IT leaders are now focusing on generative AI, applying it across various business-critical areas like marketing, design, product development, data science, operations, and sales. Outside the enterprise, generative AI is also being utilized for important humanitarian issues, such as vaccine development, cancer detection, and environmental and social governance initiatives like resource optimization.
However, each use of generative AI carries significant security risks, including concerns about privacy, compliance, and the potential loss of sensitive data and intellectual property. These risks are expected to grow over time.
Organizations need to plan generative AI projects with both current and future risks in mind, balancing innovation with user trust, and prioritizing privacy and authenticity. Generative AI poses unique threats, like more sophisticated phishing attacks through deep fakes and identity fraud, and there are also concerns about users inputting sensitive data into AI models, which could lead to violations of privacy regulations.
Despite the significant data requirements of large language models (LLMs), security measures exist for securing raw data and preventing leaks. However, the main issue is the vulnerability of the AI pipeline, where fraudsters can manipulate the model to produce inaccurate predictions, deny service, or cause embarrassment on social media. Detecting such attacks is challenging and often only becomes evident over time.
IT security teams can rely on established frameworks like MITRE ATLAS and the OWASP Top 10, but it’s critical to recognize that generative AI is still evolving, and security measures must keep pace with this evolution.
Another concern is intellectual property security and the “opaque box” problem of generative AI. Users cannot trace the decision pathways or the data sources used by AI models, which can lead to the exposure of IP. There’s a need to secure data access while protecting sensitive information from being misused both internally and externally.
Using retrieval-augmented generation (RAG) is a method to address some of these challenges. RAG integrates information retrieval into the text generation process, allowing for real-time context and user-specific information while keeping private data internal. It helps reduce hallucinations and provides a way to customize models without exposing sensitive information.
In planning for the future, adopting a zero-trust approach is essential. Assume that anything can go wrong at any stage of the pipeline, from data collection to deployment and access control. Documentation is vital for tracking data sources, models, and applications to help mitigate risks.
Security needs to be embedded at every layer of the system to ensure that if one layer fails, other defenses can still provide protection. Building AI models that handle security issues within other models is one proposed solution for addressing the complex security challenges posed by generative AI.
Trust plays a critical role in the success of generative AI applications. Security issues can erode user trust, which is crucial for business. An insecure AI model is essentially unusable and can undermine monetization efforts and business KPIs. On the other hand, compromised user data can lead to a permanent loss of trust, a significant risk in the AI landscape.
As generative AI and its security measures are evolving rapidly, it’s important to start with a zero-trust mindset, build defense in depth, and continuously adapt to new security challenges and AI developments.
In summary, maintaining a secure generative AI environment requires constant vigilance, innovation in security techniques, and a focus on building and preserving user trust to ensure long-term success.