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Foundations of AI & Cybersecurity - Lesson 35: Identifying the Attack Indicators This module explains how AI attacks and failures often appear as subtle behavioral signals rather than obvious breaches. It outlines seven key indicators, including hallucinations, output manipulation, data leakage, insecure execution, excessive autonomy, human overreliance, and model drift, that act as early warn...

Foundations of AI & Cybersecurity - Lesson 34: Scenario on Auditing Model Output for Risks This scenario lesson explains that auditing AI outputs must be treated as a continuous operational control, not a one-time review step. It shows how grounding against hallucinations, validating accuracy, testing for fairness, and enforcing access controls work together to make AI outputs safer and more t...

Foundations of AI & Cybersecurity - Lesson 33: Audit Model Output for Risks This lesson explains that securing AI requires continuous auditing of what the model actually outputs, not just the infrastructure around it. It focuses on four major output risks: hallucinations, accuracy failures, bias, and unauthorizedaccess, and shows how each can lead to harmful decisions, compliance issues, or lo...

Foundations of AI & Cybersecurity - Lesson 32: Scenario on Analyzing Model Behavior This scenario example explains how AI confidence must be turned into an operational control, not left as a background metric. It shows how calibrated confidence scores, risk-based thresholds, logging, and human review workflows help organizations decide when AI can proceed, when it must escalate, and when it sh...

Foundations of AI & Cybersecurity - Lesson 31: Analyze Model Behavior This module explains why model response confidence is a critical security and governance control, not just a technical metric. It shows how calibrated confidence scores help detect hallucinations, support risk-based routing to humans, and provide audit evidence for high-impact AI decisions. The core lesson is that trustworth...

Foundations of AI & Cybersecurity - Lesson 30: Scenario on Generating and Processing Logs This scenario lesson explains how a secure AI logging strategy depends on three connected capabilities: active monitoring, careful sanitization, and strong protection of the logs themselves. It shows how organizations can use logs not just to investigate incidents, but to detect misuse in real time, preve...

Foundations of AI & Cybersecurity - Lesson 29: Generate & Process Logs This lesson explains that AI logging must do three things well at the same time: monitor activity, sanitize sensitive content, and protect the logs from tampering or unauthorized access. It shows why logs are the only reliable way to investigate AIincidents, measure guardrail performance, and support auditability withou...

Foundations of AI & Cybersecurity - Lesson 28: Scenario on Capture & Observe AI Activity This scenario shows how enterprise AI security becomes real only when prompt monitoring, rate monitoring, and cost monitoring work together as a single defense system. It explains how these three controls help detect data leakage, automated abuse, compromised accounts, and runaway spending before they...

Foundations of AI & Cybersecurity - Lesson 27: Monitoring and Auditing AI systems - Capture & Observe AI Activity This chapter explains that trustworthy AI depends on visibility into three things at all times: what users send and receive, how fast the system is being used, and what that usage is costing. It shows how prompt monitoring, rate monitoring, and cost monitoring work together to...

Foundations of AI & Cybersecurity - Lesson 26: Scenario on Data Safety This scenario lesson with the Automate Corporation example explains why enterprise AI security depends on five foundational data controls working together: anonymization, classification, redaction, masking, and minimization. It shows how each control addresses a different part of the data risk problem, from protecting ident...

Foundations of AI & Cybersecurity - Lesson 25: Data Safety This module explains the five foundational data safety controls every AI system needs: anonymization, classification, redaction, masking, and minimization. It shows how these controls work together to prevent models from memorizing, exposing, or misusing sensitive information. The core point is that safe AI starts with controlling the...

Foundations of AI & Cybersecurity - Lesson 24: Scenario Using Encryption Requirements This scenario lesson example explains how Automat Corp. is securing AI that requires protecting data across all three states: in transit, at rest, and in use. It shows that encryption for movement and storage is necessary, but data being actively processed is often the most exposed and overlooked risk. The ke...

Foundations of AI & Cybersecurity - Lesson 23: Encryption requirements This chapter explains why AI data must be protected in all three states: in transit, at rest, and in use. It shows how each state introduces different risks, from interception and model theft to memory scraping and sensitive data exposure during inference. The key point is that secure AI depends on encrypting data across it...

