AI system security: 3 strategies to protect your model infrastructure and data

Organizations have never been more eager to develop artificial intelligence (AI) models, driven by customer demand and the need to stay competitive. However, while AI transformation can lead to efficiency gains and support more strategic business decisions, it also introduces security risks.

AI security vulnerabilities are unlike those observed in traditional network or data environments, which can often be mitigated through strategies like patch updates or enforcing safer password practices. Transformation requires organizations to explore new AI risk mitigation techniques to protect sensitive data and model infrastructure from attackers.

An innovative approach to AI system security is especially crucial because exploiting these systems can have greater consequences than other cybersecurity intrusions. For example, malicious manipulation of AI systems operating autonomous vehicles, informing healthcare diagnoses, or guiding legal decisions.

Prioritizing the security of your training data and AI infrastructure is necessary for several reasons. It’s essential for maintaining stakeholder trust, avoiding financial and legal penalties, and protecting trade secrets and other information that, when exposed, could diminish your competitive edge. To navigate this landscape, organizations must maintain constant awareness of AI’s evolving risks and develop robust strategies for threat detection, response, and recovery. 

Understanding AI system security risks: Cybersecurity in AI 

AI’s unique security challenges are rooted in the technology’s training limitations, as well as the potential to manipulate training data and prompts. 

Training and reasoning limitations 

Models trained on large datasets can detect patterns and generalize knowledge to new information. While this makes them extremely powerful for supporting a range of tasks, models are still incapable of reasoning or scrutinizing information like humans. Skilled adversaries can exploit this limitation by manipulating prompts and inputs to disrupt a model’s decision-making process.

For example, an attacker could strategically place tape on roadway markers or signs. While a human would likely still recognize the original meaning of an altered stop sign, an AI-powered vehicle might change its behavior by driving through an intersection if not trained to classify stop signs with an unusual appearance. 

Training data poisoning 

Adversaries can also disrupt a model by manipulating training data. This attack vector, called model poisoning, may alter system behavior, introduce bias, and compromise output reliability. Bad actors don’t need direct access to your data to poison a model, either—they can introduce harmful information on sources a model may ingest, like a public webpage. 

Prompt-based attacks

They can also access external data through techniques like prompt injection. Using cleverly worded prompts, attackers may trick models into revealing sensitive or private training data, such as customer details, company documents, or trade secrets.

Methods like prompt injection or input-based attacks can be hard to prevent for several reasons:

• Enterprises have little control over external activities, like manipulated public data sources or stop signs, and may not detect these risks until too late. 
• Such attack vectors exploit core aspects of AI functionality, making them difficult to safeguard against. 
• AI models are often considered black boxes, meaning their internal algorithms and decision-making processes lack transparency. As a result, identifying when models have been compromised and fixing those vulnerabilities is a constant challenge. 

These factors make AI security different from securing networks and other parts of your IT infrastructure. They are also why organizations must adopt innovative and flexible solutions as the technology accelerates. 

3 key strategies for AI vulnerability management 

Enterprises should implement encryption to protect AI models and training data. However, encryption is just part of the solution. Organizations must also implement solutions like threat detection systems and comprehensive incident response plans as part of a holistic AI risk mitigation strategy. 

AI model and data security 

Organizations must secure both AI training data and the model. This includes the model’s algorithms, parameters, and other components of AI architecture. Because the model is a digital file, it can also be corrupted or stolen. Attackers who gain access to these files can easily compromise model functionality and bypass any security infrastructure you’ve invested in to protect it. 

During model development and inference, ensure your data is encrypted throughout its lifecycle—from labeling to data transfer and storage. Also, encrypt AI model files and edge devices like vehicles, sensors, or medical devices. Even if adversaries gain access to these systems, they won’t be able to exploit proprietary data or models.

