What is AI security?

AI security defends AI applications against malicious attacks that aim to weaken workloads, manipulate data, or steal sensitive information. It adapts principles of confidentiality, integrity, and availability for the AI lifecycle and technical ecosystems. 

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AI workloads introduce new areas that traditional IT security doesn't cover. That’s why AI security focuses on protecting AI workloads from misuse and manipulation. It’s different from using AI for cybersecurity (protecting against criminal or unauthorized use of electronic data) or AI safety (preventing harmful consequences that result from using AI). 

AI security involves identifying vulnerabilities and supporting the integrity of your AI systems so they can run as intended and without disruption. This includes securing untampered training data, model provenance, and graphics processing unit (GPU) isolation within your platform. (Most GPUs aren't built with security or isolation in mind and can be easy targets for attackers.)

Most existing security postures need upgrades to account for the new attack surface AI workloads present. In this rapidly evolving space, a flexible approach will help secure AI workloads and the systems they run on as new guidance is generated.

To protect your AI systems, it’s important to understand them inside and out. The more you understand your AI technology, the better you can protect it. 
 

What is AI infrastructure? 
 

Think about it this way: A typical IT configuration is like a house. It has a few vulnerabilities, like doors and windows, but they can all be locked and sealed. An AI solution is more like an apartment building on another planet, with entry points that stretch across dozens of floors and galaxies. It has many attack points you might not have considered before.

AI solutions provide a wealth of opportunities for users and attackers, meaning they can be both useful tools and security nightmares. Given the security challenges of traditional software, AI’s complexity requires specialized security strategies that can work with your current processes.

An effective AI security strategy accounts for all the doors and windows, closing gaps and avoiding opportunity for infiltration through active prevention. It not only protects sensitive data from exposure and exfiltration, but also applies protections against attacks, maintains compliance with explicit security policies and regulatory frameworks (such as the EU Artificial Intelligence Act), and provides visibility and confidence in the security posture of your AI systems.

AI manipulators are becoming smarter and, therefore, sneakier. While AI security attacks vary, some occur more frequently than others. Common attack types include:  

• Prompt injection attacks. AI attackers use malicious prompts to manipulate AI outputs into revealing sensitive data, executing unintended actions, or bypassing implicit and explicit security policies and controls.
• Data poisoning. Attackers manipulate AI models by injecting malicious data or malware, resulting in inaccurate, biased, or harmful outputs. This can cause disruptions and generate inappropriate results.

AI systems face risks from both malicious attacks and operational failures. Just like any deployed system, models can suffer from drift and decay when not cared for properly. When models are trained on low-quality data or aren’t updated over time, the data can become incorrect, outdated, and even harmful, leading to poor performance and inaccuracies. 

To protect your team and technology, layer your strategies—1 line of defense probably won’t cut it. Common tactics include:

• Behavioral analysis. This type of threat detection can catch anomalies and deviations within a network. After tracking typical data sets, patterns, and activity, it becomes intimately familiar with the AI system’s typical behavior. When it comes across atypical behavior, such as biased content—or worse, public-facing passwords in cleartext format—it triggers an alert.
• Runtime threat detection. If an adversary is scanning an environment for possible exploitations, existing runtime security can detect repeated probes and sound the alarm. You can automate and enhance this technique with AI to recognize threats and trigger alerts sooner.
• Predictive threat intelligence. This technology anticipates future events by referencing historical data to make predictions about what will happen next. For example, if an adversary were targeting fintech systems with ransomware, predictive threat intelligence would assess the organizational posture against the likelihood of a successful attack.
• Enhanced data processing. AI workloads contain a lot of data—we’re talking billions and billions of data points. AI security has to process that data in order to conclude whether it’s at risk, confidential, or available. Enhanced data processing can detect anomalies and threats in the environment faster than humans or traditional processing technology, so your team can act quickly.
• Attack path analysis: This strategy lets you map out potential vulnerabilities and opportunities for threats. For example, understanding how a threat may enter your systems and reach sensitive data helps you identify which path an attack would come from and block it. 

Every phase of the AI lifecycle has vulnerabilities that need protection. Elements of a healthy AI security strategy that can help protect your AI models, data, and privacy include:

• AI guardrails: Guardrails help generative AI models filter hateful, abusive, or profane speech, personally identifiable information, competitive information, or other domain-specific constraints.
• Protected training data: AI systems tend to be as reliable as the data they were trained on. Original training data should be secured behind firewalls or other safeguards against tampering or manipulation to protect the model’s integrity and outputs.
• Strong platform security: Protect the platform on which your AI workloads run to ensure their health and reliability. If the platform is secure, threat actors will have to work harder to inflict harm.
• Supply chain and systems security: You can adapt existing best practices in supply chain and systems security to cover AI workloads. Just as traditional software supply chain security verifies the integrity of open source libraries, AI supply chain security can account for the provenance and integrity of training data, pretrained models, and third-party AI components.
• Customized strategies: AI workloads are unique, rarely fitting into one-size-fits-all security solutions. Your security strategy should be tailored to your individual AI workloads, designs, and data. 

