Governance for Generative AI

Define generative AI and how it creates content—from text to images and audio—using machine learning and generative adversarial networks. Explore applications in content creation, and note copyright, misinformation, and biases.

Discover how large language models predict the next token from context, not real intelligence, through probability-based word choices and simple examples like 'how do you feel'.

Prompts are the inputs you provide to large language models to generate a desired response, using natural language, structured commands, or code cues.

Explore the large language model architecture from user prompts through application services and plugins to downstream databases and websites, highlighting automation agents, external data sources, and cross-layer security.

Explore how ai models train on text, images, speech, or structured data to establish a foundation model, then adapt it for use-case specific tasks like analysis or object recognition.

Define corporate governance as a system of rules, practices, and processes that direct a firm, including cybersecurity and generative AI. Use enterprise risk management to make risks transparent for decision-making.

Explore how enterprise risk management operationalizes corporate governance by identifying, analyzing, prioritizing, and responding to preventable, strategic, and external risks—emphasizing cybersecurity, financial, environmental, and cloud-provider bankruptcy concerns, plus continuous monitoring.

Identify and manage cyber security risks by understanding CIA protections, evaluating threats, vulnerabilities, and impacts, and applying remediation, mitigation, avoidance, transfer, or acceptance, often using a heatmap to prioritize actions.

Highlight the importance of security in generative ai by protecting sensitive data and privacy. Build trust and reliability with robust cyber security, ai systems integrity, and compliant practices.

Identifies prominent threat vectors for GenAI apps, covering prompt injection, data leakage, overreliance, data poisoning, supply chain risks, insecure plugins, and jailbreaks across AI data, usage, application, and platform.

Explore the Open Worldwide Application Security Project, known for the top ten risks for web apps, APIs, and large language models, plus ZAP and the Web Security Testing Guide.

Explore the OWASP top ten for llms, from prompt injection to model theft, and learn how insecure outputs, data poisoning, and supply chain risks threaten governance of generative AI.

Show how adversaries leverage AI to fuel misinformation, create fake images and news, and craft highly personalized phishing at scale, including impersonation and SEO fraud.

Explore the shared responsibility model for AI in the Microsoft cloud, clarifying customer versus Microsoft duties across iaas, paas, and saas offerings.

Explore how Microsoft defines responsible artificial intelligence in governance for generative artificial intelligence, focusing on fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability.

Assess inherent large language model risks, from imaginative yet unreliable outputs to suggestible, exploitable behavior, and cybersecurity threats like data exfiltration and prompt injections.

Explain transparency and accountability in generative AI, tackling opaque decision making, auditability, error attribution, trust risks, and legal and ethical concerns to build trusted, compliant systems.

navigate the evolving regulatory landscape for generative ai by addressing global inconsistencies, sector-specific rules, and data protection laws, while ensuring thorough reporting and audit readiness.

Explore the risks of AI hallucinations, including factually incorrect outputs, overreliance, and impacts on decision making in critical fields, and the role of data quality and monitoring in mitigating them.

Explore how bias and discrimination arise in general ai systems, how training data amplifies existing biases, and the resulting discriminatory decisions and legal risks.

Explore the risks of copyright infringement and intellectual property violations in generative ai, including unauthorized use of materials, legal liabilities, model rights, economic impact on creators, and liability complexity.

Learn how a cyber security professional at a car manufacturer builds a governance program for generative AI to govern AI-enabled business processes.

Establish a GenAI governance committee by including cross-functional leaders from IT, legal, compliance, HR, and business units; define roles, schedule regular meetings, engage diverse stakeholders, and track governance KPIs.

Develop comprehensive governance policies with the Chennai committee, covering ethical standards, data security and handling, compliance checklists, transparency, and regular updates of regulations and tech changes.

Develop robust risk management for generative AI by identifying, assessing, and prioritizing risks. Conduct vendor and third-party AI service assessments, implement monitoring, mitigation, and crisis response plans.

Develop role-specific training and ongoing awareness to support AI governance, covering cybersecurity risks, workshops, e-learning delivery, certifications, and continuous updates based on feedback.

Select relevant international or industry AI ethics frameworks, such as Microsoft Responsible AI, and embed them into governance policies, train stakeholders, and conduct audits to ensure adherence for generative AI.

Enhance monitoring and feedback for generative AI by implementing monitoring tools, real-time alerts for anomalies, user surveys, analytics, and governance committee reviews to drive actionable improvements.

Define data and identity governance for AI with proactive data lifecycle management, data quality checks, role-based access, least privilege, advanced identity verification tools, and compliance audits.

Implement continuous improvement and adoption of the governance program by leveraging feedback loops, monitoring, and technology watch to rapidly adapt policies for generative AI through pilot tests and stakeholder forums.

Learn how the Azure resource hierarchy uses management groups, subscriptions, and resource groups to cluster resources by lifecycle, location, department, or criticality, enabling governance and cost control.

Explore Azure subscription types, including free, student, pay-as-you-go, and enterprise agreement, with emphasis on free and student options for demos in this governance for generative ai course.

Discover how Entra ID tenants host identities that access Azure resources in subscriptions and resource groups, clarifying that subscriptions are not tenants.

Learn how to create a free Azure subscription by choosing between free and pay as you go and providing personal details. Log in at portal.azure.com to start building in Azure.

Define guardrails for azure resources with json policy definitions and policy initiatives. Use policy effects like audit and deny to enforce defender for cloud and servers.

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