[{"data":1,"prerenderedAt":75},["ShallowReactive",2],{"training:yapay-zeka-guvenligi-ai-security:en":3},{"id":4,"title":5,"slug":6,"summary":7,"cover_image_url":8,"category":9,"duration_hours":15,"level":16,"module_count":17,"description":18,"outline":19,"faq":58,"updated_at":74},135,"AI Security","yapay-zeka-guvenligi-ai-security","Artificial intelligence technologies have become central to decision-making processes across numerous sectors, from customer service to finance, healthcare, manufacturing, and public services. With the widespread adoption of large language models, automated decision systems, and machine learning-based applications, new types of threats targeting these systems—such as prompt injection, data poisoning, model theft, and adversarial attacks—are rapidly increasing. In this domain where traditional cybersecurity approaches fall short, it is no longer a matter of choice but a necessity for business continuity and reputation management for organizations to secure their AI systems from the design stage onward.\n\nThe AI Security training equips participants with the skills to recognize threat models specific to AI systems, analyze attack scenarios, and implement defense mechanisms. For corporate organizations, this training creates a competitive advantage by enabling early detection of security vulnerabilities in AI projects, preventing data breaches and regulatory penalties, and supporting the development of compliant and resilient AI solutions that build trust among customers and business partners.","https:\u002F\u002Facademyclub.b-cdn.net\u002Ftrainings\u002F173589c70cf74a47b9015db38e11de7a.webp",{"id":10,"name":11,"slug":12,"icon":13,"order":14},2,"Cybersecurity","siber-guvenlik","pi-shield",1,null,"",4,"\u003Ch3>Training Content and Sample Curriculum\u003C\u002Fh3>\u003Cp>In this training, participants learn about current threats targeting artificial intelligence systems and the technical and managerial measures that can be taken against these threats through hands-on examples.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>AI Threat Modeling and Attack Surface:\u003C\u002Fstrong> Analysis of risk areas and potential attack vectors that differentiate AI\u002FML systems from traditional software.\u003C\u002Fli>\u003Cli>\u003Cstrong>Attacks Targeting Large Language Models:\u003C\u002Fstrong> Defense strategies against prompt injection, jailbreak techniques, and manipulation of LLM-based applications.\u003C\u002Fli>\u003Cli>\u003Cstrong>Data Leakage and Model Reverse Engineering:\u003C\u002Fstrong> Risks of exposing sensitive training data through techniques such as model inversion and membership inference, along with prevention methods.\u003C\u002Fli>\u003Cli>\u003Cstrong>Poisoning Attacks and Training Data Security:\u003C\u002Fstrong> Detection of data poisoning and model poisoning scenarios, and ensuring data integrity.\u003C\u002Fli>\u003Cli>\u003Cstrong>Adversarial Examples and Model Manipulation:\u003C\u002Fstrong> Mechanisms of image, audio, and text-based adversarial attacks, and resilience testing.\u003C\u002Fli>\u003Cli>\u003Cstrong>Governance, Compliance, and Regulations:\u003C\u002Fstrong> Audit and compliance requirements for AI systems under the EU AI Act, ISO\u002FIEC 42001, and the Turkish Personal Data Protection Law (KVKK).\u003C\u002Fli>\u003Cli>\u003Cstrong>Secure AI Architecture and MLSecOps Practices:\u003C\u002Fstrong> Establishing a secure model development lifecycle, along with monitoring, logging, and incident response processes.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>The curriculum for all our training programs is customized to fit your organization's needs; please contact us to discuss your requirements. The information provided here has been prepared as an example based on our previous training sessions.\u003C\u002Fp>\u003Ch3>Target Audience\u003C\u002Fh3>\u003Cul>\u003Cli>Cybersecurity experts and SOC analysts\u003C\u002Fli>\u003Cli>Software developers and machine learning engineers\u003C\u002Fli>\u003Cli>Information security managers and CISOs\u003C\u002Fli>\u003Cli>Data scientists and AI\u002FML project managers\u003C\u002Fli>\u003Cli>Internal audit and compliance specialists\u003C\u002Fli>\u003Cli>Corporate risk management and IT infrastructure teams\u003C\u002Fli>\u003C\u002Ful>\u003Ch3>Benefits to Your Organization\u003C\u002Fh3>\u003Cul>\u003Cli>Early detection of security vulnerabilities in AI systems\u003C\u002Fli>\u003Cli>Reduced risk of data leaks, model theft, and reputational damage\u003C\u002Fli>\u003Cli>Strengthened compliance with regulations such as the EU AI Act, KVKK, and ISO 42001\u003C\u002Fli>\u003Cli>Institutionalization of secure AI development processes (MLSecOps)\u003C\u002Fli>\u003Cli>Faster response to cybersecurity incidents\u003C\u002Fli>\u003Cli>Enhanced trust and competitive advantage among customers and business partners\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Artificial intelligence has become an integral part of corporate strategy; harnessing this power safely and sustainably is only possible with the right knowledge and skills. Join our AI Security training to prepare your team for the threats of the future and confidently shape your organization's AI transformation.