How to Secure AI Systems: 5 Best Practices for AI Security in 2026

A decade ago, it would have been hard to believe that artificial intelligence could achieve what it accomplishes today. However, this same transformative power introduces a new attack surface that traditional security frameworks were never designed to address. As AI technology becomes deeply embedded in mission-critical operations, organizations must implement a multi-layered defense strategy encompassing data protection, access control, and continuous monitoring to safeguard these advanced systems. Five foundational practices effectively address these emerging risks.

🔐 1. Enforce Strict Access and Data Governance

AI systems depend entirely on the data they consume and the individuals who access them, making role-based access control (RBAC) one of the most effective methods to limit exposure. By assigning permissions based strictly on job function, teams ensure only authorized personnel can interact with and train sensitive AI models.

Encryption reinforces protection: AI models and their training data must be encrypted both at rest and in transit between systems. This becomes especially critical when data includes proprietary code or personally identifiable information.

Leaving a model unencrypted on a shared server creates an open invitation for attackers. Robust data governance serves as the final line of defense keeping those valuable assets secure.

🛡️ 2. Defend Against Model-Specific Threats

AI models face a diverse array of threats that conventional security tools were not engineered to detect. Prompt injection ranks as the top vulnerability in the OWASP Top 10 for large language model (LLM) applications. This attack occurs when adversaries embed malicious instructions within inputs to override a model's intended behavior.

Deploying AI-specific firewalls that validate and sanitize inputs before they reach an LLM represents one of the most direct methods to block these attacks at the entry point.

👁️ 3. Maintain Detailed Ecosystem Visibility

Modern AI environments span on-premise networks, cloud infrastructure, email systems, and endpoints. When security data from each area exists in separate silos, visibility gaps inevitably emerge—and attackers exploit these gaps to move undetected. A fragmented environmental view makes correlating suspicious events into a coherent threat picture nearly impossible.

Security teams require unified visibility across every layer of their digital environment. This means dismantling information silos between:

• Network monitoring
• Cloud security platforms
• Identity management systems
• Endpoint protection tools

When telemetry from all these sources feeds into a single unified view, analysts can connect the dots between an anomalous login, lateral movement attempt, and data exfiltration event—rather than viewing each in isolation.

As the NIST Cybersecurity Framework Profile for AI makes clear, securing these systems requires organizations to secure, govern, and defend all relevant assets—not merely the most visible ones.

🔄 4. Adopt a Consistent Monitoring Process

Security is not a one-time configuration because AI systems constantly evolve. Models receive updates, new data pipelines are introduced, user behaviors shift, and the threat landscape evolves alongside them. Rule-based detection tools struggle to maintain pace because they depend on known attack signatures rather than real-time behavioral analysis.

Security teams receive immediate alerts with sufficient context to act decisively. The shift toward real-time detection proves critical for AI environments where data volume and velocity far outpace human review capabilities. Automated monitoring tools that learn normal behavioral patterns can detect low-and-slow attacks that would otherwise remain unnoticed for weeks.

📋 5. Develop a Clear Incident Response Plan

Incidents are inevitable, even with robust preventive controls in place. Without a predefined response plan, companies risk making costly decisions under pressure, potentially worsening the impact of a breach that could have been quickly contained.

An effective AI incident response plan should encompass four critical phases:

AI incidents require unique recovery steps, such as retraining a model fed corrupted data or reviewing logs to assess what the system produced while compromised. Teams that plan for these scenarios in advance recover faster with significantly less reputational damage.

🏆 Top 3 Providers for Implementing AI Security

Implementing these practices at scale requires purpose-built tooling. Three providers stand out for organizations looking to establish a comprehensive AI security strategy.

1. 🔷 Darktrace

Darktrace represents a premier choice for AI security, largely due to its foundational Self-Learning AI technology. The system builds a dynamic understanding of what constitutes normal behavior within an enterprise's unique digital environment. Rather than relying on static rules or historical attack signatures, Darktrace's core AI identifies anomalous events, dramatically reducing the false positives that plague rule-based tools.

Darktrace pioneered AI-driven cybersecurity, giving its solutions a maturity advantage over newer market entrants. Its coverage spans on-premise networks, cloud infrastructure, email systems, OT environments, and endpoints—all manageable in unison or at the individual product level. One-click integrations from the customer portal enable organizations to extend coverage without lengthy, disruptive deployment cycles.

2. 🔷 Vectra AI

Vectra AI excels as a solution for organizations operating hybrid or multi-cloud environments. Its Attack Signal Intelligence technology automates the detection and prioritization of attacker behaviors within network traffic and cloud logs, surfacing the most critical activity rather than flooding analysts with raw alerts.

Vectra employs a behavior-based approach to threat detection, focusing on attacker actions within an environment rather than initial access methods. This makes it highly effective at catching:

• Lateral movement
• Privilege escalation
• Command-and-control activity that bypasses perimeter defenses

For teams managing complex hybrid architectures, Vectra's ability to provide consistent detection across on-premise and cloud environments within a single platform represents a significant operational advantage.

3. 🔷 CrowdStrike

CrowdStrike is recognized as an industry leader in cloud-native endpoint security. Its Falcon platform is built on a powerful AI model trained on an extensive body of threat intelligence, enabling it to prevent, detect, and respond to endpoint threats, including novel malware variants.

In environments where endpoints constitute a substantial portion of the attack surface, CrowdStrike's lightweight agent and cloud-native architecture facilitate rapid deployment without operational disruption. Its threat intelligence integrations help security teams connect individual device incidents to larger attack patterns across the entire infrastructure.

🚀 Chart a Secure Future for Artificial Intelligence

As AI systems grow increasingly capable, the threats designed to exploit them will inevitably grow more sophisticated. Securing AI demands a forward-thinking strategy built on three pillars: prevention, continuous visibility, and rapid response—one that adapts dynamically as the threat environment evolves.