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Foundations of AI & Cybersecurity - Lesson 69: Internal Organizational Governance and Policy Controls This module explains how internal AI governance turns external laws, standards, and frameworks into practical rules employees and teams can follow. It covers AI policies,sanctioned versus unsanctioned tools, private versus public models, sensitive data controls, vendor reviews, and data sovere...

Foundations of AI & Cybersecurity - Lesson 68: External Laws, Regulations, and Global Standards This lesson explains how global AI governance is shaped by four layers: binding laws, ethical principles, certifiable standards, and practical risk frameworks. It covers the EU AI Act, OECD AI Principles, ISO/IEC 42001, and the NIST AI Risk Management Framework. The core message is that trustworthy...

Foundations of AI & Cybersecurity - Lesson 67: Shadow AI (Autonomous & Unapproved AI Systems) This chapter explains how Shadow AI emerges when employees or departments use unapproved AI tools outside official governance, security, and compliance processes. Itcovers the major risks of Shadow AI, including data leakage, regulatory violations, hidden attack surfaces, governance failures, inac...

Foundations of AI & Cybersecurity - Lesson 66: AI and the Core AI Risks (Threats and Exposure Categories) This lesson explains the six foundational AI risk categories organizations must manage: bias, accidental data leakage, reputational loss, model accuracy and performance failures, intellectual property risks, and autonomous system risks. It covers how these threats create legal, financial,...

Foundations of AI & Cybersecurity - Lesson 65: AI Security, Risk, and Governance Roles This chapter explains how Responsible AI reduces risk by building safety, fairness, transparency, privacy, and accountability into AI systems from the start. It covers nine principles: fairness, reliability, transparency, privacy and security, explainability, inclusiveness, accountability, consistency, and a...

Foundations of AI & Cybersecurity - Lesson 64: AI Security, Risk, and Governance Roles This lesson explains the key security, risk, and governance roles needed to protect enterprise AI systems. It covers AI Security Architects, AI Governance Engineers, AI Risk Analysts, and AI Auditors as distinct layers of oversight. The core message is that trustworthy AI requires independent validation, ris...

Foundations of AI & Cybersecurity - Lesson 62: AI Development and Engineering Roles This chapter explains the core engineering roles required to build secure, scalable, and production-ready AI systems. It covers Data Scientists, ML Engineers, AI Architects, Platform Engineers, Data Engineers, and MLOps Engineers, showing how each role protects a different part of the AI lifecycle. The core mes...

Foundations of AI & Cybersecurity - Lesson 62: AI Governance - Organizational Structures for Secure AI Systems This lesson explains why secure AI adoption requires formal governance, not scattered or uncontrolled AI activity. It covers the role of an AI Center of Excellence, policies, risk assessments, AI inventories, lifecycle oversight, andcross-functional accountability. The core message is...

Foundations of AI & Cybersecurity - Lesson 60: AI-Integrated DevSecOps - CI-CD Security Pipeline   This chapter explains how AI is integrated into DevSecOps pipelines to strengthen security across code, dependencies, testing, model validation, deployment, and rollback. It covers AI-enhanced code scanning, software composition analysis, unit testing, regression testing, model red-teaming, and p...

Foundations of AI & Cybersecurity - Lesson 56: AI-Automated Incident & IT Operations Management This lesson explains how AI automates incident response and IT operations by turning manual workflows into faster, more consistent processes. It covers ticket creation, triage, enrichment, root-cause analysis, remediation, change evaluation, rollback, drift detection, and approval workflows. The...

Foundations of AI & Cybersecurity - Lesson 54: 3.3.1 Enhanced Automation & Scripting Foundations This module explains how AI has evolved from a security analyst into an active execution layer for security and IT operations. It covers AI-driven scripting, low-codeautomation, autonomous agents, incident response, remediation, continuous monitoring, and attack surface management. The core mes...

Foundations of AI & Cybersecurity - Lesson 52: AI-Automated Attack Construction & Operations This lesson explains how AI automates cyberattack construction and operations across the executionphase. It covers AI-powered attack discovery, malicious payload generation, malware variant production, honeypot deception, DDoS orchestration, and full campaign automation. The core message is that AI...

