Certified - AI Security Audio Course

This opening episode provides a structured orientation to the AI Security and Threats Audio course series, helping listeners understand what the program covers and how to best engage with the material. The overview defines the scope of AI security by placing it within the broader context of cybersecurity and risk management, while clarifying the distinctive elements that make AI-specific security necessary. It explains how the episodes are organized, moving from foundational principles through attack surfaces, defenses, governance frameworks, and advanced considerations. The episode also outlines the intended audience, which includes exam candidates, practitioners, and professionals from related disciplines, while emphasizing accessibility for beginners. By framing AI security as both a technical and organizational discipline, the episode positions the Audio course as a comprehensive study and reference tool for learners at all levels.The description also introduces the concept of using checklists, transcripts, and structured resources to reinforce retention of exam-relevant material. It explains that each episode is designed to be self-contained, yet forms part of a coherent series that builds on prior topics for cumulative understanding. Scenarios are introduced as a way to contextualize threats and defenses, ensuring that learners connect theory with practice. Troubleshooting considerations, such as how to recognize gaps in current understanding or apply lessons across domains, are emphasized to prepare learners for certification exams. The episode closes with guidance on how to approach the course—either linearly or by focusing on specific areas most relevant to the listener’s role or goals—so that every learner can extract maximum value from the structured format. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

This episode defines the AI security landscape by mapping the assets, attack surfaces, and emerging threats that distinguish AI from classical application security. It introduces critical components such as training data, model weights, prompts, and external tools, explaining why each must be protected as an asset. The relevance for certification exams lies in understanding how these components shift trust boundaries and create new risks compared to traditional software systems. The episode emphasizes that adversaries target AI differently, often exploiting natural language, data poisoning, or model extraction techniques. By describing the breadth of risks, the episode establishes the foundation for examining each in detail throughout the Audio course.In its applied perspective, the episode explores how organizations must expand security programs to account for AI-specific challenges. Examples include leakage of personal information through outputs, manipulation of retrieval-augmented generation pipelines, and exploitation of agents connected to external systems. It discusses how exam candidates should recognize parallels and differences between AI security and established AppSec practices, noting where controls such as authentication, logging, and encryption remain essential but insufficient. Scenarios highlight how adversary motivations—ranging from fraud to disinformation—shape the threat landscape. The description underscores the importance of holistic defenses, aligning technical, organizational, and compliance strategies to manage this new class of risks. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

This episode explains the architecture of AI systems, breaking down their stages and components to show how trust boundaries shift across the lifecycle. Training, inference, retrieval-augmented generation (RAG), and agent frameworks are introduced as discrete but interconnected environments, each with distinct risks. For exam relevance, learners are expected to identify these architectural elements, describe where threats occur, and understand how adversaries exploit them. The discussion highlights how traditional security boundaries—such as network segmentation or user authentication—must be re-evaluated when applied to AI. Understanding these system dynamics is crucial for answering exam questions and for analyzing risks in real deployments.The applied discussion explores how architecture decisions affect overall system resilience. Examples include how training pipelines depend on secure data provenance, how inference APIs expose models to prompt injection or extraction attacks, and how agents connected to tools introduce risks of privilege escalation. The episode emphasizes practical considerations such as monitoring trust boundaries, enforcing least privilege, and mapping dependencies across cloud and on-premises environments. Troubleshooting scenarios illustrate how gaps in architecture create opportunities for attackers, reinforcing why governance of system design is as important as technical controls. By mastering these architectural concepts, learners gain both exam readiness and practical insight into AI security. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

This episode examines data lifecycle security, covering the journey of data from collection and labeling through storage, retention, deletion, and provenance management. It explains why data is the foundation of AI system reliability and how its misuse or compromise undermines security objectives. For certification preparation, learners are introduced to key definitions of provenance, integrity, and retention policies, while understanding how regulatory requirements drive data governance practices. The episode situates data lifecycle security as both a technical and compliance necessity, bridging privacy, accuracy, and accountability in AI environments.The applied discussion focuses on real-world considerations such as how unvetted datasets can introduce bias or poisoning, how insecure storage creates risks of leakage, and how failure to enforce deletion or retention policies leads to regulatory violations. Best practices include documenting data sources, applying encryption at rest and in transit, and ensuring role-based access controls for labeling and preprocessing steps. Troubleshooting scenarios emphasize what happens when provenance cannot be established or when training datasets contain sensitive information without consent. For exams and professional practice, this perspective reinforces why lifecycle controls must be embedded in organizational AI policies, not treated as optional afterthoughts. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

