AI is everywhere now, transforming the way businesses run and how users engage with apps, devices and services. Many applications have artificial intelligence inside, including chat interface support, intelligent analysis of data, and matching user preferences. While AI is undoubtedly beneficial to users, it also poses new security challenges, particularly identity-related security challenges. Let’s explore what these challenges are and what you can do to face them.
Which ai?
Everyone talks about AI, but the term is very common and some technologies fall under this umbrella. For example, Symbolic AI uses technologies such as logic programming, expert systems, and semantic networks. Other approaches use neural networks, Bayesian networks, and other tools. The new Generated AI uses machine learning (ML) and large language models (LLM) as core technologies to generate content such as text, images, video, audio and more. Content creation is equipped with ML and LLM. That’s why when people talk about AI, they probably refer to ML and LLM-based AI.
AI systems and AI-powered applications are at different levels of complexity and are subject to a wide range of risks. Typically, vulnerabilities in AI systems also affect AI-powered applications that rely on it. This article focuses on the risks that affect applications with AI. Most organizations have already started building or will be building it in the near future.
Protect your genai app from identity threats
There are four important requirements that identities are important when building AI applications:
First, user authentication. The agent or app needs to know who the user is. For example, in a chatbot, you may need to know my age and country of residence to view chat history or customize replies. This requires some form of identification and can be performed with authentication.
Secondly, call the API on your behalf. AI agents connect to far more apps than typical web applications. As Genai Apps integrates with more products, it is important to call APIs safely.
Third, asynchronous workflows. AI agents may need to spend time completing tasks or wait for complex conditions to be met. It could be a few minutes or a few days. Users don’t have to wait that long. These cases become mainstream and are implemented as asynchronous workflows, with agents running in the background. In these scenarios, humans act as supervisors and approve or reject actions when they leave the chatbot.
Fourth, approval of search extension generation (RAG). Almost all Genai apps can provide information to AI models from multiple systems to implement RAG. To avoid confidential disclosure, all data provided to the AI model to respond or act on your behalf must be data that the user has permission to access.
To fully realize your Genai potential, you need to solve all four requirements and ensure that your Genai application is built safely.
Use AI to support security attacks
AI has also made it easier and faster for attackers to execute target attacks. For example, leverage AI to run social engineering attacks or create deepfakes. Additionally, attackers can use AI to exploit vulnerabilities in large-scale applications. Building Genai safely into your application is one challenge, but can it help you use AI to detect and respond to potential attacks faster due to security threats?
Traditional security measures like MFA are no longer sufficient on their own. Integrating AI into your identity security strategy allows you to detect bots, stolen sessions, or suspicious activity. It helps us:
Common performs intelligent signal analysis to detect unauthorized or suspicious access attempts, analyze various signals related to application access activity, and automatically terminate sessions if suspicious activity is detected. Compare patterns with historical data to search
There is a huge amount of AI-based applications, but AI also poses new security challenges.
What’s next?
AI is changing the way humans interact with technology. Over the next decade, we will see the rise of a huge AI agent ecosystem: a network of interconnected AI programs that are integrated into applications and act autonomously for us. While genai has many positives, it also introduces important security risks that must be taken into consideration when building AI applications. It is important for builders to integrate Genai into their apps to make it AI and enable enterprise-ready.
The backstage of AI is a way to help with traditional security threats. AI applications face similar security issues as traditional applications, such as unauthorized access to information, but also the use of new attack techniques by malicious actors.
AI is, for better or worse, reality. It brings countless benefits to users and builders, but at the same time, it is concerning concerns and new challenges on the security side and across all organizations.
Identity companies like Auth0 are here to help remove security pieces from the plate. For more information about building Genai applications, see auth0.ai safely.
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