Generative AI and LLM Security from Coursera

Building on these fundamentals, you’ll gain hands-on experience in securing LLM applications, aligning model outputs to security objectives, and applying guardrails, watermarking, and safety evaluation methods. You’ll also work with API integrations using platforms like Gemini API and Google Colab to simulate secure deployment practices and mitigate risks in live systems.

Next, the program delves into AI lifecycle security, covering strategies to secure training data, prevent poisoning attacks, and protect AI pipelines. You’ll explore model provenance, dependency scanning, and secure deployment pipelines—ensuring the integrity of AI systems across their entire supply chain.

The course also emphasizes AI ethics and compliance, including bias detection, fairness in model design, and global regulatory frameworks like GDPR, CCPA, NIST AI RMF, ISO standards, and the EU AI Act. Using tools like Sola Security, you’ll practice auditing, governance, and risk management to operationalize ethical and compliant AI practices.

Finally, you’ll examine frontier threats in emerging domains such as multimodal AI and Agentic AI, exploring adversarial attacks, cross-modal vulnerabilities, and their implications for enterprise cybersecurity.

By the end of this program, you will be able to:

- Identify and evaluate attack vectors targeting GenAI and LLMs.

- Apply secure prompt engineering and defense strategies against prompt injection and jailbreaks.

- Design and implement guardrails, safety mechanisms, and watermarking in LLM applications.

- Protect AI training data, pipelines, and deployment workflows from poisoning and supply chain risks.

- Assess and enforce regulatory compliance with GDPR, CCPA, NIST, ISO, and the EU AI Act.

- Recognize and mitigate frontier threats in multimodal and agentic AI systems.

- Integrate ethical, transparent, and resilient security practices across the AI lifecycle.

This specialization is designed for cybersecurity engineers, LLM developers, AI security specialists, ML engineers, and cloud/edge security architects who want to build advanced expertise in safeguarding GenAI systems.

Join us to gain the skills, tools, and strategies required to secure next-generation AI systems against evolving adversarial threats.

What's inside

Syllabus

Threats in Generative AI Systems

Uncover the vulnerabilities of Generative AI systems by examining common attack vectors such as prompt injection, jailbreaks, and model theft. Learn how adversaries exploit weaknesses, explore mitigation strategies, and gain hands-on practice in detecting and responding to real-world GenAI risks.

Career center

Learners who complete Generative AI and LLM Security will develop knowledge and skills that may be useful to these careers:

AI Security Engineer

An AI Security Engineer designs, implements, and maintains robust security measures for artificial intelligence systems, including Generative AI and Large Language Models. This role is crucial for identifying vulnerabilities, developing defensive strategies, and responding to cyber threats targeting AI. This program explicitly equips you to identify, analyze, and mitigate vulnerabilities in GenAI and LLMs, examining common attack vectors like prompt injection and model theft. You will gain hands-on experience in securing LLM applications, designing guardrails, and protecting AI training data and deployment pipelines from poisoning attacks. The course’s focus on applying secure prompt engineering, aligning model outputs to security objectives, and integrating ethical and compliant AI practices makes it invaluable for aspiring AI Security Engineers. Learning to detect and respond to real-world GenAI risks and understanding frontier threats will ensure you are prepared for the evolving landscape of AI cybersecurity.

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LLM Security Specialist

An LLM Security Specialist focuses specifically on safeguarding Large Language Models against various threats, ensuring their ethical and secure operation. This role involves deep dives into LLM-specific vulnerabilities and developing countermeasures. This course directly addresses the core competencies for an LLM Security Specialist, beginning with exploring GenAI threats and attack vectors such as prompt injection and jailbreaks. You will gain practical, hands-on experience in securing LLM applications, implementing guardrails, watermarking, and safety evaluation methods. The program’s emphasis on aligning model outputs to security objectives and mitigating risks in live systems using API integrations, like Gemini API, provides targeted skills for this role. Understanding how to protect AI pipelines and enforce regulatory compliance like the EU AI Act further solidifies your expertise in building resilient and trustworthy LLM systems.

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AI Auditor

An AI Auditor independently assesses AI systems for compliance with ethical guidelines, regulatory requirements, and internal security standards. This role provides assurance that AI models are fair, transparent, and secure. This program is exceptionally beneficial for an AI Auditor. Its dedicated module on AI Ethics and Regulatory Compliance is central, teaching you to identify ethical risks, address bias and fairness challenges, and implement transparency. You will gain hands-on experience with compliance frameworks, auditing practices, and tools like Sola Security to ensure AI-driven systems are responsible, transparent, and legally compliant. Furthermore, the course equips you to identify and evaluate attack vectors targeting GenAI and LLMs, understand AI lifecycle security, and recognize frontier threats. This comprehensive understanding allows an AI Auditor to perform thorough assessments of both the security posture and the ethical governance of AI systems across their entire lifecycle, ensuring robust and defensible AI deployments.

