Tech Talks Daily

What happens behind the scenes when you dial 911, and is the infrastructure ready for AI, satellite messaging, video, and precise location data?
In this episode of Tech Talks Daily, I'm joined by John Snapp, VP of Technology at Intrado. John has spent around 23 years working with cellular, location, and 911 technologies. He explains how a mobile emergency call is located, routed through a dedicated network, and directed to the appropriate Public Safety Answering Point.
We discuss where AI can provide practical support inside emergency communications. Translation can help telecommunicators understand callers without waiting for an interpreter. Real-time transcription can capture details and suggest established procedures. AI voice agents can also handle suitable nonemergency inquiries, giving trained staff additional time for calls where lives may be at risk.
John is clear that emotional emergency calls still demand human understanding and authority. AI can supply information, identify possible synthetic voices, and reduce administrative work, but trained telecommunicators remain responsible for interpreting the situation and directing the response.
Our conversation also examines the infrastructure beneath these capabilities. Legacy 911 networks were designed largely for voice and limited amounts of data. Next Generation 911 introduces IP connectivity capable of carrying text, images, video, and richer location information. John explains how this foundation has made satellite texting possible and why similar capabilities were far slower to introduce using older networks.
Moving to NG911 creates its own problems. Different vendors can comply with the same technical standard while implementing it differently. Calls may also need to move between modern and legacy call centers, making interoperability testing between jurisdictions a major part of deployment.
We also consider cloud resilience, local survivability, connectivity diversity, telephony denial of service attacks, AI generated swatting calls, and the danger of adopting automation before establishing governance. John recommends starting with lower-risk areas such as quality assurance and nonemergency calls, communicating openly about AI use, and expanding only after teams understand the operational impact.
As emergency communications become richer and increasingly connected, how should public safety agencies balance faster innovation with the reliability and human judgment every caller depends on? Listen to the episode and share your thoughts with me.

When organizations review their cybersecurity posture, printers are rarely the first systems that come to mind. Yet they often account for around 20% of network endpoints while receiving, storing, processing, and transmitting sensitive business information every day.
In this episode of Tech Talks Daily, I welcome back Jim LaRoe, CEO of Symphion, to discuss why printers and other connected IoT devices have become one of the most overlooked areas of enterprise cybersecurity and why AI-powered attacks are raising the stakes for organizations that continue to ignore them.
Jim explains how many businesses continue to treat printers as simple office equipment rather than Linux-based network devices with privileged access to email systems, file servers, identity services, and critical business workflows. Because responsibility for these devices often sits between procurement, managed print providers, IT operations, and security teams, they can easily fall outside normal cybersecurity processes.
We discuss how the threat landscape has changed over the past year as AI enables attackers to automate reconnaissance, credential theft, lateral movement, and ransomware deployment. Jim explains why organizations adopting Zero Trust principles also need to rethink how they secure and manage connected endpoints that have traditionally been overlooked.
The conversation also explores certificate lifecycle management, cyber hygiene, firmware management, endpoint visibility, and why unsupported devices can introduce unnecessary risk into modern enterprise environments.
For organizations managing hundreds or even thousands of printers across multiple locations, Jim explains why protecting these endpoints does not need to create additional operational burden. Instead, security should become an ongoing operational program that continuously monitors devices, detects configuration drift, applies security controls, and helps organizations maintain compliance without disrupting critical business workflows.
We also discuss the governance challenge many organizations face. Before companies can reduce risk, someone needs to own it. That means establishing accountability, assigning budget, understanding which devices exist across the business, and recognizing that printers and IoT devices deserve the same attention as servers, laptops, and other managed endpoints.
If you're responsible for cybersecurity, IT infrastructure, risk management, or digital transformation, this episode offers practical advice on protecting forgotten endpoints, strengthening Zero Trust strategies, improving endpoint visibility, and reducing the hidden risks that AI-powered attackers are increasingly looking to exploit.
Sometimes the biggest cybersecurity vulnerability isn't the system you forgot to patch. It's the one you forgot was connected in the first place.

