Next in Tech

Mysterious lights are seen in the sky above New Jersey, throwing many into a panic. Is this the current concern about drones or the War of the Worlds radio broadcast 86 years ago? There are interesting similarities and analyst Johan Vermij returns to the podcast to kick off the New Year discussing aliens, drones and the more recent furor in the Garden State. Various forms of drones, in the air, in the seas and on land are becoming a ubiquitous resource. Costs have come down and they’ve become much more capable. But they’re still not well understood by society at large. The New Jersey sightings are still being investigated, but many seem attributable to more conventional aviation activities. That’s not to say that these sightings can be disregarded. There are many documented cases of geopolitically driven military surveillance, with arrests of drone pilots with apparently nefarious intent around the globe. This is one more factor in play in the very complex world of drones today. It’s all too easy to deflect the conversation around drones from the many practical uses in which they’re put to work today. Drones are being used to aid first responders, with drone-in-a-box (DiaB) technologies available to be first on the scene to enhance situational awareness in critical situations. They’re surveying crops to increase yields while reducing pesticide use, finding methane leaks to reduce gas emissions and improving maintenance of cell towers and power transmission lines. And they’ve revolutionized the battlefield, creating an asymmetric advantage and driving a rapid evolution of technologies in a conflict-driven proving ground. We won’t see the replacement of jets and howitzers, but drone’s impacts have already been far reaching. More S&P Global Content: A global IT outage highlights the paradox of success in IT, OT and cybersecurity Tenn. man charged with attempted drone attack on Nashville power station Nokia aims to boost full-stack B2B offerings, with Drone-in-a-Box service as a proof point 2025 Trends in IoT, Edge & Digital Industries How the Russia-Ukraine war is raising the bar for the drone industry Credits: Host/Author: Eric Hanselman Guest: Johan Vermij Producer/Editor: Kyle Cangialosi and Odesha Chan Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith  

In the rush to deliver data to AI projects, it’s all to easy for teams to pull data that’s most easily accessible, without given consideration to its nature and scope. Emily Jasper and Abby Simmons return to discuss ethical concerns about the data that feeds AI projects with host Eric Hanselman. AI implementations place a much greater burden on data quality than traditional IT projects. When data becomes the product, development practices, such as minimum viable product (MVP) releases, require that data be held to a much higher quality standard to address ethical concerns about its suitability. If a dataset contains bias or lacks representation for the community it serves, it will not only fall short in function, but can reinforce the bias and errors in the data. In effect, it becomes its own data poisoning attack, one of the key security concerns in AI applications. Ethical approaches to AI applications have to focus on ensuring that outputs reflect the diverse nature of society and move beyond a narrow, middle of the road, average. They have to integrate perspectives and feedback from the full spectrum of the society they claim to represent. It involves additional work to achieve this and it can pay off in the expanded market it gives access to. At the same time, organizations need to put their capabilities to work to serve those parts of their community that don’t have access to AI’s benefits. This can help to keep marginalized segments of society from being left behind, in what is becoming the next chasm in the digital divide. More S&P Global Content: Next in Tech | Episode 119: Defeating Digital Deficiencies 2025 Trends in Data, AI & Analytics Take 5: Data quality and AI — a bidirectional relationship Compliance automation, Part 1: Governance, risk and compliance, or something new? Credits: Host/Author: Eric Hanselman Guests: Emily Jasper, Abby Simmons Producer/Editor: Donovan Menard and Odesha Chan Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith Other Resources: Trans Tech Tent Kevin Guyan - Queer Data

Enterprises are wrestling with delivering data to fuel their AI efforts, hitting roadblocks around data security and privacy concerns and sifting through use cases and models to put it to work. Too many are making high-stake gambles feeding vast quantities of data into massive models. Jesse Robbins, one of the founders of Chef, a progenitor of the DevOps movement, a builder of the early Internet infrastructure and now partner at Heavybit, joins host Eric Hanselman to look at alternatives to the path that many are taking in pursuit of successful AI projects. In much the same way that DevOps patterns look to shift application development to more smaller, incremental changes with a pipeline that drives continuous improvement, AI projects can work with smaller models and localized datasets to manage risk and iterate faster. It’s a pattern that avoids concerns of pushing sensitive data to cloud-based offerings by working locally. Using smaller models reduces infrastructure costs and the need for vast quantities of GPU’s. Larger models sizes and data sets create two problems – more computational power and supporting infrastructure is required and more data complicates data provenance, security and ownership issues. Starting smaller and expecting to iterate on the results locally can have multiple benefits. If the data being used never leaves the local confines, security concerns are constrained to local environments. Tools like the open source project Ollama can deliver a choice of models to fit a variety of use cases and infrastructure capacities. Just like DevOps patterns, starting small and iterating quickly can get further faster and with lower risk. More S&P Global Content: Big Picture GenAI report Kubernetes community capitalizes on open source and AI synergies Credits: Host/Author: Eric Hanselman Guests: Jesse Robbins Producer/Editor: Donovan Menard and Odesha Chan Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith Other Resources: The Data Pipeline is the New Secret Sauce Ollama Continue

Much of business success depends on successfully managing risk, but that includes not only those aspects under direct control, but also the risks in relationships with third parties. It’s a task that can be daunting for any business and Luke Nordlie joins host Eric Hanselman to discuss the challenges and technology-based approaches to risk assessment, monitoring and compliance. With expanding supplier ecosystems, businesses are faced with issues of scaling their third party risk management efforts to match that growth. At the same time, there is growing regulatory pressure and associated compliance mandates. For many businesses, this comes at a time when they’re working with fewer staff, as they try to manage costs. To become more efficient and effective, they need to shift their tactics from point-in-time evaluations to a continuous compliance process, leveraging data to build better understanding of their risk posture. It’s an approach that has seen use in cybersecurity and insurance.  With increasing desire to outsource non-core business functions comes a growing supplier inventory. Better controls are needed for onboarding new suppliers to help understand inherent risk. But as industries rely on common providers for specific capabilities, concentration risk increases across them. Regulators are focusing on resilience, with regulations like the UK’s DORA explicitly requiring assessments of third party risk. There is additional pressure placed in areas like FinTech, where changes in the funding landscape are translating into needs for greater resilience. Performing due diligence in these new conditions is putting focus on technologies like questionnaire-less onboarding and monitoring using different data sources. Efforts at regulatory harmonization show promise in aiding third party risk management and helping it become a continuous process of evaluation and understanding of risk posture. More S&P Global Content: Getting Smarter with TPRM: Reimaginin Due Diligence Webinar Survey Data Hub - Voice of the Enterprise: Information Security, Technology Roadmap 2024 Credits: Host/Author: Eric Hanselman Guests: Luke Nordlie Producer/Editor: Donovan Menard, Odesha Chan Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith