A Guide to Securing AI Applications

Overview for Cybersecurity Professionals

AI solutions are increasingly becoming integral to enterprise environments, impacting nearly all aspects of productivity. From content generation and AI-assisted search to virtual agents and data analytics, these tools are becoming a staple in our everyday work. As cybersecurity professionals, we must anticipate the potential harm these tools might introduce and implement mitigations—ideally starting from the design phase. To do this effectively, it is crucial to understand the unique security challenges posed by AI applications.

AI does not operate in isolation. It depends on various infrastructure and software components that work together to deliver its intended functionality. Protecting AI applications, therefore, requires a holistic approach—one that encompasses infrastructure, software, and the AI models themselves. In this overview, we’ll outline the steps necessary to safeguard your AI applications.

Preparation and Planning

Failing to plan is planning to fail.

Start by engaging application owners and stakeholders to understand the use case. Gather details such as the business need the application addresses and the parts of the enterprise it impacts. This context is essential to understand the environment in which the application operates.

Next, scope out the application by documenting its components, data sources, and external integrations. Collect supporting materials such as system diagrams and data flows to map out the landscape. Assemble a team of stakeholders—including cybersecurity professionals, AI specialists, and developers—who can provide input based on their expertise. This collaborative approach will guide your planning and help structure information-gathering sessions with the relevant participants.

Asset Inventory

You can’t secure what you don’t know you have.

Start by taking inventory of everything that powers the application—servers, databases, software, APIs, and even training datasets. Classify these assets based on their sensitivity and importance. For example, confidential data such as user information or proprietary algorithms should be flagged for high-priority protection. A clear understanding of your assets is essential for pinpointing where to focus your security efforts.

Identifying Threats

A vulnerability on its own does not constitute a risk if no threat exists to exploit it. Therefore, identifying potential threats in the environment and mapping them to the vulnerabilities found in the product is crucial. To do this effectively, you need to think like an attacker. Leverage frameworks such as STRIDE to uncover potential risks through threat modeling:

• Could someone tamper with your data?

• Spoof an identity to gain unauthorized access?

• Deny service to legitimate users?

• Exfiltrate sensitive data?

Analyze these possibilities and rank them based on their likelihood and potential impact. This prioritization helps ensure you address the most critical vulnerabilities first.

Discuss the findings with business stakeholders to decide how to manage them. The outcome of this discussion should be a clear remediation plan that includes a timeline, assigned responsibilities, and a commitment to action.

Infrastructure Security

Your application’s infrastructure is its backbone, and any weaknesses in this area can have far-reaching consequences. To bolster security, consider adopting a Zero Trust approach. This model assumes that no user or device—whether inside or outside the network—should be trusted by default. Zero Trust combines elements such as:

• Identity management for authentication,

• Policy enforcement points to control access, and

• Policy engines to make dynamic access decisions.

Best practices for infrastructure security include:

• Segmenting networks to limit exposure,

• Enforcing secure communication protocols,

• Deploying NG firewalls, intrusion detection/prevention systems (IDP/IPS), and web application firewalls (WAFs) to secure entry points,

• Protecting endpoints with EDR (Endpoint Detection and Response) or MDR (Managed Detection and Response) solutions,

• Keeping servers and devices patched and monitored.

For cloud or hybrid environments, ensure your provider is reputable and offers robust security controls. All communication between cloud and on-premise systems must be encrypted and authenticated to prevent interception or tampering.

Regularly assess your infrastructure for weaknesses through penetration testing, and promptly mitigate any findings. Establish baseline security settings and routinely verify compliance to maintain a consistent level of protection.

Software Security

The software components of your AI application require close scrutiny. It’s essential to adhere to a Software Development Life Cycle (SDLC) process. The SDLC is a structured framework used by development teams to create high-quality software. It divides software development into distinct, repeatable stages, serving as a roadmap to ensure the final product meets expectations. From a cybersecurity perspective, the SDLC is critical for integrating security measures at every phase, starting as early as the planning stage.

Key practices include:

• Code Review: Regularly review the code for vulnerabilities and ensure developers follow secure coding practices.

• Third-Party Libraries: Audit and monitor third-party libraries, as they are often a weak link, and keep dependencies up-to-date.

• Testing: Conduct both automated scans and manual penetration testing to identify vulnerabilities that the tools might miss.

When using open-source software, obtain it from trusted sources, such as official project websites or reputable software repositories, to ensure its integrity. Before deploying any open-source component, verify it against public vulnerability databases like the National Vulnerability Database (NVD) or Common Vulnerabilities and Exposures (CVE). This step ensures the software is secure and free from known issues.

AI/ML Components

AI introduces unique security challenges, particularly for the Large Language Model (LLM) components of AI applications. Specific attacks target these models to corrupt outputs, poison the model, or infer sensitive training data, such as personally identifiable information (PII). Known as injection attacks, these exploits work by embedding malicious commands disguised as harmless user inputs via the prompting interface.

To prevent such attacks, several strategies should be employed:

• Input Sanitization: Ensure prompts are clean of potentially malicious content before being processed by the LLM.

• Output Validation: Validate outputs to ensure they do not contain harmful, erroneous, or unintended content.

• Adversarial Training: Train the model to improve its resilience against injection attacks by exposing it to adversarial examples during development.

For pre-trained models, review their security scores and evaluations on platforms such as the LLM Safety Leaderboard by Hugging Face to ensure they meet your security requirements.

Additional best practices include:

• Deploying models in isolated, secure environments like containers,

• Monitoring for suspicious activity and unusual behavior, and

• Regularly updating and patching models to address emerging vulnerabilities.

Data Security

In today’s world, data is the new oil.

AI applications are only as effective as the data they rely on, making data security and quality critical to their success.

To safeguard data, start by inventorying it as you would other application components. Begin by identifying all data sources—whether from a data lake, database, network storage, or user-uploaded files. Classify the data based on its sensitivity within the organization and ensure it is stored accordingly. Sensitive data may require encryption, and it’s essential to maintain encryption throughout the entire data flow. For data in transit, encrypt all communication, even within internal networks.

Implement role-based access controls (RBAC) to enforce strict permissions on the data. Ensure that permissions are properly maintained as data moves through the application. Processing data should not involve excessive access rights, and technical personnel should be limited to administrative tasks without access to sensitive information. The principle of least privilege should guide access controls throughout the process.

Additionally, consult your company’s data retention policies. When copying data from its original location for processing in the AI application, ensure retention policies are upheld. Retaining data longer than necessary can lead to negative legal implications, so this should be actively avoided.

Operational Practices

Implement centralized logging to track activities and detect anomalies within your AI application. Continuously monitor models for any anomalous behavior and integrate event data into your incident management process. A robust incident response plan is essential to address security events that could jeopardize the application or compromise data.

Ensure a backup strategy is in place for all components, including data, models, and infrastructure. For critical applications, consider implementing a comprehensive disaster recovery plan (DRP) to minimize downtime and data loss in the event of a failure.

Documenting and Sharing Insights

Finally, document your findings. Summarize vulnerabilities, risks, and recommended mitigations in a clear and actionable report. Share these insights with stakeholders to align on priorities and guide investment decisions. Develop a roadmap for mitigating risks and ensure follow-up on the implementation of mitigation measures.

Transparent communication is key to ensuring that everyone understands the security landscape and their role in maintaining it.