Privacy
Privacy is the protection and controlled handling of information or activities to prevent inappropriate access, collection, use, or disclosure.
Data for All
The safeguarding of information about people against unauthorized collection, access, use, disclosure, or retention.
- How it is used
- Frames everyday data sharing as a question of personal choice, organizational practice, and legal protection.
- How deep it goes
- Defines privacy and distinguishes human need from legal right, then examines practical limits without teaching technical implementation.
- Examples
- Uses connected household devices and Chrome’s incognito mode to show everyday concerns and the limits of a privacy feature.
- What you can do after
- Evaluate/understand privacy claims by checking what remains recorded and considering whom to give personal data.
First-Party Data Activation
Safeguards that prevent improper collection, use, disclosure, or exposure of personal information.
- How it is used
- Guides personalization and first-party data use while maintaining customer consent, trust, and responsible handling across the marketing journey.
- How deep it goes
- Develops principles and layer-by-layer considerations, then names channel safeguards without providing technical implementation steps.
- Examples
- Uses a purchase-history recommendation explanation, reported breaches, and website, email, and social-media decisions to make risks concrete.
- What you can do after
- Apply it by reviewing data collection, protection, user controls, and channel-specific practices throughout a marketing data journey.
LLMs in Production
The protection of data from unauthorized access, disclosure, misuse, or loss while respecting requirements governing personal information.
- How it is used
- Frames privacy as a reason to choose local LLM development over sending sensitive information to an external service.
- How deep it goes
- Explains the deployment trade-off through scenarios but does not specify privacy controls or a secure deployment procedure.
- Examples
- Uses sensitive military maintenance manuals and police analytics conversations to illustrate why a hosted assistant may be unsuitable.
- What you can do after
- Evaluate/understand when sensitive data makes local LLM development preferable to uploading information to another company.
The Art of AI Product Development
Privacy is the protection of individuals' or organizations' sensitive information from unauthorized collection, disclosure, or use.
- How it is used
- Positions privacy as a governance and user-trust concern when AI products collect or use customer data.
- How deep it goes
- Defines privacy against confidentiality and identifies product safeguards and trade-offs, without detailing their technical implementation.
- Examples
- Uses exposed voice and account data, a forecasting platform’s safeguards, and self-hosting sensitive customer data as examples.
- What you can do after
- Evaluate/understand it by weighing data-use limits and deployment choices against performance, while communicating specific controls to users.
Read how The Art of AI Product Development explains privacy →
Introduction to Generative AI, Second Edition
Privacy is the protection of people and their information from unwanted observation, collection, use, or disclosure.
- How it is used
- Frames privacy as a shared responsibility across AI development, deployment, workplace use, regulation, and everyday interaction.
- How deep it goes
- Traces risks from training-data memorization through prompt disclosure and regulatory response, while stopping short of implementation detail.
- Examples
- Uses confidential contract prompts, memorized financial and medical information, exposed chat histories, and public social-media training controversies.
- What you can do after
- Apply privacy precautions by limiting sensitive prompts, checking permissions, and evaluating how AI systems collect and use data.
Read how Introduction to Generative AI, Second Edition explains privacy →
Effective Data Analysis
Privacy is the ability of people to control access to information about themselves and how that information is collected or used.
- How it is used
- Protects people’s information, agency, and rights when analytics collects, stores, interprets, or disseminates data.
- How deep it goes
- Explains masking, consent, ethical review, and regulatory variation, but stops short of specifying technical anonymization procedures or complete legal requirements.
- Examples
- Uses masked location and IP data, loan analyses by demographic group, and an informed consent document for an artificial-intelligence survey.
- What you can do after
- Apply via informed consent, personally identifiable information masking, cautious interpretation, and responsible handling of sensitive information.
Build AI-Enhanced Web Apps
Data privacy is the protection and proper governance of information about identifiable people, including how it is collected, stored, processed, and deleted.
- How it is used
- Guides the handling of user information when AI web applications store chat histories, call LLM providers, and move into production.
- How deep it goes
- Provides a procedural checklist for sensitivity, encryption, access, retention, and de-identification, but does not specify an end-to-end implementation.
- Examples
- Uses chat histories containing financial or medical details and a production checklist covering provider policies, encryption, and anonymization.
- What you can do after
- Evaluate/understand privacy risks in AI application deployment and select controls for stored histories and data shared with LLM providers.
Delivering Machine Learning Projects: From design to deployment
Data privacy is the protection and governance of information about people, including how it is collected, accessed, used, and disclosed.
