Lesson 1Access controls and role-based permissions, least privilege, special access watchinDis section explain how to plan access controls and role-based permissions for AI systems, enforce least privilege, and watch special access so sensitive data and admin functions stay tight controlled.
Defining AI-specific roles and permissionsImplementing least privilege for AI adminsStrong authentication for privileged usersSession recording and just-in-time accessPeriodic access review and recertificationLesson 2Loggin, audit trails, and unchangeable loggin for data access and model query recordsDis section cover loggin plans for AI systems, includin detailed audit trails and unchangeable logs for data access and model queries, allowin checks, accountability, and proof for regulator or inside reviews.
Defining AI logging scope and granularityCapturing user, admin, and system actionsImmutable logging and tamper resistanceLog minimization and pseudonymizationLog review, alerting, and investigationsLesson 3Data cuttin small and pre-processin: tricks for reducin PII before sendin to LLMDis section explain data cuttin small and pre-processin tricks dat reduce personal data before sendin to AI models, usin coverin, groupin, and changin to lower risk while keepin use for business cases.
Identifying unnecessary personal data fieldsRedaction and masking of free-text inputsAggregation and generalization techniquesEdge preprocessing before API submissionBalancing utility with minimization dutiesLesson 4Input filterin and prompt engineerin: removin sensitive data, pattern scrubbin, NLP classifiersDis section focus on input filterin and prompt engineerin to remove sensitive data before processin, usin pattern scrubbin and NLP classifiers to spot risky content and enforce company rules at de edge.
Pattern-based scrubbing of identifiersNLP classifiers for sensitive categoriesPrompt templates that avoid PII captureReal-time input validation and blockingUser guidance and consent at input timeLesson 5Governance: DPIA mixin, Data Processin Agreements, record updates and change controlDis section describe governance setups for AI, includin mixin DPIAs, handlin Data Processin Agreements, and keepin records and change control so system changes stay clear, checked, and followin rules.
When and how to run AI-focused DPIAsKey DPA clauses for AI processingMaintaining records of processing for AIChange control for models and datasetsGovernance forums and approval workflowsLesson 6Pseudonymization and tokenization ways for free-text data and structured fieldsDis section look at pseudonymization and tokenization plans for free-text and structured data, showin how to swap identifiers wid reversible or irreversible tokens while handlin re-identification and key separation risks.
Pseudonymization versus anonymization limitsTokenization for structured identifiersHandling names and IDs in free-text dataKey and token vault management controlsRe-identification risk assessment methodsLesson 7Output filterin and post-processin: sensitivity spottin, hallucination spottin, confidence scorinDis section cover tools dat check and adjust AI outputs to spot sensitive data, find hallucinations, and use confidence scorin so risky answers get blocked, flagged, or sent for review before reachin users.
Detecting personal and sensitive data in model outputsHallucination detection rules and model ensemblesDesigning confidence scores and thresholdsHuman review workflows for risky responsesUser feedback loops to refine output filtersLesson 8Retention rules, automatic deletion, and backup retention match wid purpose limitinDis section explain how to set retention times for AI data, set automatic deletion, and match backups wid purpose limitin so trainin data, logs, and prompts no stay longer dan needed or used wrong.
Mapping data categories to retention periodsAutomated deletion of prompts and logsBackup retention and restore testingHandling legal holds and exceptionsDocumenting retention decisions for auditsLesson 9Sandboxin and rate-limitin API calls; slowin, request checkin, and queuinDis section explain how to box off AI services, control traffic amount, and check incomin requests usin sandboxin, rate limits, slowin down, and queuin so systems stay steady, safe, and strong against abuse or denial-of-service.
Designing API rate limits and burst controlsSandbox environments for testing AI featuresRequest validation and schema enforcementQueueing strategies for high-volume workloadsAbuse detection and automated blocking rulesLesson 10Vendor checkin: security question lists, SOC/ISO reports, penetration test needsDis section detail how to check AI vendors usin steady due diligence, includin security question lists, SOC and ISO reports, and penetration test needs, makin sure processors meet legal, security, and strongness hopes.
Building AI-specific security questionnairesReviewing SOC 2, ISO 27001, and similar reportsPenetration testing scope for AI integrationsAssessing data residency and subcontractorsOngoing vendor monitoring and reassessmentLesson 11Runnin measures: staff trainin, privacy-by-design, incident response plans, breach notice stepsDis section focus on runnin safeguards like staff trainin, privacy-by-design ways, incident response plans, and breach notice steps dat make sure AI work stay followin rules, strong, and well recorded.
AI-specific security and privacy trainingEmbedding privacy by design in AI projectsIncident detection and triage for AI systemsAI incident response and communication plansBreach notification timelines and contentLesson 12Encryption movin and restin; key handlin and envelope encryption for model inputs/outputsDis section cover encryption movin and restin for AI data, includin key handlin and envelope encryption patterns dat protect prompts, outputs, and logs while supportin access control, turnin, and regulator hopes.
TLS configuration for AI APIs and servicesDisk, database, and object storage encryptionEnvelope encryption for prompts and outputsKey lifecycle, rotation, and segregationHSMs and cloud KMS integration options
