10 Key AI Security Controls For 2026

To enable secure usage of AI and agents, essential controls include deep visibility, strong authentication and AI-aware data loss prevention.

AI Security Controls To Know

For tech industry veteran Moudy Elbayadi, there’s no question that the arrival of productivity-boosting AI and agentic technologies is a net positive. “I’m the biggest fan of AI,” said Elbayadi, whose resume has included serving as CTO of Shutterfly and CIO of LifeLock. “My view is, let’s use every capability to make every human as smart as possible. To me, that is the only smart way of doing things.”

But to get the most out of AI, security and governance must be taken seriously from the get-go. “You’ve got to put in the guardrails,” said Elbayadi, who is now chief AI and innovation officer at Evotek, No. 92 on CRN’s Solution Provider 500 for 2025. For Solana Beach, Calif.-based Evotek, which specializes in providing numerous IT solutions and services with a focus on cybersecurity, the opportunity to work with customers around implementing security controls for enabling AI usage is massive, according to Elbayadi.

For starters, visibility into AI usage remains a big challenge. Key questions every organization must ask, he said, include: “How do you know what AI tools are being used and abused? What agents are running? What MCP servers may be connecting to an internal system?”

Other key AI security controls range from strong identity and authentication, AI-aware data loss prevention and continuous AI red teaming. Notably, many of the controls themselves will benefit greatly from the use of GenAI and AI agents, Elbayadi said.

For CRN’s inaugural AI Security Week 2026, we’ve assembled a list of the 10 most important AI security controls for solution providers to know for enabling secure usage of GenAI-powered tools and AI agents.

What follows are the details you need to know on 10 key AI security controls for 2026.

Deep Visibility

For solution providers looking to protect their customers, “inventory visibility is so critical” when it comes to AI, according to Kenny Parsons, cloud security team lead at Irvine, Calif.-based Trace3, No. 34 on CRN’s Solution Provider 500 for 2025. Thus, the foundational AI security control for most organizations in 2026 will be around gaining full visibility into all AI usage. This is crucial for many reasons, both for overseeing the use of sanctioned AI SaaS platforms and agents as well as for detecting employee use of unsanctioned “shadow AI” tools. Deep visibility entails the ability to see what is happening in real time, not after the fact. Such visibility should cover which AI tools are being used, who is using the tools and what data is being accessed. In addition, organizations need to know how AI tools are interacting with internal systems. Ultimately, visibility is essential to enforce all other AI security controls, making it the starting point for organizations looking to enable secure usage of AI.

Approved AI Tooling

For many organizations, providing a list of sanctioned AI tools—or even better, deploying tools directly to employees—will be a key part of the answer to enabling secure usage of GenAI and agents, solution providers said. That’s critical both for heading off shadow AI as much as possible and also goes hand-in-hand with boosting AI visibility—since doing so will be more straightforward with approved tools. Key priorities include evaluating AI tools and platforms for data practices, followed by creation and maintenance of a catalog of vetted AI tools and platforms. Providing approved AI tooling ultimately signals to employees that usage of AI—when it meets the needs of the organizations around security and governance—is encouraged.

Strong Identity And Authentication

To truly enable productivity-enhancing usage of GenAI and agents, identity and access considerations should be paramount, according to solution providers. “You need to be thinking from an identity standpoint of how do [agents] get access to things at different times for different time lengths?” said Damon McDougald, global security services lead at Accenture, No. 1 on CRN’s 2025 Solution Provider 500. “That’s different than what we usually do today for humans.” Key controls include strong authentication for every AI tool or agent that is accessing systems and data—with continuous authorization and verification even after initial authentication—as well as least-privilege access controls such as just-in-time access. Utilization of behavioral analytics, which are in fact powered by AI, will also be important for enabling detection of abnormal identity and access activity. All in all, it’s crucial for organizations to treat AI agents on the same level as human identities, with inventories and governance performed just as it would be for human workers.

Governance And Policy Enforcement

While it may seem like an obvious necessity, establishing strong governance and policy enforcement for AI usage can quickly get complex, according to solution providers. Setting the rules of engagement for AI usage requires organizations to account for a wide array of variables. Governance and policy implementation may need to cover who can use AI tools—and who can use which tools—as well as what data can be accessed and how outputs can be utilized. In addition, policies and governance around situations requiring human oversight is a critical aspect to get right, solution providers said. The introduction of AI agents adds further challenges: To ensure that agents are not going astray from what is expected, adding an extra layer of enforcement on top of agents will be pivotal. According to Ben Prescott, head of AI solutions at Trace3, No. 34 on CRN’s Solution Provider 500 for 2025, this is where visibility comes in as a foundational requirement. “Now you [have to know] what is the agentic solution itself actually planning and executing? And how do we understand what the right output is that is actually generating within that agentic workflow?” Prescott said.

