In today's enterprise technology landscape, Generative AI (GenAI) stands out for its potential and its challenges, particularly in managing non-deterministic outcomes (results that are unpredictable and inconsistent). These elements can significantly impact the effectiveness and reliability of GenAI applications. This blog delves into how enterprise buyers, evaluators, and users of GenAI can benefit from the well-established field of cybersecurity. By drawing parallels, we aim to demonstrate how cybersecurity wisdom can be effectively applied to manage and optimize non-deterministic outcomes in GenAI.
1. Understanding Different Personas
Each persona involved in the acquisition and implementation of technology solutions has unique concerns and focuses:
- Buyers prioritize strategic alignment, assessing ROI, and managing risks. For instance, cybersecurity buyers consider the impact of false positives on operational efficiency crucial. Similarly, GenAI buyers need to assess how hallucinations could affect critical decision-making processes.
- Evaluators need a deep technical understanding and hands-on validation. Cybersecurity evaluators typically conduct penetration tests to evaluate false negatives. Similarly, GenAI evaluators need to use specific datasets to test the frequency and impact of bias and hallucinations.
- Users are on the frontline, interacting daily with the technology. Security teams typically adjust filters to optimize the rate of false positives. Similarly, internal users of GenAI tools need to continuously note and report any irrelevant or erroneous outputs.
2. Identifying Suitable Use Cases
Selecting the right applications for non-deterministic solutions is crucial. Cybersecurity teams use AI in anomaly detection, where traditional methods may fail to recognize new or evolving threats. Similarly, GenAI excels in customer support environments, handling a wide variety of queries with evolving natural language processing models that learn from each interaction.
3. Evaluating Non-Deterministic Solutions
A thorough Proof of Concept (POC) is essential. This includes setting clear, achievable goals that take into account the inherent unpredictability of the technology. For example, a cybersecurity POC will focus on the rate of false positives and false negatives, while a GenAI project could measure the accuracy and relevance of generated content against human-produced standards.
4. Phased Rollout
A phased rollout is essential to monitor how well the non-deterministic solution adapts to real-world conditions. For cybersecurity, this will mean incrementally implementing the solution across different network segments. For GenAI in customer support, it could start with handling low-priority queries and gradually taking on more complex customer interactions as the system learns and improves.
5. User Training
User Training should specifically address the challenges presented by non-deterministic outcomes. Cybersecurity training typically covers the interpretation and handling of ambiguous threat data, whereas GenAI training could focus on recognizing biases and learning how to refine inputs to achieve better outputs.
6. Implementing Feedback Mechanisms
Feedback mechanisms are vital for refining the deployment of non-deterministic technologies. In cybersecurity for example, user feedback helps in tuning the systems to reduce false positives and negatives. In the customer support scenario, feedback from support staff and customers helps improve the AI's responses and handling of queries, ensuring the system evolves to meet user needs effectively.
7. Scaling for Broader Rollouts
After initial successes and necessary adjustments, broader rollout plans can be implemented. This stage should still be approached with caution, expanding the user base gradually while continuously monitoring for new challenges that may arise.
Conclusion
The unpredictability of non-deterministic outcomes requires careful consideration, but the lessons learned from cybersecurity can provide valuable guidance for enterprises looking to leverage GenAI. At Radware, we have garnered extensive insights from our experience in the cybersecurity sector, understanding the intricacies of managing non-deterministic technologies effectively. We are sharing these learnings aiming to assist other enterprises on their journey with GenAI.
For customers embarking on GenAI projects, a valuable resource lies within your organization as well - the security team. With extensive experience in managing non-deterministic outcomes in cybersecurity systems and projects, these professionals can provide crucial insights into navigating the complexities of GenAI. Bringing in collaboration between your GenAI and security team, can accelerate your GenAI projects by leveraging previous learnings from Cybersecurity.
By addressing the unique challenges posed by non-determinism in GenAI, organizations can improve the implementation and acceptance of GenAI technologies. This approach ensures a smoother transition into this evolving field. Collectively, these advancements not only enhance individual enterprise outcomes but also influence the broader landscape of technological innovation.