Foundations of AI & Cybersecurity - Lesson 51: AI-Enabled Evasion & Obfuscation Techniques This lesson explains how AI enables malware to evade detection by continuously changing its code, behavior, and communication patterns at machine speed. It covers techniques such as polymorphic and metamorphic malware, adversarial inputs that fool machine learning defenses, and stealth tactics like t...

Foundations of AI & Cybersecurity - Lesson 50: AI-Driven Reconnaissance and Target Discovery This module explains how AI turns reconnaissance into a fast, automated, and predictive intelligence process. It covers how attackers use AI to map digital assets, correlate public and leaked data, profile people, and build attack paths. The core message is that organizations must understand their expo...

Foundations of AI & Cybersecurity - Lesson 49: AI-Enhanced Identity & Influence Attacks This lesson explains how AI is reshaping deception through deepfakes, synthetic content, and highly personalized social engineering. It covers how attackers can impersonate trusted people, manipulate decisions, and scale scams across text, voice, video, and images. The core message is that security must...

Foundations of AI & Cybersecurity - Lesson 47: Use Cases for Using Al-Enabled Tools to Facilitate Security Tasks This lesson explains how AI-enabled tools act as force multipliers for cybersecurity teams facing overwhelming volumes of alerts, logs, and threat data. It covers practical use cases including threat modeling, secure coding, vulnerability analysis, automated penetration testing, ano...

Foundations of AI & Cybersecurity - Lesson 44: Using Al-Enabled Tools to Facilitate Security Tasks This chapter explains how AI-enabled tools can help secure AI systems across development, operations, and incident response. It covers IDE plug-ins, browser plug-ins, CLI assistants, chatbots, personal assistants, and MCP servers aspractical tools for prevention, detection, response, and assuranc...

Foundations of AI & Cybersecurity - Lesson 43: Applying Compensating Controls This lesson explains how compensating controls create a safety net for AI systems after risks, weaknesses, or misconfigurations are identified. It covers eight key controls: prompt firewalls, model guardrails, access controls, data integrity controls, encryption, prompt templates, rate limiting, and least privilege....

Foundations of AI & Cybersecurity - Lesson 42: Scenario for Determining Root Cause and Evidence Strength This scenario lesson explains how to investigate an AI security incident by determining what happened, why it happened, and how strong the evidence is before applying a fix. It walks through timeline reconstruction, prompt and context correlation, RAG inspection, telemetry review, tool acti...

Foundations of AI & Cybersecurity - Lesson 41: Determining Root Cause and Evidence Strength This module explains how to investigate AI security incidents by determining root cause before jumping to mitigation. It covers key tasks such as timeline reconstruction, prompt correlation, telemetry review, RAG inspection, tool invocation analysis, identity mapping, and evidence strength classificatio...

Foundations of AI & Cybersecurity - Lesson 40: Scenario for Identifying Direct Model-Targeted Attacks This scenario lesson explains how AI attack indicators act as an early warning system for detecting misuse, compromise, and model drift. It covers hallucinations, output integrity attacks, sensitive information disclosure, insecure output handling,excessive agency, overreliance, and model skew...

Foundations of AI & Cybersecurity - Lesson 39: Identifying Direct Model-Targeted Attacks This chapter explains seven early warning signs that an AI system may be compromised, misused, or drifting away from safe and reliable behavior. It covers key indicators such as hallucinations, output integrity attacks, sensitive data disclosure, insecure output handling, excessive agency, overreliance, an...

Foundations of AI & Cybersecurity - Lesson 38: Scenario on Analyzing the Attack Surface,& Classify the Attack Type This scenario lesson explains how to secure an AI system by identifying where it is exposed,classifying the type of attack, and applying the right compensating controls. It walks through eight common AI attack scenarios, including prompt injection, input manipulation, guardrai...

Foundations of AI & Cybersecurity - Lesson 37: Analyzing the Attack Surface & Classify the Attack Type This module explains that identifying an AI attack is only the first step, because effective defense requires analyzing the attack surface, classifying the specific attack type, and applying the right compensating controls. It walks through common AI attack types such as prompt injection,...

Foundations of AI & Cybersecurity - Lesson 36: Scenario on Identifying the Attack Indicators This scenario lesson shows how AI attacks often reveal themselves through subtle behavioral indicators rather than obvious technical failures. It shows how signs like hallucinations, output manipulation, sensitive data disclosure, insecure execution, excessive autonomy, overreliance, and model drift ca...