AI-Aware Data Loss Prevention

For Gladstone, Ore.-based Covenant Technology Solutions, prioritizing a review of a customer’s data practices has become a cornerstone of how the MSP is working with customers around AI, according to CEO Timothy Choquette. The typical customer discussion lately has been along the lines of, “‘We can help you with this, but we need to take a look at your data,’” Choquette said. “‘We need to make sure you’re prepared and ready to do these things you want to go do, so we can confidently know that you’re ready to go.’” A key part of those preparations will involve implementing data classification and data loss prevention (DLP) that is “AI-aware”—in other words, that can be applied in real time to prompts, uploads and outputs as well as agent-to-agent data interactions. Meanwhile, unlike traditional methods, AI-aware DLP utilizes contextual analysis to understand the intent of users and agents—and can then respond by blocking or redacting sensitive data before it’s exposed, based on the organization’s policies.

Continuous AI Red Teaming

Red team testing—which assesses software for vulnerabilities and exploitability—is typically associated with occasional, point-in-time testing. But the dynamic, continuously evolving nature of GenAI and agentic models adds massive complexity to the testing process, according to solution providers. Thus, continuous AI testing is not possible for most organizations from a financial or operational perspective without automation, said Evotek’s Elbayadi. Trying to hire a major firm to perform AI red teaming exercises on an ongoing basis can run in the millions of dollars, Elbayadi said. New tools using AI itself, however, “will do that as effectively and as continuously as possible,” he said. These new AI-powered tools can probe for vulnerabilities such as prompt injection, data exposure and unauthorized agent actions—and continue to run the tests even as models are updated and agents gain new capabilities.

Supply Chain Security

A related control to continuous AI red team testing is AI supply chain security, which brings a focus on assessment and management of risks introduced by AI model providers, open-source software and third-party data sources. Vulnerabilities in a third-party model or poisoned training data, for instance, can lead to a massive risk of a compromise even if the organization has taken all the right steps to secure its own internal models. As a result, solution providers said organizations should consider extending AI security controls beyond their own environments—such as through conducting vendor risk assessments and tracking the origins of AI models. Continuous monitoring of third-party components for changes or emerging threats is another important step to take for most organizations. The ultimate goal is to treat AI models and agent frameworks with the same rigorous level of security that would be applied to traditional software systems.

AI Risk Monitoring

With many of the new AI systems, the reality is that performance and behavior are likely to change as the models are used, according to security experts. Depending on the way that you define your AI system, it can potentially get better or worse over time, according to Daniel Kendzior, global data and AI security practice leader at Accenture, No. 1 on CRN’s Solution Provider 500 for 2025. AI risk monitoring, of course, is going to be most essential for identifying instances where performance has degraded over time. And importantly, “it’s not always obvious which path you’re going down,” Kendzior said. Major risks that might crop up can include hallucinations, model drift, bias or policy violations, according to experts. Deploying continuous monitoring of AI systems can provide detection of such issues at an early stage—ultimately enabling teams to intervene before the risks escalate.

Continuous Validation

In the same way that AI risk monitoring assesses how AI models are behaving over time, continuous validation for the AI security controls themselves is important for similar reasons. As AI models, agents and data sources change, the AI security controls an organization has in place may not remain as effective as they once were. Continuous validation brings a focus on consistent testing to verify that AI security controls—such as identity enforcement, data protection and governance—are still functioning properly and in the way that was originally intended. Such processes are increasingly a good fit for automation, which can rapidly detect policy drift, misconfigurations and other issues.

AI Security Training

Last, but definitely not least, is for organizations to consider the human element of AI risk. Advanced AI security controls can quickly become moot if organizations don’t make sure that their teams receive training about secure usage, common risks and corporate policies related to AI tools. Cesar Avila, founder and CIO of Fort Lauderdale, Fla.-based AVLA, said his MSP has been helping to train employees at customer organizations to be aware of how they’re using AI tools. The challenge is that many people are still not very conscientious when it comes to using AI and agents, Avila said. Ultimately, “you have to train them and give them an AI policy.”