to write and debug code.
These and many other AI use cases come with their own set of security implications. One of the most pressing issues is the risk of intellectual property leakage. As employees and contractors use AI tools, sensitive data can inadvertently make its way into cloud-hosted services or into the hands of cyber criminals. Another significant challenge is the introduction of malicious code. As R&D teams race to integrate AI into their products and services, the risk of incorporating unvetted third-party software increases. This could lead to intrusion risks, malware introduction, and the creation of bad data.
Leveraging Leading Frameworks and Standards
To navigate the complex landscape of AI-related risks, organizations can turn to established frameworks and standards. The NIST AI Risk Management Framework and the ISO 42001 standard are two such resources that provide comprehensive guidelines for managing AI risks.
The NIST framework offers flexible guidelines that can be adapted to various industries, providing a robust foundation for implementing compensating controls. On the other hand, the ISO 42001 standard focuses on the entire AI system lifecycle, from conception to deployment, ensuring that AI systems are developed and used responsibly.
Another valuable resource is the OWASP top 10 security risks for LLM applications. This list provides practical recommendations for mitigating common vulnerabilities associated with AI, such as prompt injection attacks and supply chain vulnerabilities. By aligning with these frameworks and standards, organizations can build a solid foundation for secure AI adoption.
Practical Steps for Building an AI Risk Management Program
Building an AI risk management program involves a multi-layered approach that starts with strategy and governance before moving on to technical solutions. The first step is to develop a risk strategy that includes understanding data classification, assessing specific AI-related risks, and adopting acceptable use policies.
Next, governance plays a crucial role in ensuring that the workforce is aware of the risks and understands the guidelines for secure AI use. This involves user awareness training and developing a culture of security within the organization.
Finally, implementing technical solutions is essential for discovering AI usage, testing for vulnerabilities, and controlling risks. Tools that help in discovering AI use within the organization, testing vulnerabilities, and setting controls are invaluable. Additionally, methods for detecting active threats and responding to them are crucial for maintaining a secure environment.
5 Key Takeaways for Securely Adopting AI
To sum up, securely adopting AI technologies requires a comprehensive approach that includes understanding risks, leveraging established frameworks, and implementing robust governance and technical controls. Here are the key takeaways for organizations looking to secure their AI adoption:
- Understand and Assess AI Risks: Conduct thorough risk assessments to identify potential vulnerabilities and threats specific to AI technologies.
- Validate AI Technologies: Ensure that the AI tools and technologies being used are validated and approved for secure use within the organization.
- Adopt Guidelines for Meaningful AI Use: Develop and enforce policies that provide clear guidelines for the secure and responsible use of AI.
- Invest in Workforce Education: Educate employees about AI risks and best practices to build a culture of security and resilience.
- Implement Technical Controls: Use advanced tools and methods to discover AI usage, test for vulnerabilities, and control risks effectively.
By following these steps, your organization can harness the power of AI while ensuring that they remain secure and compliant. Remember, the journey to secure AI adoption is ongoing, and continously transforming your cybersecurity program is key.