Compliance
Compliance is conformity with applicable legal, regulatory, contractual, policy, or other governing requirements.
Data-Driven Cybersecurity
Cybersecurity compliance is conformity with applicable security requirements, standards, regulations, or framework controls.
- How it is used
- Uses compliance metrics to demonstrate adherence, support audits, and direct security resources toward gaps.
- How deep it goes
- Works through targets, dashboards, audits, and reporting decisions rather than interpreting individual legal requirements.
- Examples
- Illustrates gaps with healthcare policy dashboards, vendor audits, encryption coverage, and an executive report listing HIPAA, PCI DSS, and GDPR rates.
- What you can do after
- Practice/perform compliance reporting by comparing measured rates with targets, identifying gaps, and recommending actions for leadership.
Effective Platform Engineering
Compliance is adherence to applicable laws, regulations, policies, and prescribed controls.
- How it is used
- Positions compliance as an assurance requirement that a platform must address without recreating developer delivery friction.
- How deep it goes
- Explains responsibilities and stakeholder requirements, then sketches incremental guardrails without specifying control implementations or verification procedures.
- Examples
- Uses critical-vulnerability requirements, SOX deployment evidence, and authorized access to sensitive dataset APIs.
- What you can do after
- Evaluate/understand it by distinguishing platform and application responsibilities and identifying requirements for baseline platform guardrails.
Read how Effective Platform Engineering explains compliance →
Infrastructure as Code, Patterns and Practices: With examples in Python and Terraform
Compliance is adherence of systems, configurations, or operations to applicable policies, standards, regulations, or requirements.
- How it is used
- Uses policy as code to check infrastructure security and compliance before production, after functional testing.
- How deep it goes
- Explains metadata evaluation, test organization, shared repositories, and enforcement categories, but not detailed policy syntax.
- Examples
- Uses GCP infrastructure, database and firewall policies, CIS benchmarks, pytest tests, and shared policy repositories.
- What you can do after
- Apply it via policy engines or pytest to detect noncompliant infrastructure and gate changes before production.
Azure Security
Compliance is the state of satisfying the requirements defined by an applicable policy or rule set.
- How it is used
- Assesses Azure resource configurations against technical controls and regulatory standards to track compliance posture and remediation work.
- How deep it goes
- Explains dashboard-based assessment, failing-control investigation, refresh timing, and policy evaluation without covering manual process-control assessment.
- Examples
- Uses PCI DSS 3.2.1 failing controls and an Allowed virtual machine size SKUs policy assignment with a waiver exemption.
- What you can do after
- Operate/configure continuous compliance assessment by reviewing standards, remediating failed resources, and assigning scoped Azure Policy definitions.
Generative AI in Action
Compliance is conformity with applicable laws, regulations, standards, policies, and contractual requirements.
- How it is used
- Places compliance within production GenAI deployment alongside security, reliability, observability, and lifecycle management.
- How deep it goes
- Details regulatory identification, privacy design, encryption, access control, audits, anomaly detection, and incident response without interpreting individual regulations.
- Examples
- Uses GDPR, HIPAA, PCI-DSS, and CCPA as regulatory examples, with TLS, encryption, RBAC, audits, vulnerability scans, and anomaly detection as controls.
- What you can do after
- Operate/configure GenAI applications with documented privacy, security, audit, monitoring, vendor, and breach-response safeguards.
Architecting an Apache Iceberg Lakehouse
A compliance posture is an organization's current condition of adherence to applicable laws, regulations, policies, and data-handling requirements.
- How it is used
- Guides storage, catalog, access, and consumption architecture when laws, residency rules, privacy, and auditability constrain lakehouse design.
- How deep it goes
- Explains controls across storage and catalog layers, then applies them to a governed healthcare workload without descending into regulatory interpretation.
- Examples
- Uses regulated banking storage, archived regulatory reports, and regional healthcare analytics with dashboards, notebooks, and federated queries.
- What you can do after
- Operate/configure a lakehouse that localizes regulated data, restricts access, protects metadata, preserves audit trails, and supports compliant analytics.
Read how Architecting an Apache Iceberg Lakehouse explains compliance →
Authorization in Action
Compliance is adherence to applicable legal, regulatory, contractual, or organizational requirements.