This episode introduces prompt injection and jailbreaks as fundamental AI-specific security risks. It defines prompt injection as malicious manipulation of model inputs to alter behavior and describes jailbreaks as methods for bypassing built-in safeguards. For certification purposes, learners must understand these concepts as new categories of vulnerabilities unique to AI, distinct from but conceptually parallel to classical injection attacks. The discussion highlights why prompt injection is considered one of the highest risks in generative AI systems, as it can expose sensitive data, trigger unintended actions, or produce unsafe outputs.The applied perspective explores common techniques used in injection and jailbreak attacks, including direct user prompts, obfuscated instructions, and role-playing contexts. It also explains consequences such as data leakage, reputational damage, or compromised tool integrations. Best practices are introduced, including guardrail filters, structured outputs, and monitoring of anomalies, while emphasizing that no single measure is sufficient. Troubleshooting scenarios include how systems fail when filters are static or when output handling is overlooked. The exam-relevant takeaway is that understanding these risks prepares candidates to describe, detect, and mitigate prompt injection attacks effectively in both testing and professional settings. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

This episode examines indirect and cross-domain prompt injections, which expand the attack surface by embedding malicious instructions in external sources such as documents, websites, or email content. Unlike direct injection, where the attacker provides inputs to the model directly, these threats exploit retrieval or integration features that feed information into the AI system automatically. Learners preparing for certification exams must understand the mechanics of these attacks, which occur when contextual data bypasses normal user input validation and reaches the model unchecked. The relevance lies in recognizing how indirect vectors can compromise confidentiality, integrity, and availability in AI environments, and why they present challenges that differ from classical injection risks.The applied discussion highlights scenarios such as a retrieval-augmented generation pipeline that fetches poisoned documents or a plugin that receives hidden instructions from a web source. Best practices include validating all retrieved data, implementing layered content filters, and designing workflows with isolation boundaries between model prompts and external data. Troubleshooting considerations emphasize how reliance on untrusted content sources creates cascading failures that are difficult to diagnose. For exam preparation, candidates must be able to articulate both the theoretical definitions and the operational defenses, making indirect prompt injection an essential area of study for AI security professionals. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

This episode explains the distinction and overlap between content safety and security in AI systems, a concept often emphasized in both professional practice and certification exams. Content safety refers to filtering or moderating outputs to prevent harmful or offensive material, while security focuses on protecting systems and assets from adversarial manipulation or data loss. Although they are related, treating them as identical can cause organizations to miss critical risks. Learners must grasp why an AI model can pass content safety tests yet still be vulnerable to prompt injection, data poisoning, or privacy leakage, making a dual approach essential. Understanding this distinction helps candidates evaluate scenarios in which filtering alone is insufficient to meet security objectives.In application, this distinction is illustrated by comparing moderation filters designed to block offensive text with monitoring systems aimed at detecting adversarial prompts or anomalous usage. A secure AI program requires both: safety filters to manage user experience and security defenses to protect organizational assets. Best practices include aligning safety policies with ethical and regulatory requirements, while embedding security controls across the entire AI lifecycle. Troubleshooting scenarios highlight failures when organizations rely solely on moderation layers, leaving underlying vulnerabilities unaddressed. For exam preparation, learners should be ready to differentiate safety measures from adversarial security controls and describe how the two domains reinforce each other without overlap. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

This episode introduces data poisoning as a high-priority threat in AI security, where adversaries deliberately insert malicious samples into training or fine-tuning datasets. For exam readiness, learners must understand how poisoning undermines model accuracy, introduces backdoors, or biases outputs toward attacker goals. The relevance of poisoning lies in its persistence, as compromised models may behave unpredictably long after training is complete. Definitions such as targeted versus indiscriminate poisoning, as well as the concept of trigger-based backdoors, are emphasized to ensure candidates can recognize variations in exam scenarios and real-world incidents.Applied examples include adversaries corrupting crowdsourced labeling platforms, inserting poisoned records into scraped datasets, or leveraging open repositories to distribute compromised models. Defensive strategies such as dataset provenance tracking, anomaly detection in data, and robust training algorithms are explored as ways to mitigate risk. Troubleshooting considerations focus on the difficulty of identifying poisoned samples at scale and the potential economic impact of retraining models from scratch. By mastering the definitions, implications, and defenses of data poisoning, learners develop a critical skill set for both exam performance and operational AI security. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