\u003C\u002Fp>",[20,31,38,48],{"title":21,"lessons":22,"duration":16},"Overview of AI Security",[23,24,25,26,27,28,29,30],"Narrow AI and Artificial General Intelligence","Why Is AI Called a Black Box?","Is It Possible to Understand and Interpret the AI Black Box?","Can AI Be Hacked?","Can AI Hack?","Can Large Language Models (LLMs) Like ChatGPT, Gemini, and DeepSeek Be Hacked?","Can Autonomous Systems Like Driverless Cars and Factories Be Hacked?","A Conceptual Look at AI and Its Security",{"title":32,"lessons":33,"duration":16},"Machine Learning Security Frameworks and Guidelines",[34,35,36,37],"Google Secure AI Framework","AI Risk Management Framework","OWASP Machine Learning Security Top 10","OWASP Top 10 for Large Language Model",{"title":39,"lessons":40,"duration":16},"Machine Learning Security",[41,42,43,44,45,46,47],"Overview of AI Security Vulnerabilities","Input Manipulation Attack","Data Poisoning Attack","Model Inversion Attack","AI Supply Chain Attacks","Output Integrity Attack","Key Resources for Machine Learning Security",{"title":49,"lessons":50,"duration":16},"Large Language Model (LLM) Security",[51,52,53,54,55,56,57],"Overview of Current Large Language Models","Security and Impact of Large Language Models","Prompt Injection","Data and Model Poisoning","Vector and Embedding Weaknesses","Unbounded Consumption","Key Resources for Large Language Model Security",[59,62,65,68,71],{"answer":60,"question":61},"In this training, we cover current threats targeting artificial intelligence systems in education, along with practical measures to counter them. The content includes topics such as AI threat modeling and attack surfaces, prompt injection and jailbreak attacks against large language models, model reverse engineering and data leakage, poisoning attacks, adversarial examples, and model manipulation. We also address governance and compliance matters under the EU AI Act, ISO\u002FIEC 42001, and the Turkish Personal Data Protection Law (KVKK), as well as secure AI architecture and MLSecOps practices.","What topics are covered in the Artificial Intelligence Security training?",{"answer":63,"question":64},"This training is suitable for cybersecurity experts and SOC analysts, software developers, and machine learning engineers. It also benefits information security managers and CISOs, data scientists and AI\u002FML project managers, internal audit and compliance specialists, as well as enterprise risk management and IT infrastructure teams.","Who is the Artificial Intelligence Security training suitable for?",{"answer":66,"question":67},"The training enables early detection of security vulnerabilities in artificial intelligence systems, reducing the risks of data leakage, model theft, and reputational damage. It strengthens compliance with regulations such as the EU AI Act, KVKK, and ISO 42001, and supports the institutionalization of secure AI development processes (MLSecOps). It also accelerates incident response in cybersecurity events, building credibility and competitive advantage in the eyes of customers and business partners.","What benefits does the Artificial Intelligence Security training provide to an organization?",{"answer":69,"question":70},"Participants gain the ability to recognize threat models specific to AI systems, analyze attack scenarios, and implement defense mechanisms. This enables organizations to detect security vulnerabilities in AI projects at an early stage and prevent data breaches and regulatory penalties. Participants also gain the capacity to develop compliant and resilient artificial intelligence solutions that build trust with customers and business partners.","What do participants gain at the end of the Artificial Intelligence Security training?",{"answer":72,"question":73},"Artificial intelligence technologies have become central to decision-making processes across many sectors, from customer service to finance, healthcare, manufacturing, and public services. While threats targeting these systems—such as prompt injection, data poisoning, model theft, and adversarial attacks—are rapidly increasing, traditional cybersecurity approaches fall short in this domain. For this reason, making AI systems secure from the design stage onward has become essential for organizations in terms of business continuity and reputation management.","Why is artificial intelligence security important for organizations?","2026-10-12T14:27:02.936871+03:00",1792015376847]