What happens to digital asset ownership when the cryptography proving that ownership can no longer be trusted?
In this episode of Tech Talks Daily, I speak with Yoon Auh, cofounder of BOLTS Technologies, about quantum computing, blockchain security, and the need for crypto agility. Yoon brings an unusual perspective to the subject. Before moving into applied cryptography, he spent years building and operating high performance trading systems at firms including Credit Suisse, Goldman Sachs, Geode Capital, and Magnetar Capital.
Yoon explains that blockchain ownership ultimately depends on digital signatures and public keys. Most major blockchain systems use variants of elliptic curve cryptography because it has historically offered speed, compact signatures, and dependable protection. However, sufficiently powerful quantum computers could eventually challenge the mathematics supporting that protection.
The risk does not begin when such a quantum computer arrives. Yoon describes how attackers can collect encrypted traffic today, store it, and attempt to decrypt it later. This creates an immediate concern for governments, financial institutions, and businesses holding information that must remain private for many years.
We also discuss QFlex, the post quantum ready API developed by BOLTS Technologies. The company describes its approach as cryptographic logistics, allowing different cryptographic methods to be selected at the transaction level. Yoon argues that a small payment and a multimillion dollar asset transfer should not automatically receive identical protection, particularly when stronger cryptography may require additional processing, storage, and cost.
Another concern is uncertainty around the available post quantum algorithms. Yoon explains that cryptographic methods can survive years of examination before a weakness is discovered. His argument is that organizations need the ability to change algorithms quickly if one becomes vulnerable, rather than making a permanent choice and hoping it survives every new attack.
The conversation also examines digital asset sovereignty. Who decides how a transaction is protected: the platform, the protocol, or the asset holder? BOLTS Technologies believes that choice should return to the holder, while QFlex aims to provide that control without hard forks, network downtime, or protocol changes. The interview also covers the company's research background and its pilot work with the Canton Foundation.
Yoon closes with a lesson from his trading career. Backup and failover exercises often failed because they were treated as occasional events. His advice is to make exceptional processes routine, ensuring that the organization has already practiced changing systems before the moment arrives when it has no other option.
Should digital asset holders control the cryptography protecting every transaction, or should platforms continue making that decision for them? Listen to the episode and share your thoughts with me.

What prevents a successful AI experiment from becoming a dependable production system that delivers measurable business value?
In this episode of Tech Talks Daily, I speak with Ed Macosky, Chief Product and Technology Officer at Boomi, about AI pilot purgatory, integration, governance, model selection, token costs, and the technical skills businesses may need as adoption grows.
Ed leads Boomi's product and engineering teams while also using AI tools inside his own organization. That gives him a view from both sides: creating technology for enterprise customers and applying it within active product development workflows.
He believes many AI pilots begin with the wrong question. Teams become interested in the latest model or feature before defining the business problem they want to solve. The experiment may work during a demonstration, then fail when it encounters real data, access controls, security policies, and production systems.
Placing company information inside a data lake and adding a language model does not automatically create a business application. The system must access current data reliably, respect employee permissions, connect with existing applications, and operate within governance rules that security teams can approve.
Ed recommends beginning with a defined business opportunity and establishing the access required to support it. Existing APIs can already provide authentication, permissions, and governance. MCP can offer another route into enterprise systems, but those connections still require security, monitoring, and management.
Team alignment also matters. An AI center may be racing to test models while an integration center concentrates on a different set of priorities. When those groups fail to coordinate, the pilot lacks the connectivity and automation required to become part of a production workflow.
The discussion then turns toward fragmentation. Every technology wave produces new vendors, frameworks, and specialist tools. Early experimentation benefits from variety, but mature companies can eventually find themselves maintaining a complicated collection of products held together with custom code and, occasionally, the digital equivalent of duct tape.
Ed does not recommend placing every function with one provider. He does argue for enough consolidation and abstraction to prevent experimentation from creating years of technology debt. Governance and observability should also work horizontally across different environments, including platforms such as SAP, Salesforce, and several AI model providers.
That becomes increasingly important as businesses introduce autonomous agents. Leaders need to know which agents exist, what systems they can access, what actions they can take, and how each decision is recorded. AI gateways and agent control towers can provide a wider view across otherwise separate technology environments.
Ed also introduces the idea of the frontier engineer. A prompt engineer concentrates on communicating effectively with a model. A frontier engineer understands how the model works, including its logic, mathematics, algorithms, and suitability for different workloads.
He does not believe every company needs a large team of these specialists. However, he argues that enterprises need at least one person capable of assessing vendor claims and deciding whether a frontier model, specialist model, or open weight model fits a particular workload.
Cost creates another reason to examine model selection. Sending every employee request or agent task to the most capable frontier model can become expensive. Some repeatable workloads may run on open weight models inside the company's cloud or hardware environment, giving finance teams greater cost certainty.
Boomi is developing Boomi Prompt to route requests according to their complexity and requirements. A simple factual request might go directly to an API. A forecasting task may use a smaller model. A difficult analytical request could be sent to a frontier model.
Ed uses the weather as a helpful example. Retrieving next Tuesday's forecast does not require a language model when a public weather API can return the answer directly. Asking a model to perform every form of automation wastes tokens, computing power, energy, and money.
The episode closes with practical advice for CIOs. Avoid starting with a broad objective such as agentifying the entire business. Choose a department, identify a small number of tasks, define the expected return, and work backward. Once the team proves value and understands the operating requirements, it can repeat the process elsewhere.
Could intelligent routing, stronger integration, and clearer business outcomes finally move enterprise AI beyond pilot purgatory? Listen to the episode and share your thoughts with me.

