Part 3: What Responsible AI Should Look Like This brings us to our final question in our 5-part series about AI’s impact on networks: what should responsible AI look like? Achieving a balance between automation and AI with human expertise necessitates a human-in-the-loop approach. In this model, automation handles high-volume, repetitive tasks, while human judgment […]
Part 3: What Responsible AI Should Look Like The eleventh question in our 5-part series on AI’s impact on networks is a thought-provoking one. Network automation vendors are increasingly expected to clearly explain how their AI generates insights. It’s crucial that these AI actions are safe, responsible, and fully aligned with your organization’s goals for
Part 3: What Responsible AI Should Look Like If you are following along, this is our second question in our 5-part series about responsible AI, where we address common questions regarding AI’s impact on networks. Here, I discuss the importance of embracing responsible AI in network operations. Building trust is essential, emphasizing safety, reliability, human augmentation, and transparency. To learn more about
Part 3: What Responsible AI Should Look Like Artificial Intelligence (AI) poses both challenges and benefits for businesses. While hackers exploit vulnerabilities quickly, AI enhances network resilience by improving response times, optimizing resource use, and enabling targeted remediation efforts. The rapid rise of AI has raised concerns about privacy, transparency, and ethics. In our 5-part
Part 2: The Promise vs the Risk In our final question of Part 2 of our 5-part series on the risk of AI in networks, BackBox CEO Rekha Shenoy dives deeper into the topic of AI data sources and ways to mitigate risk. Surprisingly, there are highly valuable AI use cases for network automation that
Part 2: The Promise vs the Risk Welcome back to Part 2 of our 5-part series on the risk of AI in networks. This time, BackBox CEO Rekha Shenoy focuses on transparency, an extremely important topic when evaluating the promise versus the risk of AI in networks. Transparency is top of mind for enterprise buyers
Part 2: The Promise vs the Risk This is the second installment in Part 2 of our 5-part series, where BackBox CEO Rekha Shenoy discusses key questions about the promise versus the risk of AI in networks. Here, we explore why AI hallucinations, bias, and data quality issues are especially serious in network automation. In
Part 2: The Promise vs the Risk In Part 1 of this 5-part series on AI’s impact on networks, BackBox experts addressed common questions to help set the stage for AI adoption. In Part 2, we examine the promise versus the risk of AI in networks. To kick off Part 2, BackBox CEO Rekha Shenoy
Part 1: Setting the Stage Finally, our fourth question helps set the stage in our 5-part series about AI’s impact on networks. This time, BackBox Field CTO Irfahn Khimji discusses where AI is currently helping defenders. AI has emerged as an essential asset in strengthening network cyber resilience. It enables faster response times, optimizes resource
Part 1: Setting the Stage The third question in our 5-part series about AI’s impact on networks is a good one! Artificial Intelligence (AI) has become both a headwind and a tailwind for enterprises, as hackers exploit vulnerabilities in days that could have taken months or years to identify in the past. At the same