- How it is used
- Treats privacy as a pre-project viability constraint and a production-readiness requirement for ML data and models.
- How deep it goes
- Defines top-level requirements, stakeholder negotiation, risk registration, and model-leakage testing, without specifying privacy-preserving implementation techniques.
- Examples
- Uses unknown personal-data origins, missing consent, and medical-record prompts as concrete privacy-risk examples.
- What you can do after
- Evaluate data usage and model behavior, assign privacy stakeholders, and escalate unresolved requirements through the project risk register.
Read how Delivering Machine Learning Projects: From design to deployment explains privacy →
Generative AI in Action
Privacy is the protection of personal or confidential information from inappropriate collection, access, use, or disclosure.
- How it is used
- Frames privacy as a constraint on using confidential indexed data and proprietary data in enterprise RAG deployments.
- How deep it goes
- Discusses deployment risks and service capabilities, without specifying access-control designs or step-by-step compliance procedures.
- Examples
- Contrasts enterprise-controlled proprietary RAG data with larger context windows and lists Azure OpenAI’s enterprise privacy features.
- What you can do after
- Evaluate/understand privacy risks and available platform controls when planning an enterprise application that uses confidential data.
Machine Learning System Design
The protection of information about individuals or entities from unauthorized collection, use, disclosure, or inference.
- How it is used
- Treats privacy as a cross-lifecycle design, platform-selection, and production-deployment constraint.
- How deep it goes
- Connects privacy to design, platforms, and serving, but stops short of specifying privacy-preserving algorithms or regulatory procedures.
- Examples
- Uses healthcare and legal data, backend-versus-mobile deployment, model reverse engineering, and LLM jailbreaks as privacy-adjacent cases.
- What you can do after
- Evaluate privacy exposure and tradeoffs when designing training platforms and serving systems.
Starting Data Analytics with Generative AI and Python
The protection and proper governance of information about identifiable people during its collection, processing, storage, and disclosure.
- How it is used
- Guides responsible decisions about collecting, sharing, storing, and processing sensitive data with generative AI.
- How deep it goes
- Connects data categories, access, storage, logging, accountability, and policy controls, but stops short of detailed legal analysis.
- Examples
- Uses customer records, private communications, financial information, organizational policies, model logs, and external-provider data sharing.
- What you can do after
- Operate generative AI more safely by classifying data, checking access, reviewing provider controls, and assigning privacy accountability.
Read how Starting Data Analytics with Generative AI and Python explains privacy →
Building Reliable AI Systems
The protection of information about people from inappropriate collection, access, use, or disclosure.
- How it is used
- Positions privacy as a production reliability requirement that protects users’ sensitive information before exposure occurs.
- How deep it goes
- Explains the privacy layer’s rationale and lists operational safeguards, but does not specify redaction algorithms or regulatory mappings.
- Examples
- Uses employees’ submission of confidential Samsung information to ChatGPT as an example of sensitive inputs reaching an external service.
- What you can do after
- Operate/configure it by limiting stored data, adding PII detection and redaction, and giving users control of their data.
Authorization in Action
Privacy, or information privacy, is the ability of individuals to control or limit the collection, access, sharing, and use of information about them.
- How it is used
- Guides digital identity, authorization, and governance decisions that control personal-data exposure.
- How deep it goes
- Distinguishes privacy from confidentiality and authenticity, then operationalizes it through governance and PBAC rather than detailed implementation.
- Examples
- Uses virtual healthcare communication, authenticated banking, employee need-to-know access, and hospital insurance verification.
- What you can do after
- Apply governance and PBAC to restrict data access to necessary parties, purposes, situations, and thresholds.
Software Testing with Generative AI
Customer privacy is the protection of information and confidentiality associated with people who use or receive a service.
- How it is used
- Guides decisions about what customer, user, or organizational data can be sent to LLMs during test-data generation.
- How deep it goes
- Explains legal, moral, and policy obligations but stops short of prescribing a detailed privacy-control procedure.
- Examples
- Uses confidential source code, internal meeting notes, dummy SQL structures, rooms, bookings, and user data as privacy cases.
- What you can do after
- Operate LLM-assisted test-data generation while selecting permissible inputs and framing prompts around privacy constraints.
Read how Software Testing with Generative AI explains privacy →
Machine Learning Platform Engineering
The protection of information about individuals or sensitive entities from inappropriate disclosure or use.
