A Roadmap for Secure AI Adoption Across Your Team

Here’s a pill many organizations are having a hard time swallowing: despite the hype surrounding AI, despite the many promises and predictions of it disrupting entire industries, despite the nearly daily announcements of new capabilities and groundbreaking results, AI has yet to make us more secure.

In fact, there may even be an argument that we’ve become more vulnerable than ever. According to new research from Forrester, rapid AI adoption has enterprises spending more on security while still struggling to secure their systems — with as many as 67% of them experiencing a significant breach in the past year. 

While it might be tempting to blame AI for much of this, the real problem is how many organizations still don’t have a standardized approach to AI adoption. In the rush to gain an edge, they’ve neglected foundational security strategies, leaving them without the right controls, effective governance structures, and an informed workforce prepared for the new AI threat landscape.

But secure AI adoption isn’t something that happens by accident. Instead, it requires a clear and deliberate strategy for building up AI tools and integrating them into your enterprise in a way that keeps your data, employees, and business safe. Here’s a roadmap for doing that.

Align AI adoption with business goals

One of the first mistakes organizations make with AI is treating the technology as the strategy. In reality, its value comes from how effectively it serves the organization’s goals.

To be fair, AI is almost certainly a transformational tool that will very likely upend entire industries — but it’s still just a tool. This means that it’s most effective when it’s put to the service of the organization’s goals. By clarifying how and why the business intends to use AI to achieve these objectives, they can ensure they’re both prioritizing the right investments and preventing liabilities that can stem from AI misuse.

What are these liabilities? Without a clear strategy, AI adoption can become fragmented. Different teams may adopt different tools, duplicate efforts, or pursue projects that have little connection to organizational goals. Worse, employees may expose sensitive information that puts the business at risk. 

And yet, despite these reasons, a majority of organizations still lack this foundation. Research shows that only 14% of organizations have a documented AI strategy in place.

The good news is that creating this foundation isn’t difficult. Begin by deciding not what kind of AI tools or technology you want to use, but what you want to accomplish with it. Most AI initiatives fall into one of three categories:

• Productivity gains: Using AI to reduce costs, automate repetitive work, and save time.
• Differentiation: Using AI to create better customer experiences or unique capabilities that competitors cannot easily replicate.
• Disruption: Using AI to develop entirely new products, services, or business models.

If you have an understanding of which of these outcomes you’re pursuing, you’ll be much better positioned to make smart decisions about your technology, security, and investment.

You should also consider your data estate. As the foundation of every AI system, your data has an outsize impact on the effectiveness and safety of your AI initiatives. This makes it vital to properly manage and protect the information you intend to feed your models. Organizations can do this by establishing clear data usage policies, implementing privacy and security controls, and ensuring sensitive information is handled according to internal policies and regulatory requirements.

Without these data policies, even the most sophisticated AI strategy can quickly become a security liability.

Establish effective governance controls

Long a pillar of effective cybersecurity strategies, governance has proven equally important in the era of AI, even if it doesn’t look quite the same. 

When it comes to managing AI systems, governance refers to the set of decisions, guardrails, and accountability structures that shape how AI is used across the business. Whereas traditional governance focuses on managing static assets like systems, data, and access controls, AI governance must also account for the more unpredictable nature of AI-generated outputs and decisions. Similarly, whereas traditional governance focuses on shielding an organization from security threats, AI governance also includes measures to protect against ethical breaches or unintended bias.

In short, AI governance is less a reinvention of traditional governance frameworks and more of an extension of it. Both are still chiefly concerned about providing clarity and visibility into complex, fallible systems — which is vital for any organization trying to securely adopt and integrate AI.

Just consider the problem of shadow AI. This is when employees use and adopt AI tools without organizational oversight, usually in an attempt to improve productivity or test out a new technology. Although their intent is positive, these unauthorized tools make it more likely to expose sensitive data, fall afoul of compliance regulations, or provide attackers with a convenient target. 

Once a niche issue, shadow AI is now recognized as a major problem by 76% of organizations, very likely because the average cost of a breach due to shadow AI is now more than $4 million.

So how should organizations start building an effective AI governance framework? Here are a few foundational steps:

1. Detect and audit AI usage: The first step is understanding where AI is already being used across the organization. This includes both sanctioned tools as well as unauthorized applications that employees or teams may have adopted on their own. 
2. Define approved tools and accounts: The next step is to establish clear policies around which AI platforms you want authorized for business use and which accounts employees should use when interacting with those platforms.
3. Assign ownership: Every AI tool, workflow, and business process should also have a clearly defined owner. Ownership should extend to incident response responsibilities too so that teams know who’s accountable if something goes wrong.
4. Continuously monitor AI activity: Don’t think of AI adoption as a one-time event. New tools, workflows, and use cases emerge constantly. Continuous monitoring helps organizations maintain visibility and identify risks before they become incidents.

Governance is probably not the most exciting part of AI adoption. However, it can very well be the difference between a scalable AI program and a potential security problem. Getting your framework established early on in your adoption process will help you continue to build it out as your AI usage grows.

Educate your employees

Many companies may be dreaming of the day they can unleash fully autonomous AI agents without a worry, but for now that day is still a dream. Humans are and for the foreseeable future will need to remain tightly integrated with how AI systems are both used and abused. This makes promoting and providing ongoing AI literacy an essential aspect of AI adoption.

Start this process by ensuring employees understand the fundamentals of AI. For instance, they should know how AI systems work, what they’re capable of, and where their limitations lie. They should also be familiar with common AI threats like prompt injection and data poisoning, as well as how establishing guardrails can and cannot protect against these threats. And they should know about even more subtle risks, such as what can happen when they begin overrelying on AI-generated outputs.

From there, it’s a short jump to ethical considerations. Especially as more organizations and their individual employees become increasingly comfortable with advanced AI systems, it’s important to educate them on how they can recognize potential issues, such as data bias, embedded assumptions, and discrimination. Alongside this should be education on how to prevent these issues and establish clear human responsibility and accountability. Employees should become familiar with human-in-the-loop best practices and know how to strike a balance between too much intervention and too little in order to preserve the benefits of AI automation.

Finally, you should give employees practical guidance on how to safely use AI in their day-to-day work. This includes methods to validate AI-generated outputs, strategies to help prevent any sensitive or regulated data from being ingested by AI systems, and a clear framework for understanding just what information should never be entered into public AI tools. Any established policies for AI usage within your specific organizations should also be clearly explained and reiterated across the company.

Ultimately, the goal here isn’t to turn every employee into an AI expert. It’s to create a workforce that can recognize risks, use AI responsibly, and contribute to a stronger security culture.

Secure AI adoption is a long-term commitment

AI offers enormous opportunities for organizations willing to embrace it. But realizing those benefits requires more than simply deploying new tools. 

Instead, organizations must think through why they want to use AI in the first place so that they can focus their efforts and finances in the most meaningful way. They need to also build visibility and employee accountability at every step so that their AI usage remains responsible and compliant. And they need to invest in ongoing education so that their employees aren’t just an observer, but an active and integral part of their AI initiatives. This is how you create the foundation for secure, scalable AI adoption.

Interested in learning more about how to build a successful AI program? Explore our Creating a Successful AI Program course. You'll learn how to align AI initiatives with business outcomes, implement governance frameworks based on NIST and ISO guidance, build a scalable AI strategy, and manage critical risks such as bias, compliance, and security.