As a best practice, enforce strict access controls for AI users. For example, consider building policies in the AI system that restrict financial data access to the appropriate users on your finance team. Early in AI development, sanitize training data to prevent sensitive information from being unnecessarily exposed to your model. To avoid data corruption risks caused by third-party vendors, review the terms and conditions carefully and only use trusted data suppliers that already use robust security infrastructure. 

AI anomaly detection 

Network security tools often have continuous monitoring features that scan and alert for anomalies indicating an intrusion. Similarly, organizations must implement detection systems for finding deviations in AI systems.

This can be difficult, considering the black box approach typical of some AI models. Additionally, determining suspicious behavior is often complex. For example, an AI user who repeats a prompt dozens of times could be an attacker trying to manipulate outputs or a valid user simply testing system performance. 

Organizations must define what constitutes allowable activity on an AI system on a case-by-case basis. This is a crucial first step in ensuring real risks aren’t overlooked while minimizing false positives.

Common alerting techniques used with AI systems include:

• Prompt handling: Monitor user prompts and flag those indicating malicious intent, such as prompt injection. 
• Input sanitization: Check inputs for unsafe or invalid characters signaling intrusion. 
• Data sanitization: Use outlier detection systems to find and omit adversarial data before model training. 
• Output audits: Maintain output reliability and quality through regular reviews and automated validation systems that fact-check outputs.

Security teams can also implement ethical hacking for AI anomaly detection, a strategy in which security personnel uncover vulnerabilities by mimicking malicious hacking attempts. AI ethical hacking takes this a step further. Using techniques like machine learning to automate risk detection is valuable for understanding the evolving security landscape from an attacker’s perspective because adversaries may use their own AI systems to target enterprise AI. 

Incident response planning 

It’s important to build an incident response plan for AI-specific risks. As part of the plan, thoroughly brief employees on response measures, especially those deviating from traditional cybersecurity protocols, and clearly define roles and responsibilities. 

Planning for AI-specific risks 

Determine what qualifies as an AI security incident for your enterprise and investigate how your AI systems could be compromised based on each use case. Researching common AI risk scenarios in your industry or niche will also help you anticipate how an incident may unfold, and which response measures will be effective. For example, if your organization is a prime target for customer data breaches, consider how adversaries may execute prompt-based attacks to steal information. 

Containing damage 

Organizations should perform a complete inventory of their AI systems and map shared assets. This is crucial for anticipating how a threat could escalate and allows you to quickly identify the scope of impact when a breach is detected. From here, security teams must develop an isolation plan to prevent the spread of damage. Administrators may opt to isolate data, systems, processes, or networks, depending on the type of intrusion. If an AI system must be isolated or shut down, establish alternative solutions to fill the gaps and be prepared to support workarounds for affected users and customers. 

Considering real-world implications 

Your organization may need to address real-world security measures, depending on how and where AI systems are deployed. For example, AI administrators may be responsible for securing edge devices or reversing manipulation to physical objects, like road signs. 

Reporting and recovering 

When it comes to incident recovery, organizations should be able to provide comprehensive documentation to remain accountable and transparent to stakeholders. Some enterprises may need novel solutions that can automatically log AI system access points and changes to make it easier to trace user activity during post-incident investigations. Before any AI systems are recovered or re-deployed, development teams need to establish policies for strengthening AI system security and validating the trustworthiness of its inputs and outputs. 

Staying adaptive amid evolving AI system security 

Because we’re still in the early stages of widespread AI transformation, there’s no standard approach to safeguarding AI systems from exploitation. AI practitioners should stay aware of the technology’s emerging risks and develop proactive and reactive measures tailored to their infrastructure’s vulnerabilities and likely attack vectors.

It’s also important to be up to date with best practices and solutions for addressing AI security issues. Organizations should review resources like the National Institute of Standards and Technology’s (NIST) Artificial Intelligence Risk Management Framework, the Open Worldwide Application Security Project’s (OWASP) Top 10 for LLM, or the Organization for Economic Cooperation and Development’s (OECD) AI Incidents Monitor. These institutions provide valuable information on common AI vulnerabilities and security frameworks.

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