با برنامه Player FM !
Security Tools Don’t Get a Free Pass When It Comes to Human-Centered Design with Jaron Mink
Manage episode 446471150 series 2836702
In this episode, we talk about:
- Security tools don’t get a free pass when it comes to involving end users as part of the design process.
- People studying and building ML-based security tools make a lot of assumptions. Instead of wasting time on assumptions, why not learn from security practitioners directly?
- Businesses (and academia) are investing a great deal in building ML-based security tools. But are those tools actually useful? Are they introducing problems you didn’t anticipate? And even if they are useful, how do you know security practitioners will adopt them?
- Why are adversarial machine learning defenses outlined in academic research not being put into practice? Jaron outlines three places where there are significant roadblocks: First, there are barriers to developers being aware of these defenses in the first place. Second, developers need to understand how the threats impact their systems. And third, they need to know how to effectively implement the defenses (and, importantly, be incentivized to do so).
Jaron Mink is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University focused on the intersection of usable security, machine learning, and system security.
In this episode, we highlight two of Jaron’s papers:
- “Everybody’s Got ML, Tell Me What Else Do You Have”: Practitioners’ Perception of ML-Based Security Tools and Explanations.”
- “Security is not my field, I’m a stats guy”: A Qualitative Root Cause Analysis of Barriers to Adversarial Machine Learning Defenses in Industry
47 قسمت
Manage episode 446471150 series 2836702
In this episode, we talk about:
- Security tools don’t get a free pass when it comes to involving end users as part of the design process.
- People studying and building ML-based security tools make a lot of assumptions. Instead of wasting time on assumptions, why not learn from security practitioners directly?
- Businesses (and academia) are investing a great deal in building ML-based security tools. But are those tools actually useful? Are they introducing problems you didn’t anticipate? And even if they are useful, how do you know security practitioners will adopt them?
- Why are adversarial machine learning defenses outlined in academic research not being put into practice? Jaron outlines three places where there are significant roadblocks: First, there are barriers to developers being aware of these defenses in the first place. Second, developers need to understand how the threats impact their systems. And third, they need to know how to effectively implement the defenses (and, importantly, be incentivized to do so).
Jaron Mink is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University focused on the intersection of usable security, machine learning, and system security.
In this episode, we highlight two of Jaron’s papers:
- “Everybody’s Got ML, Tell Me What Else Do You Have”: Practitioners’ Perception of ML-Based Security Tools and Explanations.”
- “Security is not my field, I’m a stats guy”: A Qualitative Root Cause Analysis of Barriers to Adversarial Machine Learning Defenses in Industry
47 قسمت
همه قسمت ها
×به Player FM خوش آمدید!
Player FM در سراسر وب را برای یافتن پادکست های با کیفیت اسکن می کند تا همین الان لذت ببرید. این بهترین برنامه ی پادکست است که در اندروید، آیفون و وب کار می کند. ثبت نام کنید تا اشتراک های شما در بین دستگاه های مختلف همگام سازی شود.