- How it is used
- Protects data and model behavior across MLOps workflows and production serving.
- How deep it goes
- Connects privacy to workflow and deployment risks but stops short of specifying implementation controls or privacy techniques.
- Examples
- Uses large-scale data management and deployed-model information leakage as operational examples.
- What you can do after
- Evaluate privacy risks across data workflows and model-serving architectures.
Read how Machine Learning Platform Engineering explains privacy →
AI for Everyday IT
Privacy is the protection of individuals' or organizations' information and control over how it is collected, accessed, and used.
- How it is used
- Protects sensitive documents, workplace communications, and source code when using AI services.
- How deep it goes
- Explains practical controls including tool approval, redaction, logging, verification, versioning, and local processing.
- Examples
- Uses AI-processed documents, workplace messages, patient records, proprietary code, and model-processing headers as examples.
- What you can do after
- Operate AI tools more safely by limiting shared data, selecting suitable services, and documenting human verification.
AI-Powered Search
Privacy is the protection of information about a person from unauthorized access, use, or disclosure.
- How it is used
- Protects users when personalized search collects and exposes past interaction behavior.
- How deep it goes
- Frames privacy operationally through behavioral signals, transparency, opt-out controls, and conservative personalization.
- Examples
- Uses movie, bookstore, and grocery recommendations to illustrate sensitive inferences from interaction history.
- What you can do after
- Apply it via transparency, opt-out controls, and conservative personalization limited to the top few results.
Effective Conversational AI
Privacy is the protection and appropriate handling of information about identifiable individuals.
- How it is used
- Protects confidential conversational-AI data when selecting an LLM for augmentation.
- How deep it goes
- Checks data retention and training permissions, but stops short of detailing privacy controls or implementation.
- Examples
- Uses confidential corporate conversational data and vaccine-appointment flows involving personal information.
- What you can do after
- Evaluate LLM choices and conversational-AI changes for data-retention, security, and regulatory risks.
Build Applications with Local AI Models on a Mac
Privacy is the protection of a person's information from unauthorized collection, retention, use, or disclosure.
- How it is used
- Motivates local voice-enabled AI by contrasting on-device conversations with information sent to cloud services.
- How deep it goes
- Explains privacy through a cloud/local comparison and sensitive-data scenarios, while qualifying protection by application, operating-system, and network configuration.
- Examples
- Uses personal journaling, business documents, legal files, medical information, student data, and an airplane-mode chatbot test.
- What you can do after
- Evaluate/understand it by testing the Ollama chatbot offline and assessing whether its supporting components keep information on-device.
Read how Build Applications with Local AI Models on a Mac explains privacy →
Data Without Labels
The protection of individuals' or organizations' information from unauthorized access, use, or disclosure.
- How it is used
- Guides responsible deployment of ChatGPT and GenAI systems that handle information from users, employees, customers, or other stakeholders.
- How deep it goes
- Defines privacy as a governance requirement and connects it to safeguards, policies, supervision, testing, and monitoring without specifying technical controls.
- Examples
- Uses chatbot conversations, virtual assistants, real-world data, user feedback, and privacy policies as concrete contexts.
- What you can do after
- Operate GenAI systems with privacy guidelines, interaction oversight, validation checks, and monitoring for emerging problems.
Financial AI in Practice
The protection of personal information through appropriate controls on its collection, use, sharing, retention, and access.
- How it is used
- Constrains credit-scoring data choices while balancing predictive analytics against consumer privacy.
- How deep it goes
- Contrasts regional data constraints and consumer-data monetization, but leaves privacy controls operationally unspecified.
- Examples
- Uses EU restrictions on registry or social-media data and US credit-lead monetization as examples.
- What you can do after
- Evaluate credit-data choices against regional restrictions and consumer-privacy concerns.
Related concepts
- local aipart of · Local AI is artificial-intelligence computation performed by a model on a user's device or other locally controlled infrastructure rather than through a remote service.
- GDPRconfigured by · The General Data Protection Regulation (GDPR) is a European Union regulation governing the processing of personal data and protecting individuals’ rights concerning that data.
- self-hostingaddressed by · Self-hosting is deploying and operating software or services on computing infrastructure controlled or managed by the user or organization.
- transparencycontrasts with · Transparency is the degree to which underlying content is visible through an element or relevant information, processes, and decisions are accessible and understandable to observers.
- utility functioncontrasts with · A utility function maps outcomes or alternatives to numerical values representing their desirability or preference for a decision maker or objective.
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