This episode covers training-time integrity, focusing on the assurance that data, processes, and infrastructure used in model development remain uncompromised. Learners preparing for exams must understand that threats at this stage include data tampering, corrupted labels, or manipulated hyperparameters. Unlike inference-time attacks, which target deployed models, training-time compromises affect the foundation of the model itself, potentially embedding vulnerabilities that persist throughout the lifecycle. The exam relevance lies in being able to identify how training-time risks manifest and what practices are used to safeguard against them.Examples of threats include adversaries with insider access altering training pipelines, attackers injecting mislabeled data into supervised learning sets, or subtle manipulations of evaluation metrics to distort reported accuracy. Best practices include reproducibility through version control, audit logs of dataset provenance, and multi-party review of training processes. Troubleshooting considerations emphasize detecting when anomalous behavior is due to data corruption rather than algorithmic flaws, a distinction often tested in certification contexts. For practitioners, ensuring training-time integrity is critical because any compromise at this stage undermines all subsequent defenses. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

This episode introduces privacy attacks in AI systems, focusing on techniques that reveal sensitive or personal information from training data or model behavior. Learners must be able to define key attack types, such as membership inference—determining whether a specific record was included in training—and model inversion, where attackers reconstruct approximate training inputs. The exam relevance lies in understanding not only the mechanics of these attacks but also their implications for regulatory compliance and user trust. Privacy risks are especially significant in domains such as healthcare, finance, and customer analytics, where sensitive data is central to AI adoption.In practical terms, privacy attacks exploit weaknesses in overfitting, poor anonymization, or weak defenses against memorization of training records. Scenarios include reconstructing patient data from medical AI systems or leaking user conversations from fine-tuned chat models. Best practices for mitigation include differential privacy, data minimization, and output filtering, with attention to the trade-offs between accuracy and protection. Troubleshooting considerations emphasize recognizing symptoms of leakage in outputs and integrating privacy audits into monitoring systems. Exam candidates should be prepared to evaluate privacy threats alongside technical and governance controls, demonstrating an ability to connect security practices with broader compliance frameworks. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

This episode explores privacy-preserving techniques designed to reduce the risk of sensitive information exposure in AI systems while maintaining utility of the models. Learners must understand concepts such as anonymization, pseudonymization, and data minimization, which limit identifiable information in training sets. Differential privacy is introduced as a mathematical framework that injects statistical noise into data or queries, providing measurable privacy guarantees. Federated learning is also explained as a decentralized training method that keeps raw data on user devices, mitigating risks of central collection. For exam purposes, candidates should be able to define these methods, explain how they align with regulatory frameworks, and recognize their role in ensuring privacy by design in AI workflows.The applied perspective emphasizes challenges and best practices when deploying privacy-preserving methods. Anonymization, while useful, may still leave data vulnerable to re-identification attacks if auxiliary datasets are available. Differential privacy protects individuals but introduces trade-offs with accuracy, requiring careful parameter tuning to balance utility and security. Federated learning reduces central exposure but creates new risks of poisoned or manipulated client updates. Real-world scenarios highlight how organizations apply layered combinations of these techniques to achieve compliance with global data protection laws. For certification preparation, learners must be ready to compare methods, describe their limitations, and demonstrate understanding of how they contribute to reducing privacy risks in AI systems. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

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 disadvantage, and undermining of security guarantees. These risks make model theft an enterprise-level concern, requiring both technical and governance-oriented defenses.The applied discussion examines scenarios such as adversaries using adaptive querying strategies against APIs, attackers stealing pre-trained weights from unsecured repositories, or insiders misusing privileged access to exfiltrate models. Defensive measures include authentication and rate limiting, anomaly detection in API traffic, and cryptographic watermarking or fingerprinting to prove ownership of models. The episode also emphasizes legal and compliance aspects, such as licensing terms and intellectual property protection, which often appear in exam questions. Troubleshooting considerations highlight the difficulty of distinguishing legitimate heavy usage from extraction attempts, underscoring the need for layered monitoring strategies. By mastering this topic, learners gain readiness to explain both attacker tactics and organizational safeguards in certification settings. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

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 image recognition and extends to natural language and audio domains, illustrating the cross-modal nature of the risk.The applied discussion highlights techniques for generating adversarial inputs, including gradient-based perturbations and black-box query methods. Examples range from modified stop signs that confuse autonomous vehicles to hidden commands embedded in audio targeting voice assistants. Defensive strategies include adversarial training, input preprocessing, and anomaly detection, though each has trade-offs in performance and scalability. For certification candidates, the exam relevance lies in recognizing definitions, attack mechanisms, and the limitations of current defenses. Real-world troubleshooting scenarios emphasize challenges of detecting subtle manipulations at runtime, reinforcing the need for layered monitoring and resilience. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

