Securing AI Applications: From Threats to Controls

Post published:8 December, 2025
Post category:StudyBullet-22
Reading time:6 mins read

Learn how to defend generative AI systems using firewalls, SPM, and data governance tools
⏱️ Length: 6.1 total hours
👥 8 students

Add-On Information:

Get Instant Notification of New Courses on our Telegram channel.

Note➛ Make sure your 𝐔𝐝𝐞𝐦𝐲 cart has only this course you're going to enroll it now, Remove all other courses from the 𝐔𝐝𝐞𝐦𝐲 cart before Enrolling!

Course Overview
- This comprehensive course, “Securing AI Applications: From Threats to Controls,” equips participants with the essential knowledge and practical skills to build robust security postures for artificial intelligence systems, with a particular emphasis on generative AI. In an era where AI is rapidly integrated into various business processes, understanding and mitigating its unique security vulnerabilities is paramount. The curriculum moves beyond theoretical concepts to provide actionable strategies for protecting AI models, the data they interact with, and the tools they leverage. Participants will gain a deep understanding of the expanded attack surface introduced by AI technologies and learn to construct end-to-end security architectures that address these challenges. The course fosters a proactive approach to AI security by enabling learners to develop realistic threat scenarios and implement effective safeguards. It delves into the operationalization of security controls, including the deployment of guardrails, policy engines, and secure integration into AI development lifecycles. Furthermore, the course emphasizes the critical aspects of data protection within AI pipelines, particularly for Retrieval Augmented Generation (RAG) systems, and explores the use of AI Security Posture Management (SPM) tools for continuous monitoring and compliance. By the end of this program, participants will be well-prepared to design, implement, and manage comprehensive AI security strategies within their organizations.
Target Audience
- This course is designed for a diverse group of professionals involved in the development, deployment, and governance of AI applications. It is highly relevant for AI Engineers, Machine Learning Engineers, Data Scientists, Security Architects, Cloud Security Engineers, DevOps Engineers, IT Managers, and anyone responsible for the security and compliance of AI initiatives within an organization. While a foundational understanding of AI concepts is beneficial, the course is structured to be accessible to those who may not be AI experts but are tasked with securing these systems.
Key Learning Objectives (Unique to this description)
- Deconstruct AI Attack Vectors: Understand the novel and evolving threats that target the unique components of AI systems, including adversarial attacks on model integrity, data poisoning, and exploitation of prompt injection vulnerabilities.
- Architect for Resilience: Learn to design and implement secure AI architectures that incorporate defense-in-depth strategies, ensuring that each layer of the AI system, from data ingress to model inference, is protected.
- Proactive Threat Modeling: Develop the ability to anticipate and model potential attack scenarios specifically tailored to generative AI applications, enabling the proactive identification of weaknesses.
- Operationalize AI Safeguards: Master the practical application of security controls such as input validation, output filtering, and ethical AI guardrails to prevent misuse and maintain system integrity.
- Secure AI Development Lifecycle: Integrate security checkpoints and assessments seamlessly into the AI development and deployment pipelines, ensuring that security is a continuous consideration.
- Manage AI Access and Permissions: Implement robust authentication, authorization, and access control mechanisms to govern who can interact with AI models and what resources they can access.
- Protect AI Data Integrity: Apply advanced data protection techniques to AI data pipelines, ensuring the confidentiality, integrity, and availability of sensitive information used by AI models.
- Leverage AI Security Posture Management: Utilize specialized tools to gain visibility into AI assets, detect security misconfigurations, and monitor for deviations from baseline security configurations.
- Establish AI Monitoring Frameworks: Design and deploy comprehensive monitoring solutions to track AI system activity, identify anomalous behavior, and gather essential metrics for security analysis.
- Develop an Organizational AI Security Roadmap: Create actionable plans for the widespread adoption and implementation of AI security controls across an organization, ensuring scalability and sustainability.
Skills Covered / Tools Used
- Threat Analysis and Risk Assessment: Identifying and evaluating potential security threats to AI applications.
- AI Security Architecture Design: Planning and structuring secure AI systems.
- Adversarial AI Defense Techniques: Implementing strategies against sophisticated AI attacks.
- Prompt Engineering for Security: Designing secure prompts and understanding prompt injection.
- Data Security and Privacy in AI: Implementing controls for sensitive data.
- Access Control and Authentication for AI Services: Managing user and system access.
- AI Policy and Governance Implementation: Enforcing security policies within AI systems.
- Security Orchestration and Automation (SOAR) for AI: Automating security responses.
- Cloud Security Best Practices for AI: Securing AI workloads in cloud environments.
- AI Security Posture Management (SPM) Tools: Understanding and using tools for monitoring and compliance.
- Security Information and Event Management (SIEM) Integration for AI: Correlating AI security events.
- Container Security for AI Deployments: Securing AI applications deployed in containers.
- API Security for AI Services: Protecting the interfaces of AI applications.
Benefits / Outcomes
- Enhanced AI System Security: Develop the expertise to significantly strengthen the security of AI applications, reducing vulnerabilities.
- Proactive Risk Mitigation: Gain the ability to identify and address potential AI security risks before they can be exploited.
- Improved Compliance and Governance: Establish robust frameworks for ensuring AI applications meet regulatory and organizational security standards.
- Increased Confidence in AI Deployments: Foster trust in AI technologies by demonstrating a commitment to their secure operation.
- Career Advancement: Acquire in-demand skills that are critical in the rapidly evolving field of AI security, opening new career opportunities.
- Strategic AI Security Planning: Become capable of developing and implementing effective, long-term AI security strategies for your organization.
- Reduced Likelihood of Security Incidents: Minimize the probability of costly data breaches, service disruptions, and reputational damage related to AI security failures.
- Efficient Security Operations: Streamline the process of securing AI applications through the adoption of best practices and appropriate tools.
PROS
- Highly Relevant and Timely: Addresses a critical and rapidly growing area of cybersecurity.
- Practical, Actionable Skills: Focuses on hands-on implementation and real-world application.
- Comprehensive Scope: Covers a wide range of threats and control mechanisms for AI.
- Focus on Generative AI: Specializes in the security of cutting-edge AI technologies.
- Structured Learning Path: Provides a clear framework from understanding threats to implementing controls.
CONS
- Requires Some Technical Aptitude: Participants will benefit from a basic understanding of AI concepts and IT security principles.