Bare Metal Cyber

In this episode, we unpack how AI helpdesks and support copilots are quietly becoming a new social engineering surface. Instead of just worrying about phishing human agents, leaders now have to think about attackers shaping what the model believes, what it reveals, and which workflows it triggers. We walk through how these AI front doors are wired into ticketing, HR, and identity systems, why that turns them into privileged brokers, and where prompt injection, context hijacking, and over-helpful responses start to bend your trust boundaries. The narration is based on a Wednesday “Headline” feature from Bare Metal Cyber Magazine.
From there, we move into the leadership decisions that will determine whether your copilot stays an asset or drifts toward becoming a co-conspirator. You will hear how deterministic policies collide with probabilistic model behavior, how the helpdesk trust contract needs to be rewritten for an AI-first front line, and what it means to design secure copilots from the start. We close by looking ahead at a world where attackers bring their own AI agents to probe your helpdesk at scale, and we offer a practical lens for constraining authority, improving observability, and keeping high-risk actions anchored in strong identity and human verification.

In this episode of Bare Metal Cyber’s Tuesday “Insights” feature, we unpack how security logs, events, and alerts can move from noisy fragments to a clear, trustworthy security story. You’ll hear a plain-language walkthrough of what each layer really is, where it fits across identity, network, endpoint, and cloud, and how they work together as a narrative pipeline. We follow a small end-to-end example so you can picture how scattered records turn into a coherent incident timeline instead of a pile of disconnected clues on a dashboard.
The narration, developed by Bare Metal Cyber from our Tuesday “Insights” feature in Bare Metal Cyber Magazine, also explores everyday use cases, from quick-win coverage around logins and admin changes to deeper applications in threat hunting and post-incident reviews. You’ll hear about the real benefits of treating your data as a story, the trade-offs in storage, tuning, and skills, and the failure modes that lead to alert fatigue and missing chapters. By the end, you’ll have a practical mental model you can apply the next time you open your SIEM or XDR console and need to explain “what actually happened” with confidence.

When you see “AWS Certified Cloud Practitioner” on a resume, what does it really tell you about that person’s cloud skills? In this Monday “Certified” episode, we unpack the CCP as Amazon’s foundational cloud credential and look at what it actually proves for early-career tech and security professionals, as well as career changers trying to break into cloud. You’ll hear how CCP fits into the bigger AWS certification ladder, where its limits are, and why it has become a common gateway into cloud roles.
 
We also walk through what the exam really tests: high-level cloud concepts, core AWS services, shared responsibility for security, and how pricing and billing work in the real world. Along the way, you’ll get a practical study roadmap that fits around a full-time job, plus tips for using hands-on labs and practice questions without burning out. If cloud is on your roadmap and you want a structured, honest starting point, this conversation will help you decide whether CCP is worth your time and how to get the most value from it.

In this episode, we dig into what happens when your most important artificial intelligence (AI) capabilities come from models, copilots, and APIs you did not build yourself. Instead of debating algorithms, we follow the path leaders actually live with: opaque upstream providers, shifting model behavior, and sensitive data flowing through black boxes that now sit squarely in the middle of critical business processes. You will hear how model lineage, training data choices, and vendor change control quietly shape the risk your organization ends up owning.
We walk through the key sections of the Headline article: reframing accountability for external AI, mapping the real model supply chain behind “we just call an API,” examining concrete failure patterns, and turning vendor due diligence into questions about behavior rather than just infrastructure. From there, we explore how to wrap these external systems with your own guardrails, monitoring, and kill switches, and what a realistic operating model for AI supply chain risk looks like. This narration is based on Bare Metal Cyber Magazine’s Wednesday “Headline” feature, “Model Supply Chain Mayhem: Securing the AI You Didn’t Build Yourself.”

Security controls are often described as policies, tools, and processes, but in practice they shape how your defenses behave before, during, and after an incident. In this audio walkthrough, we break down the major types of controls in clear, practical terms: preventive controls that try to stop bad things from happening, detective controls that help you see what slipped through, corrective controls that support recovery, and supporting types like directive, deterrent, and compensating controls. You will hear how these categories span people, process, and technology, and why a balanced mix matters more than the sheer number of tools in your environment.
Across two short segments, the episode walks through what these control types are, where they fit in a typical security stack, how they work together in realistic scenarios, and what benefits and trade-offs each category brings. We also highlight common failure modes such as shallow adoption, lopsided focus on prevention, and “alert museum” monitoring, then contrast them with healthy signals like tested recovery steps and clear ownership. This narration is based on my Tuesday “Insights” feature from Bare Metal Cyber Magazine, so you get the same vendor-neutral, plain-language explanations in a format you can listen to on the move.