Are your AI Inference and GenAI Environments Secure? These Five Essentials Will Help
AI systems are quickly becoming critical elements of business technology. Imagine building an AI agent trained using your internal documents and guides to quickly improve customer experience, process loan applications, or provide tier 1 support to your customers. The potential is tremendousโbut what happens if your models are trained using your own intellectual property like software code, customer data, or other sensitive information?
Malicious actors are looking to manipulate your inference environment into sharing sensitive information or providing a biased response. This can have huge ramifications for your organization, from lost customer business to regulatory fines. For IT and security teams, the challenge is to recognize and address the risks associated with AI before itโs too late.
Business-critical AI Initiativesโand Risks
As the AI revolution gathers speed, many enterprises are focused on two types of initiatives in particular:
- AI inference โ Using AI and machine learning to automate and optimize existing services. Typical use cases include loan processing, customer support, and fraud detection.
- GenAI โ Providing employees with tools based on large language models (LLMs) that can create content based on user prompts. Examples include creating graphics or videos for presentations, writing and testing code, and generating meeting summaries.
Both AI Inference and GenAI have potentially great value for the organization. As mainstream adoption continues, these capabilities are quickly becoming not just a competitive differentiator, but a baseline requirement. AI is now business-critical, and its security needs to reflect that.
The Open Worldwide Application Security Project (OWASP) has identified critical threats against AI environments. Companies now face growing risks associated with:
- Attackers manipulating model responses through prompt injection attacks or compromising their operations through data and model poisoning attacks.
- Unbounded consumption attacks in which users conduct uncontrolled inferences that degrade LLM performance and availability.
- Sensitive information disclosure when users enter personally identifiable information (PII), proprietary algorithms, or sensitive business data into an LLM and it ends up being used as training data.
- Agentic AI performing damaging actions due to lax permissions between the agents fielding user requests and the LLMs with which they connect.
You canโt rely on existing controls to mitigate these risks. Traditional security products like network firewalls only understand network traffic, and next-gen web application firewalls only understand HTTP/HTTPS. AI security requires the ability to see into the next level of information, i.e., user prompts, which in turn depends on an understanding that goes beyond HTTP/HTTPS. Legacy solutions donโt have this capability, allowing many new types of attacks against AI to go undetected.
Meanwhile, threat actors are using AI to create ever more sophisticated malware. Even bad actors with little or no hacking ability can use freely available tools to create quite sophisticated threats.
Five Security Essentials for AI Environments
Security for AI environments must go beyond tools. Youโll also need to update your practices to ensure that AI is being trained and used in the right way, by the right people, with the right controls in place. At a high level, your AI security initiative should encompass the following five elements.
- Governance
The first and foremost step is to set up rules of engagement. While not a threat against AI systems as such, lack of governance is a major risk factor for companies adopting AI. Users who enter sensitive data and intellectual property into prompts can invite data leakage and compliance violations. Improper data handling and model training can embed biases into AI-powered systems. Erroneous outputs, such as hallucinations, can bring legal problems and potential damage to customer relationships. Companies need to outline comprehensive governance policies for the use and management of AI systems and their data. Key tenets include acceptable use, data privacy, output validation, and transparency in AI decision-making, and regulatory compliance.
- Compliance
Adhering to relevant regulations and standards will help companies avoid penalties while leveraging the security best practices embedded in these guidelines. A comprehensive compliance program can help mitigate risks from data breaches and attacks to bias. Compliance will also help the organization build trust with users, stakeholders, and partners by showing accountability for strict legal, regulatory, and ethical standards.
While AI regulations are still emerging, key mandates and frameworks to follow include:
- Implement Tools to Protect from AI-related Threats
With implementing AI, it is important to understand the attack surface area of the AI environment. This means you need to learn about threats like prompt injections, data hallucinations, poisoning attacks, and others discussed in the OWASP Top 10 for LLMs and GenAI Apps. The two most critical solutions to protect against AI-related threats are:
- AI gateway โ You must be able to validate that the users or agents accessing your models are legitimate and not bad actors or bots. An AI gateway performs authentication and authorization functions for the identities requesting access to the inference model.
- AI firewall โ Once access is granted, an AI firewall inspects prompts and responses to prevent attacks like prompt injection, model theft, and data leakage. This makes it possible to detect and block threats against AI systems such as malware, data poisoning, and prompt injection. Rate limiting helps prevent volumetric attacks such as unbounded consumption.
- Monitoring and Auditing
Vigilance is as critical for AI systems as it is for every other enterprise technology. Continuous monitoring of AI systems can detect anomalies, suspicious activities, or potential vulnerabilities early for a rapid response to potential attacks or breaches. Security audits and regular penetration testing can help uncover vulnerabilities in training and deployment environments so they can be addressed proactively. Both threats and best practices continually evolve, so monitoring and auditing criteria should as well.
- Continuous Education
Employees need to know how to use AI safely as well as how to recognize signs of danger. Shadow AI poses a constant risk; sensitive data or proprietary code fed into public GenAI services can later surface in responses to outside users. Rapid innovation of AI technologies can lead users to misunderstand what their current tools are capable of and best suited for, leading to inappropriate or reckless usage. Employees need to understand how to make these decisions responsiblyโand whatโs at stake if they donโt.
AI has moved at tremendous speed from promising experiments to widespread adoption. Now security must advance with similar velocity. By updating your tools and practices for the threats facing this new technology, you can unlock its value for your business while mitigating the risks it can bring.