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AI Risk and Compliance Manager

An AI Risk and Compliance Manager oversees the identification, assessment, and mitigation of risks associated with AI systems, ensuring adherence to ethical guidelines and regulatory standards. This includes frameworks like GDPR and the EU AI Act. This program offers a comprehensive foundation for an AI Risk and Compliance Manager, particularly through its dedicated module on AI Ethics and Regulatory Compliance. You will learn to identify ethical risks, address bias and fairness in model design, and implement transparency. The course covers assessing and enforcing regulatory compliance with global frameworks such as GDPR, CCPA, NIST AI RMF, ISO standards, and the EU AI Act. Practical experience with tools like Sola Security for auditing, governance, and risk management is directly applicable. This course helps operationalize ethical and compliant AI practices, preparing you to manage complex compliance landscapes and ensure responsible AI deployment.

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Regulatory Compliance Officer AI Systems

A Regulatory Compliance Officer AI Systems ensures an organization's AI initiatives meet stringent legal and ethical standards, translating complex regulations into actionable policies and overseeing their implementation across AI systems. This role often requires an advanced degree. This program is exceptionally relevant for a Regulatory Compliance Officer AI Systems, with a core module dedicated to AI Ethics and Regulatory Compliance. You will specifically learn to assess and enforce regulatory compliance with global frameworks such as GDPR, CCPA, NIST AI RMF, ISO standards, and the EU AI Act. The course provides hands-on experience with compliance frameworks, auditing practices, and tools like Sola Security. This expertise enables you to operationalize ethical and compliant AI practices within an enterprise. Understanding bias detection, fairness in model design, and broader AI lifecycle security also provides the necessary context to develop and maintain robust, legally sound compliance programs for all AI-driven operations.

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Cybersecurity Consultant AI Security

A Cybersecurity Consultant AI Security advises organizations on best practices for securing their AI systems, conducting assessments, recommending strategies, and helping implement solutions to mitigate risks in Generative AI and LLMs. This role requires broad expertise. This program is highly valuable for a Cybersecurity Consultant AI Security, as it equips you with comprehensive expertise to identify, analyze, and mitigate vulnerabilities in GenAI and LLMs. You will gain a deep understanding of common attack vectors like prompt injection and model theft, and how to design and implement guardrails and safety mechanisms. The course covers AI lifecycle security, protecting training data and pipelines, and assessing regulatory compliance with frameworks like GDPR, NIST AI RMF, and the EU AI Act. This holistic view, combined with insights into frontier threats in multimodal AI, enables you to provide well-rounded, strategic advice to clients, helping them integrate ethical, transparent, and resilient security practices across their AI initiatives.

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Product Security Engineer AI Systems

A Product Security Engineer AI Systems embeds security principles throughout the development lifecycle of AI-driven products, ensuring they are designed with robust defenses against potential threats. This proactive role prevents vulnerabilities from reaching production. This program helps build a foundation for a Product Security Engineer AI Systems by focusing on securing LLM applications from design to deployment. You will gain hands-on experience in implementing guardrails, watermarking, and safety evaluation methods, and aligning model outputs to security objectives. The course’s emphasis on AI lifecycle security, covering strategies to secure training data, prevent poisoning attacks, and protect AI pipelines, ensures product integrity. Additionally, understanding dependency scanning and secure deployment practices helps embed security early. This course enables you to integrate ethical, transparent, and resilient security practices across the AI lifecycle, which is vital for developing secure and compliant AI products.

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DevSecOps Engineer AI Pipelines

A DevSecOps Engineer AI Pipelines integrates security practices into every stage of the AI development and operations pipeline, automating security controls to ensure continuous protection of AI systems. This role bridges development, security, and operations. This program is highly relevant for a DevSecOps Engineer AI Pipelines, offering deep insights into AI lifecycle security. You will learn strategies to secure training data, prevent poisoning attacks, and protect AI pipelines, ensuring the integrity of AI systems across their entire supply chain. The course covers model provenance, dependency scanning, and securing deployment pipelines. Practical demonstrations on how defenders can detect and respond to risks, combined with hands-on experience in applying guardrails in LLM applications, directly feed into DevSecOps practices. By understanding ethical and compliant AI operations, you can build automated security checks that align with both technical and regulatory requirements in AI system deployments.

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Machine Learning Security Researcher

A Machine Learning Security Researcher investigates novel vulnerabilities in AI and machine learning models, developing advanced defensive techniques and contributing to the theoretical understanding of AI security. This role often requires an advanced degree. This course is highly relevant for a Machine Learning Security Researcher, as it delves into the foundations of GenAI threats and common attack vectors like adversarial manipulation. You will explore how attackers exploit weaknesses in AI-driven systems and gain insight into detecting and responding to these risks. The program's focus on frontier threats in multimodal and Agentic AI, including adversarial attacks and cross-modal vulnerabilities, directly supports research into cutting-edge security challenges. Understanding how to protect AI training data, prevent poisoning attacks, and ensure supply chain integrity provides a robust foundation for identifying new research areas and developing innovative security solutions for AI systems.

