AI Security and Risk Management
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Learn to identify AI security risks, assess them with frameworks such as the EU AI Act, and tie security work to an organization’s business goals.
Artificial intelligence brings security risks of its own, and organizations need a clear way to recognize them, weigh them and respond. You examine the risks that arise when models are built and used, the threats that reach an organization from outside, and the development practices and assessment steps that reduce them. Later lessons connect this work to business strategy, risk appetite and a culture in which people across the organization help identify risks.
What You Will Achieve
- Explain how AI can strengthen security operations while also introducing new risks to them
- Tell apart threats that arise inside a model from threats outside it, and pick mitigating practices for each
- Carry out a structured AI risk evaluation and set priorities among the threats it finds
- Present AI security work in terms of the business value it protects and the goals of the organization
Program Overview
The AI Security Landscape and Its Stakeholders
Sets out the risks particular to AI, the double role AI plays in security work, and why the wider ecosystem calls for several viewpoints.
- Security risks specific to AI
- AI as a change agent in security operations
- The AI security ecosystem and balancing stakeholder interests
Risk Management Fundamentals for AI Models
Covers internal model risks and external threats, then the development practices and frameworks used to reduce and assess them.
- Common model risks and continuous monitoring
- Types of external risks and how to keep up with them
- Secure development and secure access
- Risk assessment frameworks, assessment steps and the EU AI Act
Strategic Integration and a Security-Aware Culture
Ties security to business goals and risk appetite, and looks at the shared habits that keep an organization current on AI risks.
- Risk appetite and balancing innovation with security
- Aligning security with organizational goals, with a health care and social media comparison
- Responsibility for identifying risks, including the data science team
- Staying up to date as AI risks change
Certificate Awarded (Paid)
Job Outlook
These skills apply wherever organizations adopt AI and need to govern its risks, including security and risk teams, data science groups, and leaders who plan how AI fits an organization’s strategy.
Good for: Managers and professionals in any role who need a working grasp of AI risks to make better security and strategy decisions for their organization.
Keep in mind: It is a theory course built from short exercises, so it explains risks, frameworks and practices without having you secure a real AI system.
Bottom line: A strategy-minded look at AI risk that links model and external threats, assessment frameworks and security culture back to business goals.
Frequently Asked Questions
Does the course cover the EU AI Act?
Yes. The EU AI Act appears among the AI risk assessment frameworks in the risk management chapter, next to the steps for carrying out an assessment and ranking the threats it uncovers.
What kind of exercises does the course use?
It is a theory course: short interactive exercises check your grasp of each chapter's ideas, such as common model risks, the steps of a risk assessment, risk appetite and balancing innovation with security.
Does the course address who is responsible for spotting AI risks?
Yes. The final chapter on a security-aware culture looks at who is responsible for identifying risks and the part the data science team plays, and stresses that keeping AI secure depends on many people.
What real-world examples does the course draw on?
One is a comparison of health care and social media, used in the material on connecting security with business goals to show how organizational aims affect the way AI security is approached.
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