Forsidebilde av showet Certified: The CompTIA SecAI+ Audio Course

Certified: The CompTIA SecAI+ Audio Course

Podkast av Jason Edwards

engelsk

Teknologi og vitenskap

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Les mer Certified: The CompTIA SecAI+ Audio Course

Certified: The CompTIA SecAI Certification Audio Course is an audio-first training program built for busy IT and security professionals who want to understand how AI changes cybersecurity work—and how security changes when AI is part of the environment. It’s designed for early- to mid-career practitioners, analysts, administrators, and technically curious managers who need a practical foundation without wading through research papers or hype. If you already speak basic security—identity, logging, vulnerability management, incident response—this course helps you connect those skills to modern AI systems in a way that makes sense on the job. You can use it as preparation for a CompTIA SecAI certification path, or as a focused upskilling track if your organization is adopting AI tools and you need to stay credible in the room. Inside Certified: The CompTIA SecAI Certification Audio Course, you’ll learn how AI systems work at a level that matters for defense, governance, and risk decisions. We cover the security concerns that show up in real environments: data exposure, model misuse, prompt injection, supply-chain risk in AI components, access control for AI tools, and the operational controls that make AI safer in production. You’ll also build a working vocabulary for the space—models, training data, inference, embeddings, retrieval, and guardrails—so you can read vendor claims with a sharper eye and communicate clearly with engineers and leadership. The teaching approach is built for audio: short, focused explanations, plain-English definitions, and repeated reinforcement of the concepts you actually need to recall under pressure. What makes Certified: The CompTIA SecAI Certification Audio Course different is that it treats AI security as security—not as magic and not as fear. You’ll get clear mental models, practical decision points, and the “why this matters” context that helps you choose controls instead of collecting buzzwords. Success looks like being able to walk into an architecture review and ask the right questions, map AI risks to familiar security practices, and recognize what good governance and monitoring should look like. It also looks like confidence: you can explain the difference between a data problem and a model problem, spot common failure modes, and recommend safeguards that are proportionate to the business use case. If you finish this course and feel calmer, sharper, and harder to mislead about AI security, it did its job.

Alle episoder

91 Episoder

episode Episode 90 — Prevent Shadow AI: Sanctioned Tools, Usage Rules, and Enforcement Patterns cover

Episode 90 — Prevent Shadow AI: Sanctioned Tools, Usage Rules, and Enforcement Patterns

This episode focuses on preventing shadow AI as a governance and data protection requirement, because SecAI+ expects you to control unapproved tools that employees adopt for convenience, often without understanding how prompts, files, and proprietary data may be retained, reused, or exposed. You will learn why shadow AI emerges, including friction in approved tooling, unclear policies, and rapid feature availability, then connect that to practical risks like confidential data leaving the organization, licensing and IP exposure, inconsistent security logging, and uncontrolled model behaviors influencing decisions. We will cover prevention patterns such as providing sanctioned tools that meet real user needs, defining clear usage rules tied to data classification, implementing technical controls like access restrictions and DLP where appropriate, and creating training that explains what is allowed with concrete examples rather than vague warnings. You will also learn enforcement patterns that are realistic, including monitoring for risky data flows, investigating repeated violations, and adjusting policies and tooling to reduce incentives for workarounds, while keeping governance credible and auditable. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.

23. feb. 2026 - 10 min
episode Episode 89 — Apply Responsible AI Principles: Fairness, Transparency, and Explainability Choices cover

Episode 89 — Apply Responsible AI Principles: Fairness, Transparency, and Explainability Choices

This episode teaches responsible AI principles in an exam-ready, security-relevant way, because SecAI+ expects you to translate fairness, transparency, and explainability into practical choices that reduce harm, improve trust, and support governance rather than treating them as abstract ideals. You will learn how fairness concerns arise from biased data, uneven error rates across groups, and feedback loops that reinforce historical patterns, then connect those concerns to security outcomes like discriminatory access decisions, inconsistent fraud controls, or reputational risk after a public incident. We will cover transparency expectations such as clearly communicating system purpose, limitations, and data usage, and why transparency must be balanced against security needs so you do not reveal internal defenses or sensitive sources. You will also learn how to choose explainability methods that fit the model and the decision, including when simple interpretable models are preferable, when post-hoc explanations are acceptable with caveats, and how to validate that explanations are stable and not misleading. Troubleshooting considerations include detecting fairness regressions after retraining, documenting tradeoffs for auditors, and designing escalation rules so high-impact decisions always have human review and clear evidence trails. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.

