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PODCAST

Certified - AI Security Audio Course

The AI Security & Threats Audio Course is a comprehensive, audio-first learning series focused on the risks, defenses, and governance models that define secure artificial intelligence operations today. Designed for cybersecurity professionals, AI practitioners, and certification candidates, this course translates complex technical and policy concepts into clear, practical lessons. Each episode explores a critical aspect of AI security—from prompt injection and model theft to data poisoning,

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All Episodes

24:24

Episode 31 — Cloud & Infra for AI

Certified - AI Security Audio Course ·

2025/09/15

This episode explores cloud and infrastructure security in the context of AI, focusing on GPU clusters, multitenancy, storage, and network isolation. For certification readiness, learners must understand that AI workloads often demand specialized compute and storage, which in turn require hardened configurations and rigorous access controls. Misconfigurations in cloud services remain one of the most common causes of breaches, and in AI environments, such errors can expose sensitive datasets

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30:38

Episode 30 — Connector/Plugin Security

Certified - AI Security Audio Course ·

2025/09/15

This episode addresses connector and plugin security, focusing on how third-party integrations expand the attack surface of AI applications. Connectors link systems to external data or services, while plugins extend model functionality by calling APIs or executing tasks. For certification, learners must be able to define these integration types and explain why they pose risks of privilege escalation, data leakage, or supply chain compromise. The exam relevance lies in understanding how

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25:11

Episode 29 — Code Execution & Sandboxing

Certified - AI Security Audio Course ·

2025/09/15

This episode examines the risks of code execution in AI systems and the security benefits of sandboxing. Many AI applications incorporate features allowing generated or user-provided code to run, enabling advanced analysis, automation, or integration with development environments. For exam readiness, learners must define sandboxing as the isolation of execution environments to minimize risk and understand how failure to sandbox exposes host systems to compromise. The exam relevance lies in

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23:44

Episode 28 — API Gateways & Proxies for AI

Certified - AI Security Audio Course ·

2025/09/15

This episode focuses on API gateways and proxies, emphasizing their role as critical control points for AI applications. An API gateway manages traffic to model endpoints, providing authentication, authorization, rate limiting, and policy enforcement. Proxies filter and shape requests or responses, enabling organizations to apply additional layers of validation. For certification purposes, learners must define these components and explain how they mitigate risks such as abuse, data leakage,

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17:14

Episode 27 — Secure Fine-Tuning & Adaptation

Certified - AI Security Audio Course ·

2025/09/15

This episode introduces secure fine-tuning and adaptation, explaining how customization of pre-trained models introduces both benefits and new risks. Fine-tuning adjusts model weights on domain-specific data, while adaptation methods such as LoRA, adapters, or reinforcement learning from human feedback (RLHF) provide lightweight ways to specialize behavior. For certification exams, learners must define these approaches and explain how insecure adaptation can create vulnerabilities, such as

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21:52

Episode 26 — Supply Chain & Artifacts

Certified - AI Security Audio Course ·

2025/09/15

This episode examines supply chain and artifact security, focusing on how external dependencies and stored components create systemic risks in AI systems. Artifacts include datasets, model weights, configuration files, and container images, each of which must be treated as high-value assets. For certification purposes, learners must be able to define supply chain risk in the AI context and explain how compromised datasets or libraries propagate vulnerabilities downstream. Exam questions

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28:03

Episode 25 — MLOps & Serving Security

Certified - AI Security Audio Course ·

2025/09/15

This episode introduces MLOps and serving security, focusing on practices that protect the deployment, operation, and continuous delivery of AI models. MLOps extends DevOps principles to AI, requiring controls for model registries, CI/CD pipelines, and serving infrastructure. For certification purposes, learners must know definitions such as model registry, rollback, and shadow deployment, and understand how these components can be secured against tampering or misuse. The exam relevance

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30:52

Episode 24 — Cost & Resource Abuse

Certified - AI Security Audio Course ·

2025/09/15

This episode examines cost and resource abuse, where adversaries or careless users exploit AI systems to drive up compute expenses or deny service to legitimate customers. For exams, learners must understand the concept of denial-of-wallet, token-based denial-of-service, and quota storms that can overwhelm infrastructure. These risks are unique to AI because of resource-intensive inference workloads, making cost management a security concern as much as an operational one. The exam relevance

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25:32

Episode 23 — Abuse & Fraud Detection

Certified - AI Security Audio Course ·

2025/09/15

This episode addresses abuse and fraud detection in AI applications, focusing on how adversaries exploit systems for spam, phishing, or marketplace manipulation. For certification purposes, learners must understand definitions of abuse, such as misuse of generative models for disallowed tasks, and fraud, defined as deceptive actions for financial or reputational gain. The exam relevance lies in recognizing common abuse patterns, their detection methods, and organizational responses to

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29:04

Episode 22 — Telemetry & Observability

Certified - AI Security Audio Course ·

2025/09/15

This episode explores telemetry and observability, emphasizing their importance in detecting anomalies, intrusions, and misuse in AI systems. Telemetry refers to the data collected from inputs, outputs, and system components, while observability describes the ability to interpret that data to understand system health and behavior. For exams, learners must know what to log, how to ensure logs are privacy-safe, and why observability is more than just storing records—it is about actionable

