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AI in telecom is finally graduating from slide decks to real operational impact, but the jump from pilot to production is where most teams get stuck. I sit down with Guy Lupo from the TM Forum, who leads the trustworthy AI and data mission, to talk about what it actually takes to become an AI native telco and why the industry’s next gains depend less on flashy demos and more on operational proof.

We break down where operators are seeing traction right now, like network fault management, faster mean time to resolve, fewer tickets, and churn reduction, and why those wins correlate directly with clean, structured signals. Then we dig into the uncomfortable middle ground: AI that augments people feels manageable, but AI embedded into tools and workflows raises hard questions about governance, monitoring, and accountability. Guy’s point lands hard: trust cannot be claimed, it must be demonstrated continuously, especially as autonomy increases.

From there, we connect the dots to risk-based regulation and sovereignty. Frameworks like the EU AI Act signal a shift away from checklist compliance toward auditable evidence over time, with telecom increasingly treated as high risk critical infrastructure. We also explore emerging concepts like agent passports, plus why the industry is asking for a shared “agent factory” reference architecture and practical, no regret patterns such as Model as a Service for consistent, governable model access. We close by looking ahead to physical AI and robotics and the surprising telecom advantage: the operational workforce that can install, maintain, and safely support devices at scale.

If you care about AI governance, autonomous networks, agentic AI, and the real-world path to production in telecom, subscribe, share this with a colleague, and leave a review with the one trust gap you want the industry to solve first.
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
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The easiest system to hack is the one that’s always there to be found. We sit down with Steve Visconti, CEO and co-founder of XIID, to talk about a different cybersecurity mindset: architectural invisibility, where the goal isn’t to build a bigger wall, it’s to make the target unreachable in the first place.

We dig into what “no inbound communication” really means, including removing public IP dependence, reducing DNS exposure, and enforcing process-to-process connectivity so only the exact executable you approve can talk to the exact service it needs. Steve explains how outbound-only tunnels can be established on both sides, and why strong encryption and post-quantum secure tunneling matter when you’re protecting high-value systems in an increasingly autonomous, machine-to-machine world.

We also get practical about where this fits in today’s security stack. Because it operates at the application layer, it can complement existing tools without a rip-and-replace overhaul, and it can roll out one app at a time while still scaling through orchestration. Along the way, we connect the dots to real risks in modern software delivery, like AI-generated code and CI/CD pipelines that accidentally leave behind discoverable test endpoints.

Finally, we zoom out to critical infrastructure, including EV charging networks and the growing connection between vehicles, cloud billing systems, and the electrical grid. If you care about reducing attack surface, protecting OT environments, and building zero trust security that survives automation at scale, this is for you. Subscribe, share this with a security-minded friend, and leave a review with your biggest question about making systems “unreachable by design.”
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A 2005 malware sample sounds like ancient history, until it looks like cyber sabotage that may predate Stuxnet. We sit down with Jags from SentinelOne’s Sentinel Labs to unpack Fast 16, a rare framework that doesn’t just break computers, it quietly corrupts high precision calculations. If you’ve ever treated simulation results, engineering models, or AI outputs as “the answer,” this conversation will make you pause.

We walk through the unexpected discovery path: a curious reference tied to the Shadow Brokers leak, years of researchers staring at a strange sample that “felt important” but refused to give up its secrets, and the moment an internal project using AI for reverse engineering helped unlock what Fast 16 was built to do. Along the way, we connect the dots to the Stuxnet era, cyber threat intelligence “paleontology,” and why truly high end nation state toolkits look like platforms, not one off scripts.

Then we get uncomfortably current. Sabotaging calculations is an integrity attack, and integrity is the foundation of modern scientific computing, cloud workloads, and frontier AI model training. We talk about how subtle degradation can waste millions, derail decision making, and even turn teams against their own experts. We close with practical lessons for CISOs and enterprise leaders: invest in visibility, telemetry, and log retention before the crisis, and start treating output verification as a core security problem.

Subscribe for more deep dives on cyber sabotage, APT tradecraft, and AI security, and if this made you rethink what “trust” means in computing, share it and leave a review. What system in your world would be hardest to verify?
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
Listen on: Apple Podcasts Spotify
Support the show
More at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected]
Your phone rings, you hesitate, and you let it go to voicemail because it might be a scam. Meanwhile your team is juggling Microsoft Teams, Webex, mobile calling, messaging apps and a growing stack of AI tools that promise better customer experience but often add complexity. We sit down with William Rubio to unpack what’s actually changing in cloud communications and what a managed service provider needs to deliver in 2026: not just licenses, but outcomes across UCaaS, CCaaS and AI.

We talk through Call Tower’s evolution and the recent strategic investment from Court Square Capital Partners, including how growth, global expansion and M&A fit into a fast-moving market. Then we get practical about mobile identity and eSIM for Teams and Webex: why “clicking the app” is friction, how caller ID consistency affects trust, and why compliance, recording and analytics become more important when work follows you from car to laptop to office to home.

On the CX side, we zoom in on conversational AI and agentic AI in the contact center: what major platforms are shipping, where specialized AI vendors can add real value, and why industry-specific AI for healthcare, finance and manufacturing is likely to define the next wave. We also cover WhatsApp integration with Microsoft Teams and what it signals about enterprise communications finally adopting consumer-like channels without giving up security.

If you care about cloud calling, AI contact centers, mobile-first collaboration and stopping spam calls from poisoning business communications, hit play. Subscribe, share this with a teammate, and leave a review with the one communications headache you most want fixed next.
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Personalized phishing is no longer “spray and pray.” It’s targeted, multi-channel, and increasingly powered by exposed employee data that’s sitting in plain sight. We sit down with Paul Mander, Chief Commercial Officer at Optery for Business, to unpack what’s driving the next wave of AI-driven social engineering and why so many security teams are rethinking where the real attack surface begins.

Paul walks us through eye-opening survey findings from more than 400 cybersecurity leaders: social engineering attempts are rising sharply, most attacks are moderately or highly personalized, and a large share of those successful attempts lead to credential compromise. We also dig into why there’s no single channel to defend anymore. Email still matters, but attackers are mixing phone calls, SMS, social media, and impersonation to make their stories feel “verified” from multiple angles.

The biggest shift is where attackers get their homework done. Data brokers and people search sites compile dossiers that include phone numbers, home addresses, relatives, employment history, and even org chart details that help threat actors pick high-leverage targets. We talk about why IT, HR, and finance often take more heat than executives, and what practical teams can do today: strengthen MFA and training, then get proactive by finding and removing exposed PII through opt-out and deletion workflows at scale.

If you’re a CISO, IT leader, or security practitioner trying to reduce phishing risk, social engineering risk, and account takeover risk, this is the playbook for treating privacy exposure as a core cybersecurity control. Subscribe, share this with your team, and leave a review with the one data source you think attackers rely on most.
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