23. feb. 2026 - 11 min
episode Episode 88 — Define AI Security Responsibilities: Owners, Approvers, Builders, and Auditors cover

Episode 88 — Define AI Security Responsibilities: Owners, Approvers, Builders, and Auditors

This episode focuses on defining responsibilities clearly, because SecAI+ scenarios often reveal failures caused by vague ownership, where everyone assumes someone else handled security review, data permissions, or monitoring, and the exam expects you to fix that with explicit accountability. You will learn how to separate responsibilities across owners who define outcomes and accept risk, approvers who validate security and compliance requirements, builders who implement controls and document evidence, and auditors who verify performance and investigate gaps independently. We will connect these roles to concrete artifacts like model cards and evaluation reports, data lineage documentation, access control decisions for retrieval and tools, change logs for prompts and model versions, and incident response playbooks for abuse, leakage, or drift. You will also learn how to avoid common pitfalls such as letting builders approve their own changes, leaving service accounts unmanaged, or assuming vendor attestations replace internal validation. Troubleshooting considerations include handling shared services across multiple business units, aligning responsibilities with existing security and compliance structures, and ensuring responsibilities remain valid as systems evolve from pilots to production services with real business impact. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.

23. feb. 2026 - 11 min
episode Episode 87 — Build AI Governance Structures: Policies, Roles, and a Working Operating Model cover

Episode 87 — Build AI Governance Structures: Policies, Roles, and a Working Operating Model

This episode explains AI governance as an operating model that makes security and compliance achievable at scale, because SecAI+ expects you to choose governance structures that produce consistent decisions instead of one-off exceptions and informal approvals. You will learn what governance must cover, including approved use cases, data classification and access rules, model and vendor evaluation requirements, monitoring and incident response expectations, and change management for prompts, tools, and model versions. We will connect policies to roles and decision forums, showing why ownership must be explicit for model deployments, retrieval sources, tool permissions, and risk acceptance, and how a governance cadence prevents drift into unmanaged “pilot forever” systems. You will also learn how to make governance workable by defining lightweight intake processes, risk-tiering so low-risk use cases move quickly, and evidence requirements that scale, such as standard evaluation sets, documentation templates, and audit-ready logs. Troubleshooting considerations include avoiding governance that is so heavy it drives shadow AI, reconciling conflicting stakeholder priorities, and building escalation paths that resolve disputes while keeping risk decisions transparent and accountable. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.

23. feb. 2026 - 10 min
episode Episode 86 — Manage CI/CD With AI Assistants: Secure Pipelines, Tests, and Change Control cover

Episode 86 — Manage CI/CD With AI Assistants: Secure Pipelines, Tests, and Change Control

This episode teaches how AI assistants fit into CI/CD without weakening security, because SecAI+ scenarios often involve AI-generated code, AI-suggested pipeline changes, or automated remediation that must still obey testing discipline and change control. You will learn where AI can help, such as drafting build steps, proposing tests, summarizing failures, and generating documentation, while emphasizing that pipeline integrity depends on controlled permissions, trusted runners, and tamper-resistant artifacts. We will connect secure pipelines to practical controls like signed commits and artifacts, protected branches, mandatory reviews for pipeline changes, secret scanning, and separation between build and deploy permissions so a compromised assistant or token cannot push directly to production. You will also cover how to treat AI-generated changes as untrusted until validated, including running unit, integration, and security tests, using SAST and dependency scans, and requiring evidence-based approvals for changes that affect authentication, data handling, or access control. Troubleshooting considerations include preventing an assistant from “fixing” failures by disabling checks, managing noisy test results without relaxing standards, and ensuring pipeline logs and outputs do not leak secrets through verbose debugging or AI summaries. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.

23. feb. 2026 - 11 min
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