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 critical assets requiring protection, emphasizing that compromised retrieval pipelines can lead to misinformation, leakage, or unsafe instructions being passed to models.The applied discussion highlights scenarios such as malicious documents inserted into indexes, adversarial embeddings crafted to bypass similarity searches, or poisoned refresh cycles introducing corrupted content. Defensive strategies include provenance tracking of documents, automated validation pipelines, and anomaly detection for unusual retrieval queries. Multi-tenant isolation and encryption of index data are emphasized as best practices, particularly in enterprise settings. For certification readiness, candidates should be able to describe how retrieval systems create unique attack surfaces, outline mitigation strategies, and explain why layered defenses are required to secure RAG deployments. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

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 AI security is critical, as context and grounding directly affect confidentiality, integrity, and trustworthiness of results.In practice, the episode highlights scenarios where retrieved content contains hidden adversarial instructions or irrelevant noise that misleads the model. Defensive strategies include rule-based filters, machine learning classifiers for unsafe content, and trust scoring of sources. Structured grounding techniques, such as binding outputs to authoritative databases or knowledge graphs, are emphasized for high-stakes applications like healthcare or finance. Troubleshooting considerations explore challenges of balancing recall and precision, preventing over-blocking of useful content, and maintaining performance at scale. By mastering context filtering and grounding, learners will be prepared to explain exam questions and real-world defenses that keep RAG outputs accurate and secure. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

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 escalation, excessive resource consumption, and data exfiltration when agents interact with connected services. Recognizing how agents differ from classical models allows exam candidates to frame their answers within the context of evolving adversarial surfaces.The applied perspective covers scenarios such as agents issuing repeated API calls without oversight, retrieving poisoned content that alters their instructions, or escalating access through poorly scoped credentials. Best practices include sandboxing, rate limiting, least-privilege permissioning, and continuous monitoring of agent actions. Troubleshooting considerations emphasize challenges of detecting malicious behavior when tasks are multi-step and distributed across external systems. For certification readiness, learners must be able to describe both attack patterns and defensive strategies, showing an understanding of how agents multiply complexity in AI security environments. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

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 vault systems, rotated regularly, and protected against unauthorized access. The exam relevance lies in identifying weak practices that expose AI applications to exploitation and recognizing recommended industry safeguards.In real-world application, common failure modes include hard-coded credentials in source code, prompt-secret leakage during model conversations, and excessive privilege scopes for service accounts. Defensive strategies include adopting vault-based management systems, enforcing least-privilege access, and implementing automated rotation policies. Troubleshooting scenarios highlight how failure to audit credential usage can lead to escalation or insider misuse. By mastering credential hygiene, learners develop readiness to answer exam questions on authentication risks, as well as practical skills for building resilient AI platforms. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

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 how least privilege is enforced across sessions and scopes. The exam relevance lies in knowing how access control mechanisms secure inference endpoints, APIs, and integrated services in LLM applications.Practical examples include requiring multi-factor authentication for developer dashboards, implementing fine-grained scopes for plugin or connector access, and enforcing session expiration to reduce token misuse. Troubleshooting scenarios emphasize the dangers of weak AuthN/Z controls, such as broad-scoped tokens enabling privilege escalation or session hijacking. Best practices include centralized identity providers, strong logging of access events, and ongoing monitoring for anomalous patterns. Learners should be prepared to evaluate case studies where inadequate AuthN/Z undermined security, as well as describe exam-ready best practices that align with enterprise standards. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

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 defense-in-depth strategy that limits both harmful behavior and misuse. Definitions of allow lists, deny lists, and structured validators are emphasized as exam-ready terms.Applied perspectives highlight scenarios such as preventing leakage of secrets in generated text, enforcing compliance with industry-specific language restrictions, or validating that responses meet expected data structure before feeding them into workflows. Best practices include layering automated validators, integrating moderation filters, and designing resilient enforcement systems that degrade gracefully under pressure. Troubleshooting scenarios illustrate failures where absence of output checks led to unsafe automation or compliance breaches. Learners preparing for exams must be able to articulate both theoretical principles and practical defenses, demonstrating mastery of how policy enforcement strengthens AI system reliability. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.

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 in recognizing how red teaming complements audits and testing pipelines by uncovering weaknesses that ordinary development processes overlook.In practice, red team exercises involve crafting malicious prompts to bypass safety filters, probing retrieval pipelines for poisoned inputs, or testing agent workflows for tool misuse. Reporting must capture not only the exploit but also recommended mitigations, ensuring that findings drive actual fixes. Best practices include defining clear scope, establishing guardrails for safe testing, and integrating results into continuous improvement cycles. Troubleshooting considerations focus on avoiding “checklist testing” and instead simulating realistic adversary strategies. For certification exams, candidates should be able to describe red teaming as an iterative, structured, and goal-driven activity that enhances security maturity. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.