Part 5: Looking Ahead Thank you for joining us on our journey as we explore AI’s impact on networks, where BackBox experts answer common AI adoption questions for network infrastructure. In Part 5, we look ahead to what we believe is coming for AI. To kick off Part 5, BackBox Field CTO Irfahn Khimji discusses […]
Part 4: AI in Action at BackBox In our fifth and final installment in Part 4 of our 5-part series on AI in networks, BackBox VP Infrastructure and Technology Richard Phillips explains how CVE Workaround Insights balances speed and safety. Vulnerabilities don’t always pose a security risk. There’s a spectrum — from high-severity vulnerabilities in
Part 4: AI in Action at BackBox This is the fourth installment in Part 4 of our 5-part series on AI in networks, where BackBox VP of Infrastructure and Technology Richard Phillips discusses the issue that CVE Workaround Insights addresses. CVE data is notoriously inconsistent and incomplete. If a patch or upgrade isn’t available or
Part 4: AI in Action at BackBox In our third installment in Part 4 of our 5-part series on AI in networks, BackBox VP of Infrastructure and Technology Richard Phillips emphasizes the importance of normalization and prioritization. Data from various trusted sources comes in different formats and is often inconsistent. To make aggregated data actionable,
Part 4: AI in Action at BackBox This is the second installment in Part 4 in our 5-part series on AI in networks. Here, BackBox VP of Infrastructure and Technology Richard Phillips explores the challenge of unifying vulnerability data and how AI assists. Even the most well-resourced NetOps teams face difficulties gathering vulnerability data from
Part 4: AI in Action at BackBox Thank you for joining us on our journey as BackBox experts address common questions about AI adoption for network infrastructure. In Part 4 of our 5-part series, BackBox, VP of Infrastructure and Technology Richard Phillips takes a closer look at AI in action. Here, he discusses three main
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