All Episodes
Displaying 21 - 40 of 91 in total
Episode 21 — Refresh training when threats, tools, and regulations change (Task 21)
This episode explains how to keep AI security awareness training current so it remains effective as new model capabilities, attacker methods, and compliance obligation...
Episode 22 — Inventory AI assets: models, prompts, data, and key dependencies (Task 13)
This episode teaches how to build an AI asset inventory that is useful for security, audit, and incident response, which AAISM scenarios often test by asking what must...
Episode 23 — Classify AI assets by sensitivity, criticality, and compliance scope (Task 13)
This episode explains how to classify AI assets so controls can be applied proportionally, which is a common AAISM decision point when scenarios ask what to protect fi...
Episode 24 — Keep the AI inventory accurate with routine governance checks (Task 13)
This episode shows how to keep an AI inventory accurate over time, because AAISM expects you to treat inventory as a living control rather than a one-time project. You...
Episode 25 — Identify data risks across the AI life cycle: leaks and tampering (Task 14)
This episode teaches how to identify data risks across the AI life cycle, focusing on leakage and tampering threats that AAISM frequently tests through scenarios invol...
Episode 26 — Protect training and test data with access control and secure storage (Task 14)
This episode explains how to protect training and test data so confidentiality and compliance are preserved, and why AAISM questions often focus on access control and ...
Episode 27 — Preserve data integrity so models stay reliable and trustworthy (Task 14)
This episode teaches integrity protections that keep AI data trustworthy, because AAISM scenarios often hinge on whether model behavior can be relied on when data pipe...
Episode 28 — Manage retention and deletion to reduce long-term AI data exposure (Task 14)
This episode focuses on retention and deletion as risk-reduction controls for AI data, which AAISM tests through scenarios involving compliance obligations, privacy ex...
Episode 29 — Build an AI security program that fits the enterprise security program (Task 19)
This episode explains how to integrate AI security into the broader enterprise security program so controls are consistent, measurable, and supportable, which is a com...
Episode 30 — Define AI security metrics leaders can understand and act on (Task 18)
This episode teaches how to define AI security metrics that drive decisions, because AAISM scenarios often test whether you can choose measurements that are meaningful...
Episode 31 — Monitor AI metrics to spot misuse, drift, and early incident signals (Task 18)
This episode explains how to monitor AI metrics in a way that reveals misuse, drift, and early incident signals before they become customer-impacting failures, which i...
Episode 32 — Use metrics to prioritize work and prove security program value (Task 18)
This episode teaches how to use AI security metrics to prioritize limited time and budget while also demonstrating program value in terms leaders understand, which AAI...
Episode 33 — Review AI security tools by coverage, gaps, and operational fit (Task 19)
This episode focuses on evaluating AI security tools the way the AAISM exam expects: by asking what risks they cover, what gaps remain, and whether the tools can actua...
Episode 34 — Implement AI security tools into monitoring, alerting, and response workflows (Task 19)
This episode explains how to implement AI security tools so they produce usable monitoring, alerts, and response actions rather than isolated dashboards, which AAISM s...
Episode 35 — Operationalize tools with tuning, ownership, and measurable outcomes (Task 19)
This episode teaches how to operationalize AI security tools so they deliver measurable risk reduction over time, which the AAISM exam tests through questions about su...
Episode 36 — Domain 1 quick review: governance, policies, assets, metrics, and training (Tasks 1–3)
This episode reinforces Domain 1 by connecting governance, policies, asset inventory, metrics, and training into one coherent operating model, because AAISM questions ...
Episode 37 — Investigate AI security incidents by collecting the right evidence fast (Task 15)
This episode explains how to investigate AI security incidents by quickly collecting evidence that preserves accuracy under pressure, which AAISM scenarios test throug...
Episode 38 — Document AI incidents clearly for regulators, contracts, and executive updates (Task 15)
This episode teaches how to document AI incidents so the record supports regulatory expectations, contractual commitments, and executive decision-making, which the AAI...
Episode 39 — Report AI security incidents on time without losing accuracy (Task 15)
This episode focuses on timely incident reporting while preserving accuracy, which AAISM treats as a disciplined process that balances speed, evidence, and stakeholder...
Episode 40 — Contain AI incidents quickly by limiting access and stopping risky flows (Task 16)
This episode teaches containment actions tailored to AI incidents, emphasizing rapid access limitation and flow interruption, which AAISM often tests as the most defen...