- How it is used
- Uses authorization policies and governance processes to enforce regulatory requirements and demonstrate that access rules were followed.
- How deep it goes
- Connects regulations to dynamic policies, audit logging, version control, testing, approvals, and review schedules.
- Examples
- Uses HIPAA treatment access, SOX report restrictions, GDPR consent, and ACME policy evidence artifacts as examples.
- What you can do after
- Evaluate access controls by linking obligations to enforceable policies, validation tests, runtime logs, approvals, and review evidence.
Introduction to Generative AI, Second Edition
Compliance is adherence to applicable laws, regulations, policies, standards, or contractual requirements.
- How it is used
- Deploys compliance as a practical use case for AI tools that interpret complex regulations and support legal, regulatory, and tax questions.
- How deep it goes
- Explains AI-assisted determinations and governance safeguards, but stops short of implementation detail.
- Examples
- Uses Norm.ai, Harvey AI, sandbox testing, privacy reviews, consent, and usage monitoring as concrete examples.
- What you can do after
- Operate/configure AI-assisted compliance cautiously through sandbox testing, workplace policies, privacy review, consent, and monitoring.
Read how Introduction to Generative AI, Second Edition explains compliance →
AI for Everyday IT
Compliance is adherence to applicable laws, regulations, standards, policies, or other governing requirements.
- How it is used
- Guides safe AI use in systems administration, database work, data handling, and workplace documentation.
- How deep it goes
- Explains practical checks for policies, retention, protected data, human review, and legal safeguards without detailing legal interpretation.
- Examples
- Uses GDPR, HIPAA, SOC 2, ISO 27001, private AI deployments, configuration audits, and HR document reviews.
- What you can do after
- Operate AI-assisted workflows more safely by checking policies, limiting sensitive data exposure, and escalating legal judgments to people.
Build AI-Enhanced Web Apps
Conformance of a system and its data practices with applicable legal, regulatory, contractual, or organizational requirements.
- How it is used
- Guides production AI web applications toward lawful data handling, privacy protection, content authenticity, and controlled provider usage.
- How deep it goes
- Explains operational safeguards for PII and chat histories, but does not specify implementation code or detailed legal interpretations.
- Examples
- Uses chat histories, PII, financial and medical information, user deletion requests, retention periods, and temporary impersonation review.
- What you can do after
- Operate/configure privacy controls for AI applications by managing consent, storage, access, retention, deletion, and sensitive prompt data.
The Art of AI Product Development
Compliance is adherence to applicable laws, regulations, policies, standards, and organizational requirements.
- How it is used
- Guides AI teams in meeting regulatory, privacy, fairness, security, and organizational requirements while building stakeholder trust.
- How deep it goes
- Details privacy controls, provider audits, jurisdictional checks, monitoring, bias checks, and incident response without specifying legal interpretations.
- Examples
- Uses commercial LLM safeguards, anonymized customer data, bias detection, regional fairness checks, provider audits, and incident response plans.
- What you can do after
- Operate/configure AI initiatives with documented data practices, access controls, audits, fairness checks, monitoring, and clear compliance communication.
Read how The Art of AI Product Development explains compliance →
Financial AI in Practice
Regulatory compliance is adherence to applicable laws, regulations, supervisory requirements, and prescribed financial controls.
- How it is used
- Uses compliance to frame fraud-risk controls and safeguards around financial AI outputs.
- How deep it goes
- Describes AML benefits and output safeguards, but does not detail specific laws or control procedures.
- Examples
- Cites Danske Bank’s AML screening results and compliance filters for generated financial advice.
- What you can do after
- Understand how compliance informs fraud controls and safeguards for generated financial responses.
Related concepts
- versioningaddressed by · The practice of identifying and managing successive revisions of files, data, models, interfaces, or other artifacts so changes can be tracked and earlier states recovered.
- local aiproduced by · 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.
- HIPAAuses · HIPAA, the Health Insurance Portability and Accountability Act, is a United States federal law that includes provisions governing health information privacy and security, insurance portability, and administrative simplification.
- CCPAuses · The California Consumer Privacy Act is a California privacy law establishing consumer rights and business obligations concerning personal information.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.