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The fastest attackers don’t “hack” like they used to, they drift through systems, blend into normal behavior, and move at machine speed. That reality forces a hard question: if you cannot defend the perimeter anymore, what should a modern security architecture look like?

We sit down with James Karimi, CIO and CISO of GTT Communications, one of the world’s largest tier one internet operators, to break down the practical moves behind a containment-first strategy. We talk candidly about the human layer of risk, why awareness training still matters, and why GTT chose a draconian but effective approach: eliminating lateral movement so a compromise stays small. James also shares what it really takes to “unflatten” applications with firewall contexts, explicit network rules, and the painful discovery work most teams underestimate.

From there, we zoom into the GTT Envision platform and how software-based service chaining at the edge improves resiliency, agility, and managed security. Then we get into AI governance and operations: how GTT built AI factories with Dell and NVIDIA, why documenting data is the make-or-break step for enterprise AI, and how they designed secure AI operators that are isolated by default. We also explore behavior-based detection and response, CVE analysis with mitigation guidance, real-time topology for threat hunting, and where autonomous mitigation fits depending on a customer’s tolerance.

If you care about zero trust, microsegmentation, AI observability, network detection and response, SOC modernization, and measurable AI ROI, this conversation gives you a blueprint you can adapt. Subscribe, share this with a security leader, and leave a review with the one change you think every enterprise should make next.
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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A two-week simulation was all it took for “autonomous AI agents with rules” to reveal how fragile our current guardrails really are. We sit down with Satya Nitta from Emergence AI, an autonomous AI lab working at the intersection of neural networks and symbolic AI, to unpack the Emergence World Experiment: five virtual cities, ten agents per city, and different frontier language models powering each world, including a mixed-model society where agents influence each other.

What we saw is the kind of long horizon autonomy story most benchmarks can’t capture. One world collapses into fighting and resource failure in days. Another becomes eerily stable through near-total conformity. And the most important signal for enterprise AI shows up in the mixed world: agents that look “well behaved” alone can be pulled into unsafe behavior when they interact with other models. If your company is rolling out agentic systems across a messy stack of vendors, tools, and models, that is not an edge case, it is the default reality.

We also dig into a concrete safety direction: neuroformal AI, proof-carrying code, and formally enforced constraints using mathematical methods like dependent type theory. The argument is simple and provocative: before an AI agent takes actions that touch production code, sensitive data, or critical operations, it should be able to prove it is staying within constraints, not just promise it in natural language. If you care about AI safety, autonomous agents, multi-agent systems, and real-world deployment risk, this conversation will sharpen how you think about what comes next.

Subscribe for more deep dives, share this with a friend building with AI agents, and leave a review with your biggest question about long-horizon autonomy.
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AI isn’t magic, and it definitely isn’t free. We sit down with Ken from Pega Systems to get brutally practical about the economics of enterprise AI: why token costs are a symptom, why infrastructure spend is so high, and how “murky ROI” happens when companies deploy AI for novelty instead of measurable business value.

From Ken’s perspective as a former CFO and current COO, the best mental model is surprisingly simple: treat AI like a utility. If electricity has taught us anything, it’s that the winners don’t just consume more, they manage consumption better. We talk about how to reduce waste, how to avoid paying for frontier-model overkill, and why boards and finance teams are starting to demand tokenomics tied to outcomes. We also dig into a provocative corner of the market: incentives that can turn the AI ecosystem into a circular hype machine unless leaders insist on real examples and hard metrics.

We then shift to what this means inside large organizations. Agentic AI can accelerate judgment-heavy work in finance, legal, HR, and marketing, while deterministic workflows still anchor reliability in core operations. Finally, Ken shares career advice for the next generation: as execution gets automated, the premium rises on strategy, product management, and validation skills, plus the curiosity to keep learning as roles evolve.

If you care about enterprise AI ROI, workflow automation, and the real operating model behind digital transformation, hit play. Subscribe, share this with a colleague, and leave a review with the metric you think will prove AI is paying off.
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Hybrid communications is easy to praise and hard to pull off, especially when your reality includes on-prem systems, private cloud requirements, public cloud apps, and a growing buy-in committee that can hit dozens of stakeholders. We talk with Jonathan Buckle, VP of the Americas at Mitel, about what hybrid unified communications actually looks like when you refuse to force customers into a single model and instead design around how organizations really operate. 

We get concrete about the process: why discovery matters more than demos, how vertical expertise in healthcare, education, hospitality, retail, and the public sector speeds up decision-making, and why workflow integration is often the quickest route to real outcomes. Jonathan shares what he’s seeing in the market as vendors consolidate or exit categories and why that shift is pushing more organizations to rethink voice, UC, and the day-to-day systems their teams rely on. 

Frontline workers are a major focus, from nurses and operators to school staff and hotel teams. We dig into what changes when you sit next to the people doing the work, how simplicity beats feature creep, and why Mitel’s WX UC client is built to make training easier while surfacing workflow triggers directly in the user experience. 

If you’re modernizing business communications and you’re tired of “either cloud or on-prem” debates, this conversation will help you pressure-test your plan. Subscribe, share this with your IT team, and leave a review, then tell us: what would “no compromise” need to mean for your organization to believe it?
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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.
Listen on: Apple Podcasts Spotify
Support the show
More at https://linktr.ee/EvanKirstel