Foundations of AI & Cybersecurity - Lesson 22: Scenario Implementing Appropriate Access controls for an AI System This module is a scenario, involving Automate Corporation, that outlines how enterprise AI systems must be secured through a multi-layered access control strategy spanning models, data, agents, and network/API layers. It shows how combining identity controls, least privilege access...

Foundations of AI & Cybersecurity - Lesson 21: Building Secure AI - Requirements Phase with API Gateway Security and Interaction Controls This lesson explains why AI security must begin at the interaction layer, before requests ever reach the model. It introduces the API gateway as the first line of defense and shows how controls like prompt firewalls, rate limits, tokenlimits, input quotas, m...

Foundations of AI & Cybersecurity - Lesson 20: Building Secure AI - Requirements Phase - Using Guardrail Assurance, Testing, and Validation This lesson explains why AI guardrails must be treated as formal requirements from the very beginning, not added later as optional protections. It focuses on three pillars: guardrail assurance to define what the system must prevent, guardrail testing to pr...

Foundations of AI & Cybersecurity - Lesson 19: Building Secure AI - Requirements Phase - Implementing Model-Level Security and Control Design This module explains why AI security must begin in the requirements phase, before a model ever goes live. It focuses on two foundational protections: model evaluation to stress-test for risks like prompt injection, hallucination, and data leakage, and mo...

Foundations of AI & Cybersecurity - Lesson 18: Scenario using AI threat-modeling resources This scenario-based lesson explains how AI security frameworks work best when used together rather than in isolation. It shows how OWASP, MITRE ATLAS, NIST AI RMF, STRIDE-for-AI, and supply chain models each play a different role in identifying vulnerabilities, modeling attacks, and aligning security to...

Foundations of AI & Cybersecurity - Lesson 17: Explaining AI threat-modeling resources This module explains the main resources and frameworks used to understand AI threats, risks, and vulnerabilities across different layers of an AI system. It shows how tools like the OWASP Top 10 lists, MITRE ATLAS, the MIT AI Risk Repository, and the NIST AI Risk Management Framework help teams move from vag...

Foundations of AI & Cybersecurity - Lesson 16: Bad Actors’ Use of AI in Cyber Attacks This lesson explains how bad actors are using AI to scale and improve cyber attacks, from personalized phishing and deepfakes to polymorphic malware and adversarial evasion. It shows that offensive use now spans multiple AI types, including generative AI, large language models, GANs, deep learning, and transf...

Foundations of AI & Cybersecurity - Lesson 15: Secure Feedback, Audit, and Continuous Improvement This module explains why AI systems cannot be treated as set-it-and-forget-it tools after deployment. It focuses on model drift, evolving attacker behavior, and the need for secure feedback loops that continuously collect, analyze, update, and re-deploy improvements. Without that cycle, AI becomes...

Foundations of AI & Cybersecurity - Lesson 14: Secure Deployment and Operational Defense This lesson explains why deployment is the point where AI models become truly vulnerable, because they are exposed to real users, APIs, and adversaries for the first time. It covers the main post-launch threats, including API misuse, inference attacks, and data leakage, along with the need for secure deplo...

Foundations of AI & Cybersecurity - Lesson 13: Secure Model Engineering and Risk Controls This chapter explains why AI security must be engineered into the model from the beginning, not added after deployment. It focuses on three foundational risks during model creation: poisoning, manipulation, and drift, and shows how weak development, evaluation, or validation can embed long-term vulnerabil...

Foundations of AI & Cybersecurity - Lesson 12: Secure and Trusted Data Foundations This chapter explains why secure AI depends on secure and trustworthy data from the very beginning. It shows how data acts as the source code of an AI system, shaping what the model learns, how it behaves, and where its weaknesses emerge. If the data is biased, poisoned, or poorly prepared, the AI will inherit t...

Foundations of AI & Cybersecurity - Lesson 11: Secure AI Strategy and Governance This module explains why secure AI starts with clear intent and organizational alignment, not just technical controls added later. It shows how defining purpose, ownership, and risk boundaries early helps prevent misuse, reduce attack surface, and avoid uncontrolled Shadow AI. Human oversight and validation are ce...