What must happen before a business can trust AI agents to detect and resolve operational problems without waiting for human intervention?
In this episode of Tech Talks Daily, I speak with Josh Clay, Regional Vice President of Solution Engineering for Dynatrace in the UK, about autonomous operations, AI observability, fragmented telemetry, business outcomes, and the growing pressure to control token and data costs.
Josh has spent much of his 11 years at Dynatrace discussing the road toward autonomous operations. The earliest version involved reducing the time organizations spent inside IT war rooms. He remembers calls with 30 or 40 people attempting to establish which team was responsible for an incident. He jokingly calls this the "mean time to innocence."
Modern observability reduced many of those investigations from several hours to between 30 and 60 minutes. Agentic AI creates the possibility of going further by identifying a problem, understanding its cause, and resolving it before the customer experience is affected.
That ambition also introduces risk. Josh cites Dynatrace research showing that 52% of respondents view security, privacy, and compliance concerns as barriers to AI adoption. He believes many organizations still lack full observability across their existing technology environments, making autonomous agents harder to supervise.
Josh shares a warning from Alex Hibbert of Storia Group: AI can amplify existing technology problems. If telemetry is fragmented, data quality is poor, or teams cannot see how services depend on one another, adding autonomous agents may increase the speed and scale of the resulting failure.
Trust therefore depends on visibility. Josh describes observability as a control plane for agentic AI because it can show what an agent is doing, why it made a decision, and what happened afterward. Defined guardrails and real time information can give leaders confidence without asking them to surrender control blindly.
The adoption figures show how early this work remains. Josh says 50% of businesses have AI operating in limited production use cases, often performing one isolated task. Only 23% describe their deployments as connected across the wider organization.
We discuss how observability has progressed beyond technical monitoring. An airport can measure whether technology changes improve e-gate availability and passenger processing times. A bank can examine whether application performance affects mortgage completion rates. These connections allow leaders to measure AI through business results rather than relying entirely on response times and infrastructure metrics.
Reliable agents also need suitable data. Dynatrace says AI agents operating with deterministic data can work 12 times more accurately and three times faster while using two and a half times fewer tokens. These remain company findings, but they demonstrate why context and causality can affect cost as well as reliability.
Fragmented telemetry creates another barrier. Logs may sit in one platform, front end monitoring in another, and metrics or traces somewhere else. Attempting to reconstruct every relationship for an AI agent can become expensive and difficult.
Josh recommends bringing observability data into a connected environment where relationships between services, cloud resources, traces, metrics, and logs are already understood. He also warns against collecting information simply because it exists. Data hoarding increases ingestion costs and can introduce personal information into systems without a clear business need.
The conversation then moves toward AI FinOps. Leaders want to know what agents cost, how many tokens they consume, and whether those costs produce a measurable return. Josh describes a Dynatrace proof of concept that identified potential annual savings just below £250,000 within one small environment.
That example reinforces a recurring concern. Organizations are racing to place AI into production, then moving to the next project without reviewing whether the previous environment is appropriately sized or financially efficient.
Josh hopes companies will develop a more pragmatic view of AI as another enterprise tool. That means establishing agreed methods for deployment, monitoring, cost management, incident response, and measuring business results.
Could observability provide the confidence businesses need to move from isolated AI experiments toward autonomous operations? Listen to the episode and share your thoughts with me.