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27:37

Episode 21 — Evals & Test Pipelines

Certified - AI Security Audio Course ·

2025/09/15

This episode examines evaluations and test pipelines as essential processes for maintaining AI system security and reliability. Evaluations, or “evals,” are structured tests that measure a model’s behavior against known benchmarks or adversarial scenarios, while pipelines provide the automated flow of regression testing, scorecards, and service-level objectives. For certification purposes, learners must be able to define these concepts, explain how they ensure system reliability, and

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29:48

Episode 20 — Red Teaming Strategy for GenAI

Certified - AI Security Audio Course ·

2025/09/15

This episode introduces red teaming as a structured method for probing generative AI systems for vulnerabilities, emphasizing its importance for both exam preparation and real-world resilience. Red teaming involves adopting an adversarial mindset to simulate attacks such as prompt injection, data leakage, or abuse of system integrations. For learners, understanding red team goals, rules of engagement, and reporting requirements is essential to certification-level mastery. The relevance lies

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30:12

Episode 19 — Output Validation & Policy Enforcement

Certified - AI Security Audio Course ·

2025/09/15

This episode examines output validation and policy enforcement as mechanisms for controlling what AI systems produce before results are delivered to users or downstream processes. Output validation ensures that responses conform to expected formats or structures, such as JSON schemas, while policy enforcement applies organizational rules that block disallowed or unsafe outputs. For exam purposes, learners must understand how these layers complement input validation, creating a

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25:53

Episode 18 — AuthN/Z for LLM Apps

Certified - AI Security Audio Course ·

2025/09/15

This episode explores authentication (AuthN) and authorization (AuthZ) for large language model (LLM) applications, highlighting their importance in managing identities and permissions. Authentication verifies that a user or system is who they claim to be, while authorization defines what actions or resources they are allowed to access. For certification readiness, learners must understand the difference between these two concepts, recognize their application in AI contexts, and describe

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22:28

Episode 17 — Secrets & Credential Hygiene

Certified - AI Security Audio Course ·

2025/09/15

This episode addresses secrets and credential hygiene, emphasizing their critical role in preventing leaks and privilege misuse in AI systems. Secrets include API keys, tokens, passwords, and configuration values embedded in prompts or environments. Learners preparing for exams must understand that secrets frequently appear in AI workflows, often stored insecurely or accidentally revealed in logs or outputs. Credential hygiene practices ensure that secrets are generated securely, stored in

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26:09

Episode 16 — Agents as an Attack Surface

Certified - AI Security Audio Course ·

2025/09/15

This episode introduces AI agents as a new and growing attack surface, highlighting how their autonomy and tool integration create unique risks. Agents differ from single-response models by persisting through plan-and-act loops, chaining multiple steps, and invoking external tools or APIs. For certification purposes, learners must understand that these design features expand the system boundary, exposing new trust assumptions and vulnerabilities. Risks include prompt injection, privilege

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21:23

Episode 15 — RAG Security II: Context Filtering &...

Certified - AI Security Audio Course ·

2025/09/15

This episode continues exploration of RAG security by examining context filtering and grounding as defenses for reliable outputs. Learners must understand context filtering as the screening of retrieved documents before they are passed to a model, ensuring that malicious or irrelevant content is excluded. Grounding is defined as aligning model outputs to trusted sources, improving accuracy and reducing hallucination. For exam purposes, mastery of these definitions and their application to

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29:02

Episode 14 — RAG Security I: Retrieval & Index Hardening

Certified - AI Security Audio Course ·

2025/09/15

This episode explores retrieval-augmented generation (RAG) security, focusing on retrieval and index hardening as foundational defenses. RAG combines language models with external document retrieval, which improves factual grounding but introduces risks. Learners preparing for exams must understand how poisoning of indexes, adversarial queries, and tampered retrieval sources can compromise model outputs. The episode explains why vector databases, document indexes, and retrievers are

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29:41

Episode 13 — Adversarial Evasion

Certified - AI Security Audio Course ·

2025/09/15

This episode introduces adversarial evasion, a class of attacks in which maliciously crafted inputs cause AI systems to misclassify or behave incorrectly. For exam purposes, learners must be able to define adversarial examples, explain why they are often imperceptible to humans, and distinguish them from poisoning attacks, which occur during training. Evasion attacks take place at inference time and undermine confidence in model reliability. The episode covers historical research origins in

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29:07

Episode 12 — Model Theft & Extraction

Certified - AI Security Audio Course ·

2025/09/15

This episode addresses model theft and extraction, highlighting how adversaries can replicate or steal valuable AI models. Model theft occurs when proprietary weights or architectures are exfiltrated, while model extraction involves querying an exposed API repeatedly to reconstruct decision boundaries or functionality. For exam purposes, learners must be able to distinguish between these two concepts and describe the potential impacts, which include intellectual property loss, competitive

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51 results

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