AI adoption has moved past the experimentation stage to the formalization stage at many large companies, now embedded across a wide range of roles and departments that guide business operations. But this adoption has come with a hidden cost: while AI is being put to work and is creating new business value, it’s also often working with few – if any – guardrails in place to secure and govern it.
The juxtaposition between AI use and AI governance or compliance is stark. According to Check Point research, 77% of security leaders have already changed their security strategy to address AI, but only 26% have the architecture in place to effectively enforce those policies. This growing gap between AI adoption and AI control is where risk emerges.
The good news is that organizations do not need to completely redesign their infrastructure to improve AI security. Instead, they can take a practical, phased approach focused on five foundational actions that help establish visibility, accountability, governance and protection.
1. Begin by understanding what AI already exists
Many organizations cannot confidently answer the most basic question concerning AI: What AI is being used across the business today?
Employees use sanctioned and unsanctioned AI tools, enterprise applications increasingly include embedded AI features and agentic systems are beginning to interact with corporate data and workflows. Before security teams can protect AI, they need visibility into what exists, where it is being used and by whom.
The first step is building a comprehensive AI inventory that includes AI tools, embedded AI features, autonomous agents, associated data access and usage patterns. This visibility creates the foundation for every security decision that follows. Organizations should be able to identify not only what AI technologies are in use, but also what systems and data they can access.
2. Create simple rules for employees
Technology alone cannot solve AI security challenges.
Employees need clear guidance on how AI should and should not be used. Policies must be practical, understandable and easy to follow. Just as importantly, organizations should provide approved alternatives that enable productivity while reducing risk.
When people have no sanctioned way to use AI, they often create their own. That can result in sensitive information being uploaded to external models, creating data exposure and compliance concerns.
The goal is not to restrict innovation but to make the secure option the easiest option. When secure pathways are readily available, adoption improves and risky workarounds become easier to identify.
3. Put controls where AI actions occur
As organizations deploy AI applications and agents that can access sensitive information, execute workflows, or interact with production systems, security controls must move closer to the point of action.
Rather than attempting to secure every AI model directly, organizations should focus on established control points they already manage, including network gateways, identity providers, access controls and agent platforms.
Runtime protections and guardrails should be prioritized for the highest-risk use cases, particularly those involving customer data, financial transactions, or operational systems. The objective is to ensure that risky prompts, unauthorized data flows, or potentially harmful agent actions can be stopped before they cause damage.
4. Assign clear ownership
One of the most common reasons AI security programs struggle is a lack of clear responsibility and accountability.
Ownership for AI often becomes fragmented across security teams, development organizations, compliance functions, data governance groups and business units. When everyone shares ownership, no one truly owns it.
Organizations should designate a single function with the authority to define AI security policies, oversee enforcement and coordinate across teams. This owner must have visibility into people, applications, data and autonomous systems.
Boards and executives increasingly want answers about AI risk. A clearly identified owner ensures someone can provide them.
5. Test continuously
AI systems are inherently dynamic. Models evolve. Prompts change. New connectors are added. Permissions expand. What appears secure today may not remain secure tomorrow.
Traditional testing approaches are not enough. Organizations must continuously evaluate AI applications and agents the way attackers would, assessing models, prompts, data sources, tools, integrations and permissions together.
Regular red teaming and security validation help expose weaknesses before adversaries do. Just as importantly, findings should feed back into policies and technical controls so protection improves over time.
Closing the AI security gap
Securing AI does not begin with a massive transformation. It begins incrementally and should start with visibility, governance, ownership and practical controls applied in the right order.
Organizations that understand what AI they have, establish clear guardrails, enforce protections at key control points, assign accountability and continually test their environment will be far better positioned to capture the benefits of AI while managing its risks.