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Cyber Threat Intelligence Analyst AI Focus

A Cyber Threat Intelligence Analyst AI Focus gathers and analyzes information on emerging threats, tactics, and procedures used against AI systems, providing actionable intelligence to defenders. This role requires understanding adversarial motivations and capabilities. This course is highly beneficial for a Cyber Threat Intelligence Analyst AI Focus, providing a deep dive into the foundations of GenAI threats and common attack vectors such as prompt injection, jailbreaks, and model theft. You will learn how attackers exploit weaknesses in AI-driven systems and how to detect and respond to these risks in real-world environments. The program’s exploration of frontier threats in emerging domains like multimodal AI and Agentic AI, including adversarial attacks and cross-modal vulnerabilities, is crucial for anticipating future threats. Understanding AI lifecycle security and model provenance helps contextualize attack surfaces, enabling more effective intelligence gathering and strategic defense planning against evolving adversarial threats.

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AI Development Engineer with Security Expertise

An AI Development Engineer with Security Expertise builds and deploys AI models, particularly Generative AI and LLMs, while integrating security best practices directly into the development process. This role ensures secure by design principles. This program provides crucial expertise for an AI Development Engineer with Security Expertise. It offers hands-on experience in securing LLM applications, aligning model outputs to security objectives, and applying guardrails, watermarking, and safety evaluation methods. You will learn to protect AI training data, prevent poisoning attacks, and safeguard AI pipelines from supply chain risks. The course's practical application of secure deployment practices, including API integrations, is directly relevant to development workflows. By understanding how to identify and mitigate vulnerabilities and adhere to ethical and regulatory compliance, you can proactively design, implement, and deploy AI systems that are inherently more resilient and trustworthy, integrating security from the very start of the development lifecycle.

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Data Ethics and Governance Specialist

A Data Ethics and Governance Specialist ensures that data practices, especially concerning AI, adhere to ethical principles and regulatory requirements. This role focuses on responsible data handling, privacy, and fairness. This course may be very useful for a Data Ethics and Governance Specialist, especially through its substantial module on AI Ethics and Regulatory Compliance. You will learn to identify ethical risks, address bias detection and fairness in model design, and implement transparency and accountability in AI workflows. The program covers assessing and enforcing regulatory compliance with key frameworks such as GDPR, CCPA, NIST AI RMF, ISO standards, and the EU AI Act. Using tools like Sola Security for auditing and governance practices provides practical experience. While the course extends beyond data *per se* to cover model security, the foundational understanding of ethical AI operations and compliance is directly applicable, helping you build responsible, transparent, and legally compliant AI-driven data systems.

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Cloud Security Architect AI Focus

A Cloud Security Architect AI Focus designs and oversees the security infrastructure for AI systems deployed in cloud environments, ensuring robust protection against threats specific to both cloud platforms and AI technologies. This role is critical for large-scale AI operations. This course may be particularly helpful for a Cloud Security Architect AI Focus by providing expertise in identifying, analyzing, and mitigating vulnerabilities in GenAI and LLMs. While not exclusively cloud-focused, the program emphasizes secure deployment practices and mitigating risks in live systems, which often occur in cloud environments. Understanding AI lifecycle security, including protecting AI pipelines and ensuring supply chain integrity, is vital when designing cloud-native AI architectures. The discussion of global regulatory frameworks like NIST AI RMF and ISO standards also applies to cloud deployments, helping architects design compliant and resilient AI security strategies. This knowledge directly enhances the ability to secure next-generation AI systems against evolving adversarial threats within cloud infrastructures.

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Security Operations Center Analyst AI Threats

A Security Operations Center Analyst AI Threats monitors, detects, and responds to security incidents specifically involving AI systems. This role requires rapid identification of adversarial activities affecting Generative AI and Large Language Models. This course may be helpful for a Security Operations Center Analyst AI Threats as it equips you to identify and evaluate attack vectors targeting GenAI and LLMs, such as prompt injection and jailbreaks. You will learn how attackers exploit weaknesses in AI-driven systems and gain practical demonstrations on how defenders can detect and respond to these risks in real-world environments. The program’s emphasis on understanding frontier threats in multimodal and Agentic AI, and exploring adversarial attacks, provides critical knowledge for anticipating and triaging AI-specific incidents. By understanding the full spectrum of GenAI vulnerabilities and mitigation strategies, you can more effectively monitor AI systems, analyze alerts, and implement timely responses to safeguard enterprise AI deployments from evolving threats.

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Digital Forensics Investigator AI Incidents

A Digital Forensics Investigator AI Incidents specializes in analyzing security breaches and incidents involving AI systems, collecting evidence, and reconstructing attack flows to understand compromised Generative AI and LLMs. This role is critical for post-incident analysis. This course may be useful for a Digital Forensics Investigator AI Incidents by providing a deep understanding of GenAI threats and common attack vectors such as prompt injection, jailbreaks, and model theft. You will learn how attackers exploit weaknesses in AI-driven systems and how defenders detect and respond to these risks. This knowledge is foundational for forensic analysis by helping to identify indicators of compromise specific to AI. Understanding AI lifecycle security, including data poisoning and supply chain risks, also aids in tracing the origin of attacks. While not a direct forensics course, the comprehensive insight into AI vulnerabilities will enhance your ability to investigate and attribute incidents in the complex landscape of AI-driven cybersecurity.

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