You can implement these best practices with common tools and solutions that protect your AI systems, including: 

• Identity and access management: These systems control who has access—as well as how and when—to AI systems and infrastructure. For example, use multi-factor authentication to protect sensitive data.
• AI security posture management: These tools monitor your security deployments and operations. They provide visibility and insights into models and data so you can keep an eye on your AI systems.
• Output validation process: Poisoned or unvalidated outputs can introduce problems in downstream systems and even expose sensitive data. This process double-checks your model’s outputs before sending them downstream for further operations.

AI security can bring a range of benefits to your enterprise AI strategies—peace of mind included. Whether it’s helping your AI workloads run smoothly or keeping your team focused on what’s important, AI security will make your AI strategy stronger. A few benefits include: 

• Reduced exposure and risk. By preventing data compromises, AI security can keep sensitive and private data from getting into the wrong hands. When attacks are stopped before they can inflict harm, users and AI systems can work as intended.
• Time and cost savings. Reduced exposure of sensitive data leads to fewer disruptions and smoother operations. Neutralizing or thwarting attacks reduces downtime and frees up more time for innovation.
• Improved threat intelligence. As your AI security acts against potential threats, it learns about common risks and how they operate. Over time, it can stay ahead of those threats. 

AI is relatively new, and the industry is still working on perfecting the technology. Because it keeps changing, securing AI requires a flexible approach. Common challenges the industry is experiencing include: 

• Evolving AI-specific threats. Because AI continues to change, the windows of opportunity for exploitation make AI applications and models attractive targets for malicious actors. As AI morphs and evolves, its security requirements change too.
• Complex software supply chain. The AI lifecycle is made up of numerous puzzle pieces, ranging from open source libraries to third-party application programming interfaces (APIs) to pretrained models. Each of these puzzle pieces is a potential entry point for attackers. Complex AI supply chains require a layered AI security approach to account for its various components.
• Critical AI safety requirements. Cleaning data to remove model bias and drift is crucial to ensuring models operate as intended. Understanding and cleaning AI training data requires specific skills that are new to the industry.
• Existing security integration. When you integrate new technologies like AI with your existing tools, be sure to use systems that secure and observe both AI workloads and supporting infrastructure.
• Visibility and governance gaps. Despite best efforts to create security and policy solutions for new AI, many unforeseen risks have not been proactively addressed—sometimes because it’s their first occurrence. For your AI security policies to work, you must constantly update them as new recommendations emerge.

While there have always been risks to data and user privacy, AI introduces many new ones. Key guidelines to follow when using AI include: 

• AI privacy. Ensuring AI privacy means protecting personal and proprietary data from unauthorized use. Consider significant security measures to make sure private data stays safe.
• AI compliance. As AI changes, legal compliance and government regulations will change with it, potentially creating an industry standard that can improve how we use AI. 

While not within the realm of AI security, AI ethics can impact the overall risk AI presents to an organization. Users should be aware of their model outputs and how they use them to make decisions.

Using AI ethically means heeding societal values like human rights, fairness, and transparency. To ensure models align with your AI ethics policy, verify how models were developed and trained. You'll also need to continuously monitor their outputs so they don't drift from the policy.

Open source encourages transparency and community trust. Our solutions are built for the hybrid cloud with open source technologies, which help secure the end-to-end AI lifecycle. 

Red Hat® AI helps teams experiment, scale, and deliver innovative applications. It offers a holistic, layered approach to AI security, built on our foundation of platform security and DevSecOps practices. 
 

Explore Red Hat AI 
 

Our solutions empower customers to build and deploy reliable AI applications, mitigating security and safety risks at every stage. Specifically, Red Hat OpenShift® AI provides benefits to maintain fairness, safety, and scalability with AI security, including:

• Enhanced visibility. Remove the “black box” phenomenon that hides how models reach their conclusions and keeps algorithms (and users) in the dark. Get insights into vulnerabilities, malicious code, license issues, and potential AI safety concerns with Red Hat Trusted Profile Analyzer.
• Integrated development workflows. Catch vulnerabilities sooner and reduce costly, redundant work by applying AI security best practices early and consistently during development. Integrated into Openshift AI, Red Hat Advanced Developer Suite hosts tools like Red Hat Developer Hub that you can implement in development workflows to support model provenance and evaluation.
• Hybrid cloud consistency. Inference anywhere with AI solutions built for hybrid cloud flexibility. AI workloads should have the same level of security and performance whether you operate on-premise, in the cloud, or at the edge.
• Model alignment. Ensure consistent data accuracy and model integrity by monitoring the alignment between model outputs and training data. OpenShift AI also supports efficient fine-tuning of large language models (LLMs) with LoRa/QLoRA to reduce computational overhead and memory footprint.
• Drift detection tools. Protect your model inputs and outputs from harmful information such as abusive and profane speech, personal data, or other domain-specific risks. AI guardrails and real-time monitors can detect when live data used for model inference deviates from original training data to harmful AI slop. 

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