AI Security in the Age of GenAI: Protecting Models, Data, and Users

The adoption of any new technology on a massive scale across different industries is likely to create concerns regarding security. Malicious actors have not left any stone unturned to explore every opportunity to exploit artificial intelligence systems. Businesses have to think about AI security in gen AI era as attackers can surprisingly leverage generative AI itself to break into the most secure AI systems. Understanding the security risks that come with gen AI has become more important than ever.

Generative AI has become one of the prominent technologies with a transformative impact on how businesses operate and view security. You could find at least one in three organizations using generative AI in one business function. Gen AI not only improves productivity and efficiency but also introduces a wide array of security challenges. Organizations have to think about AI security for models, data and their users in the age of generative AI.

Gauging the Scope of AI Security Risks in the Gen AI Era

The spontaneous growth in large-scale adoption of generative AI has introduced many new attack vectors that you cannot handle with conventional security measures. A report by SoSafe on cybercrime trends in 2025 suggested that more than 90% of security experts expect AI-driven attacks to grow in the next three years (Source). The use of AI in security systems might seem like a promising solution to achieve stronger safeguards against emerging threats. However, the numbers have a completely different story to say about how generative AI will affect security.

Gartner has pointed out that over 40% of AI-related data breaches will happen due to inappropriate use of generative AI, by 2027 (Source). A survey of global business and cybersecurity leaders in 2024 revealed that almost half of the respondents believed generative AI will drive the growth of adversarial capabilities (Source). The survey also showed that some experts believed gen AI could be responsible for exposing sensitive information and data leaks. 

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Understanding How Generative AI Increases Security Risks

Anyone interested in measuring the impact of generative AI on security would obviously search for the most notable security risks attributed to gen AI. On the contrary, they should search for answers to “How has GenAI affected security?” with an understanding of the nature of gen AI applications. You must find out where security risks creep into generative AI applications to get a better idea of gen AI security.

• Attacking through Prompts

Do you know how generative AI applications work? You give them an instruction or query 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 an output by using its training. Generative AI applications can take inputs from different sources, such as APIs, integrated applications, web forms or uploaded documents. As you can notice, the input or prompts entered in gen AI applications create a broader attack surface.

• Misusing the Context Awareness of Gen AI Applications

The proliferation of genAI security risks is not limited solely to prompts used for generative AI applications. Gen AI systems also maintain the context in conversations and could use previous interactions as a reference. Attackers can use malicious inputs to change immediate responses and the subsequent interactions with generative AI applications.

• Non-Deterministic Nature of Gen AI Applications

Generative AI models can also generate different outputs for one input, thereby creating inconsistencies in validating their responses. This unpredictability can help malicious actors find their way around security controls, thereby increasing security risks.   

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Unraveling the Most Pressing Security Concerns in Generative AI

The capabilities of generative AI are no longer a surprise as they have successfully introduced pioneering changes in various areas. Threat actors can leverage the ability of generative AI for automation and scaling up complex tasks to deploy different attacks. A review of AI security risks examples will reveal how attackers can use generative AI to create convincing phishing emails. Gen AI tools for code generation can also help attackers in creating custom malware that is hard to detect.

The security risks posed by generative AI also extend to social engineering attacks. Gen AI can serve as a tool for creating personalized manipulation techniques and generating fake videos or voices of executives. You can find 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 top security vulnerabilities found in generative AI systems.

• Prompt Injection

Hackers can create prompts that will manipulate a generative AI model into exposing sensitive information or executing unauthorized actions.

• Data Poisoning

The threats to AI security in gen AI systems can also emerge from malicious manipulation of training data. The altered training data can introduce biases in the model, generate harmful outputs or deteriorate the model’s performance.

• Denial of Service

Attackers can implement denial of service attacks through excessive resource consumption of a model. As a result, the generative AI model cannot deliver the desired service quality and may inflict unreasonably high operational costs.

• Model Theft

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. 

• Supply Chain Gaps

The adoption of AI in security systems may create more challenges due to vulnerabilities in the supply chain. The smallest flaw in libraries, training data or third-party services used by AI systems can introduce new security risks. 

• Excessive Trust in Gen AI Output

Users should also expect security risks from generative AI systems when they don’t know how to handle their output. Blind trust in gen AI outputs without verification can lead to issues such as remote code execution and possibilities of spreading misinformation.

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Preparing the Risk Mitigation Strategies for AI Security in Gen AI Era

The ideal approach to address security risks associated with generative AI should revolve around resolving the challenges for models, data and users. AI models can overcome GenAI security risks by adopting best practices for robust training data validation. Monitoring AI models for anomalous behavior after deployment and adversarial training can help you safeguard AI models.

The protection of data used in generative AI model training is also a top priority for AI security strategies. Differential privacy techniques, stricter access controls and data anonymization can enhance data integrity and maintain the highest levels of confidentiality. When it comes to protecting users, awareness and strong filters in AI models can prove useful for AI security. 

Final Thoughts 

You cannot come up with a definitive strategy to fight against security risks of generative AI without knowing the risks. Awareness of threats to generative AI security can provide an ideal foundation to develop risk mitigation strategies for AI systems. As the adoption of AI systems continues growing with generative AI gaining momentum, it is more important than ever to identify emerging security concerns.

Professional certification programs like the Certified AI Security Expert (CAISE)™ certification by 101 Blockchains can help you understand how AI security works. It is a comprehensive resource to learn about notable security risks and defense mechanisms. You can leverage the certification program to acquire professional insights on use cases of AI security across various industries. Pick the best way to hone your AI security expertise right now.