Mass adoption of any new technology across different industries is likely to raise security concerns. Malicious actors have spared no effort to explore every opportunity to exploit artificial intelligence systems. Businesses need to think about AI security in the age of generational AI, as attackers can surprisingly leverage generative AI itself to break into even the most secure AI systems. Understanding the security risks of Generation AI has become more important than ever.
Generative AI has become one of the most important technologies having a transformative impact on the way businesses operate and view security. You could find at least one in three organizations using generative AI in a business function. AI generation not only improves productivity and efficiency, but also introduces a wide range of security challenges. Organizations need to think about AI security for models, data, and their users in the era of generative AI.
Assessing the Scope of AI Security Risks in the Generation AI Era
The spontaneous growth of large-scale adoption of generative AI has introduced many new attack vectors that you cannot manage with conventional security measures. A SoSafe report on cybercrime trends in 2025 suggests that more than 90% of security experts expect an increase in AI-based attacks over the next three years (Source). The use of AI in security systems may seem a promising solution to strengthen protections against emerging threats. However, the numbers tell a completely different story about the impact of generative AI on security.
Gartner highlighted that more than 40% of AI-related data breaches will occur due to inappropriate use of generative AI, by 2027 (Source). A 2024 survey of global business and cybersecurity leaders found that nearly half of respondents believed generative AI would drive the growth of adversarial capabilities (Source). The survey also showed that some experts believed that Generation AI could be responsible for the disclosure of sensitive information and data leaks.
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Understand how generative AI increases security risks
Anyone interested in measuring the impact of generative AI on security would obviously look for the most notable security risks attributed to gen AI. Rather, they should seek answers to the question “How has GenAI affected security?” » with an understanding of the nature of AI generation applications. You need to discover where security risks creep into generative AI applications to get a better idea of generative AI security.
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Attack via prompts
Do you know how generative AI applications work? You give them an instruction or request in the form of a natural language prompt and they offer human-like responses. The language model underlying the gen AI application will analyze your prompt and generate output using its training. Generative AI applications can receive information from different sources, such as APIs, embedded applications, web forms or uploaded documents. As you can see, inputs or prompts entered into Gen AI applications create a larger attack surface.
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Misusing context awareness of Gen AI applications
The proliferation of genAI security risks is not just limited to prompts used for generative AI applications. Gen AI systems also maintain the context of conversations and could use previous interactions as a reference. Attackers can use malicious input to modify immediate responses and subsequent interactions with generative AI applications.
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Non-deterministic nature of AI generation applications
Generative AI models can also generate different results for a single input, creating inconsistencies in validating their responses. This unpredictability can help bad actors bypass security controls, thereby increasing security risks.
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Solving the Most Pressing Security Issues in Generative AI
The capabilities of generative AI are no longer a surprise as they have managed to introduce pioneering changes in various fields. Malicious actors can leverage the ability of generative AI to automate and scale complex tasks to deploy different attacks. A review of AI security risk examples will reveal how attackers can use generative AI to create convincing phishing emails. Gen AI tools for code generation can also help attackers create custom malware that is difficult to detect.
The security risks posed by generative AI also extend to social engineering attacks. AI generation can be used as a tool to create personalized manipulation techniques and generate fake videos or voices of leaders. There are many other notable security risks associated with generative AI models, beyond phishing, malicious code generation, and social engineering attacks. The Open Web Application Security Project has compiled a list of the top security vulnerabilities found in generative AI systems.
Hackers can create prompts that will manipulate a generative AI model to expose sensitive information or perform unauthorized actions.
Threats to AI security in generational AI systems can also arise from malicious manipulation of training data. Modified training data may introduce bias into the model, generate harmful results, or degrade model performance.
Attackers can implement denial of service attacks by excessively consuming a model’s resources. As a result, the generative AI model cannot provide the desired quality of service and may incur unreasonably high operational costs.
Unauthorized plagiarism of generative AI models can also lead to risks of competitive disadvantage. Organizations will find their intellectual property at risk due to model theft and may also face legal issues due to misuse of their intellectual property.
The adoption of AI in security systems can create more challenges due to supply chain vulnerabilities. Even the slightest breach in libraries, training data, or third-party services used by AI systems can introduce new security risks.
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Excessive confidence in the release of the AI generation
Users should also expect security risks from generative AI systems when they are unsure how to manage their production. Blind trust in AI generation results without verification can lead to issues such as remote code execution and opportunities for the spread of false information.
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Preparing Risk Mitigation Strategies for AI Security in the Age of AI Generation
The ideal approach to addressing the security risks associated with generative AI should revolve around solving the challenges for models, data, and users. AI models can overcome GenAI security risks by adopting best practices for robust validation of training data. Monitoring AI models for abnormal behavior after deployment and adversarial training can help you protect AI models.
Protecting data used in training generative AI models is also a top priority for AI security strategies. Differential privacy techniques, stricter access controls, and data anonymization can improve data integrity and maintain the highest levels of confidentiality. When it comes to protecting users, awareness and powerful filters in AI models can prove useful for AI security.
Final Thoughts
You cannot develop a definitive strategy to combat the security risks of generative AI without knowing the risks. Awareness of threats to generative AI security can provide an ideal basis for developing risk mitigation strategies for AI systems. As adoption of AI systems continues to grow and generative AI gains momentum, it is more important than ever to identify emerging security issues.
Professional certification programs like 101 Blockchains’ Certified AI Security Expert (CAISE)™ certification can help you understand how AI security works. This is a comprehensive resource for learning about notable security risks and defense mechanisms. You can leverage the certification program to gain professional insights into AI security use cases across various industries. Choose the best way to hone your AI security expertise now.
