5-Step Blueprint to a Smart AI Security Strategy

You’d never allow privileged information to fall into the hands of third parties, would you? If you’re using AI, you might be unwittingly doing just that.

Here’s how it can happen. Imagine your organization just rolled out a custom AI-powered document management system designed to streamline workflows and enhance productivity. This cutting-edge tool promises to automate routine tasks, generate insightful analytics, and improve decision-making. However, alongside these benefits come significant security considerations that you must address to safeguard your company’s sensitive data.

Now, suppose an attorney uploads a confidential memo into the system. Is your AI system secure enough that no one who shouldn’t see that information—whether inside or outside the company—can access privileged information from that memo? How sure are you about that? Could lawyers on other projects, who may not have access to this protected information, see portions of this memo reflected in the outputs they receive from the AI system? What about external vendors tasked with training your AI algorithm?

This is just one AI scenario where robust data security needs to be considered. Every company that leverages AI needs a well-thought-out strategy for harnessing its capabilities and maximizing operational benefits while maintaining security.

Whether you’re a Chief Information Security Officer (CISO) responsible for AI operations, a Chief Data Officer, a legal data scientist or legal process analyst, this five-step blueprint offers a clear path to a smart security strategy for implementing AI.

Step 1: Build a Cross-Functional AI Governance Team

Before you can design a security strategy around AI legal services, you must understand the security pitfalls you may encounter and how to balance them with operational needs. Begin by assembling a cross-functional team of stakeholders to explore the implications—and the risks—of using AI in your business. This team should examine how the business plans to use AI in the context of data privacy, confidentiality, and cybersecurity.

Stakeholders to include in the team include the following:

• Operational leaders, who provide insights into how to balance the practical applications of AI across business units with security needs
• Legal professionals, who ensure compliance with governance, ethical, and compliance obligations including data privacy mandates such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), Australian Privacy Act, and others
• IT experts, who make certain that AI tools have the proper technical infrastructure and security architecture and manage the risks associated with data breaches and cyber threats
• Information security professionals, who confirm that the organization is managing risks and following best practices and ensure the business is protecting the organization and working to find more secure ways to achieve operational efficiency
• Communications and change management professionals, who make sure that everyone in the organization understands the security risks of AI

Collaboration among these stakeholders facilitates organizational buy-in. By involving these key groups in the evaluation, selection, implementation, and governance of AI tools, organizations can design their AI-based systems to achieve their business goals while avoiding common data security blunders.

Step 2: Research and Evaluate AI Solution Security

AI platforms may be installed on-premises or based in the cloud. Which model you choose for which application depends on your business’s needs and internal capabilities. In-house solutions offer complete control over AI data and operations, but that means you’re responsible for anticipating every possible risk. Cloud-based platforms offer somewhat less control but may reduce risks, costs, and burdens on resources.

Regardless of whether AI is hosted in-house or via the cloud, security remains a paramount concern. A critical layer of security involves controlling access to AI technology. Does everyone in the company need access? Can access be limited to specific business units? Defining who can use the AI solution, and for what purpose, helps the company understand and control its use cases.

Security planning must also account for how AI will be used, especially if there will be a public-facing platform. Measures must be in place to prevent malicious activities like prompt injection attacks, which could compromise the data or the behavior of the AI. While platforms like Microsoft’s Copilot come equipped to tackle these challenges, newer platforms might require more scrutiny, and bespoke AI systems developed in house will be starting from ground zero.

Work closely with the legal and compliance professionals on your AI governance team as you evaluate your options. They can help you understand the data protection implications of processing different types of data or storing data in different jurisdictions.

Step 3: Test the Tool Before Deployment

Jumping in with both feet might be the fastest way to start any venture, but few would argue it’s the safest.

To ensure a secure AI implementation, follow a phased deployment strategy that utilizes a sandbox: a separate testing environment. This approach allows you to iteratively test and refine your AI models without compromising your data security.

There are many benefits of a training environment for data security. Here are just a few:

• Developers and AI trainers can tweak algorithms to explore different outcomes in a controlled setting, minimizing the risk of security breaches.
• You can protect sensitive data and operations from potential vulnerabilities and errors that may arise during development.
• The training environment provides an opportunity to identify and address real-world security issues so you can craft thoughtful solutions without the operational pressures of premature implementation.

In short, by thoroughly vetting AI models in a discrete training environment, you reduce the risk of deploying insecure or flawed systems, leading to a more secure and robust AI implementation when the system goes live.

Step 4: Map Out AI Opportunities and Conduct Pilot Tests

The potential for using AI is nearly limitless—but your resources probably aren’t. Methodically evaluate and prioritize AI projects based on their strategic value, feasibility, and security implications. Involve all members of your cross-functional team in this analysis to ensure that each chosen initiative aligns with your legal, technical, operational, and security criteria.

Finding the right opportunities for AI within an organization involves ongoing discussion with your governance team to gain diverse insights into various use cases. Evaluate and prioritize ideas so you can test the most viable and least risky AI applications first. Run a few pilot projects in your training environment to gain valuable insights into the potential risks of each option and the security challenges you may encounter in a full-scale deployment. Use these pilots to identify vulnerabilities in a controlled setting, allowing for remediation before full implementation.

Don’t overwhelm the process or your team by attempting to implement every AI application simultaneously. Doing too much at once increases security risks by introducing multiple variables at once, masking the cause of vulnerabilities. Instead, concentrate on applications where you can ensure a secure implementation. This approach paves the way for more complex future AI deployments.

Step 5: Implement AI-Related Policies

Data can be both a massive resource for organizations and a tremendous liability. Any time you feed data into an AI system, you may cede some level of control over it. Data input into an AI system may become part of its library and may show up again in its output.

This raises substantial concerns around data usage. What data is being used within the AI system? Is privileged or confidential data involved? Are privacy measures such as anonymization adequate for the tasks at hand? These questions are particularly crucial in the context of data retention, where the output may be confidential, privileged, or subject to legal holds.

Organizations cannot responsibly adopt an AI system without first implementing robust security, data mapping, and data governance practices governing its use. This includes drafting and enforcing clear policies and procedures governing what data is used in the AI system, ensuring compliance with privacy laws, and maintaining vigilance against unauthorized AI tool usage. Most importantly, organizations must fully train each authorized user on the organization’s AI tools before providing them with unsupervised access to the tool.

Follow a Smart Process to Ensure Security When Using AI

Developing a security strategy for using AI to maximize operational benefits and minimize risks won’t be easy, but it’s critical. It requires thoughtful collaboration across multiple disciplines, strategic planning, and, most of all, patience. But the rewards may be substantial.

By following these five steps, you can achieve your business objectives while safeguarding your organization. For more detailed guidance and examples from industry experts, check out our recent webinar, “Hard Hats Required: Security Considerations in AI Implementation.”‍