Ten years ago, it would have been hard to believe that artificial intelligence could do what it can do now. However, this same power introduces new attack surfaces that traditional security frameworks were not built to address. As this technology becomes integrated into critical operations, businesses need a layered defense strategy that includes data protection, access control, and constant monitoring to keep these systems secure. Address these risks with five basic practices.
1. Apply strict access and data governance
Because AI systems rely on the data they are fed and the users who access it, role-based access control is one of the best ways to limit exposure. By assigning permissions based on job function, teams can ensure that only the right people can interact with and train sensitive AI models.
Encryption provides added protection. AI models and the data used to train them must be encrypted at rest and in transit between systems. This is especially important if your data contains proprietary code or personal information. Leaving models unencrypted on shared servers is an open invitation to attackers, and strong data governance is the last line of defense to keep these assets safe.
2. Defense against model-specific threats
AI models face a variety of threats that traditional security tools cannot capture. Prompt injection, which ranks at the top of the OWASP Top 10 vulnerabilities for large-scale language model (LLM) applications, occurs when an attacker embeds malicious instructions within the input to override the model’s behavior. One of the most direct ways to block these attacks at the point of entry is to deploy an AI-specific firewall that validates and sanitizes input before it reaches the LLM.
Beyond input filtering, teams should regularly perform adversarial testing. This is essentially ethical hacking of AI. Red team exercises simulate real-world scenarios such as data poisoning and model inversion attacks to expose vulnerabilities before threat actors discover them. Research on red teaming AI systems highlights that this type of iterative testing should be built into the AI ​​development life cycle and not be added after deployment.
3. Maintain detailed visibility of the ecosystem
Modern AI environments span on-premises networks, cloud infrastructure, email systems, and endpoints. If security data for each of these areas resides in separate silos, visibility gaps can occur. Attackers move unnoticed through these gaps. A fragmented view of your environment makes it nearly impossible to correlate suspicious events to create a coherent threat picture.
Security teams need unified visibility across all layers of their digital environment. This means breaking down information silos between network monitoring, cloud security, identity management, and endpoint protection. When telemetry from all these sources is fed into a single view, analysts can connect the dots for anomalous logins, lateral movement attempts, and data leak events without looking at them individually.
Achieving this broad coverage is becoming increasingly non-negotiable. As NIST’s Cybersecurity Framework Profile for AI makes clear, securing these systems requires organizations to protect, deter, and defend all relevant assets, not the most visible.
4. Adopt a consistent monitoring process
AI systems change, so security is not a one-time configuration. Models are updated, new data pipelines are introduced, user behavior changes, and the threat landscape evolves with it. Rule-based detection tools are difficult to respond to because they rely on known attack signatures rather than real-time behavioral analysis.
Continuous monitoring addresses this gap by establishing an operating baseline for AI systems and flagging deviations as they occur. Consistent monitoring can alert you to unusual activity in the moment, such as a model producing unexpected output, a sudden change in API call patterns, or a privileged account accessing data it shouldn’t normally have access to. Security teams receive instant alerts with enough context to act quickly.
The shift to real-time detection is critical to the AI ​​environment, where the volume and velocity of data far exceeds human review. Automated monitoring tools that learn normal behavior patterns can detect low-frequency attacks that can go unnoticed for weeks.
5. Develop a clear incident response plan
Even with strong preventive controls in place, incidents are inevitable. Without a pre-defined response plan, companies risk making costly decisions under pressure, which can exacerbate the impact of a breach that could have been stopped immediately.
An effective AI incident response plan must include containment, investigation, eradication, and recovery.
Containment: Limit the immediate impact by isolating affected systems.
Investigation: Find out what happened and how far it went
Eradication: Remove threats and patch exploited weaknesses.
Recovery: Introduce stronger controls to restore normal operation.
AI incidents require unique recovery steps, such as retraining a model that was input with corrupted data or reviewing logs to see what the system produced when it was compromised. Teams that plan ahead for these scenarios recover faster and suffer far less reputational damage.
Top 3 providers implementing AI security
Implementing these practices at scale requires specialized tools. For organizations looking to implement a full-fledged AI security strategy, three providers stand out.
1. Darktrace
Darktrace is a great choice for AI security primarily because it has a basic self-learning AI. The system dynamically understands what is normal in a company’s unique digital environment. Rather than relying on static rules or past attack signatures, Darktrace’s core AI looks for anomalous events and reduces false positives that plague more rule-based tools.
The second layer of analysis is provided by cyber AI analysts who autonomously investigate every alert and determine whether it is part of a broader security incident. This can reduce the number of alerts in a SOC analyst’s queue from hundreds to just two or three critical incidents that require attention.
Darktrace was one of the early adopters of AI in cybersecurity, giving its solutions a maturity advantage over new entrants. Its coverage spans on-premises networks, cloud infrastructure, email, OT systems, and endpoints, all of which can be managed centrally or at the individual product level. With one-click integration from the customer portal, brands can expand their reach without the need for lengthy and disruptive deployment cycles.
2.Vectra AI
Vectra AI is a powerful option for organizations running hybrid or multicloud environments. The company’s Attack Signal Intelligence technology automates the detection and prioritization of attacker behavior in network traffic and cloud logs, revealing the most important activity without bombarding analysts with raw alerts.
Vectra takes a behavior-based approach to threat detection, focusing on the attacker’s behavior within the environment rather than how the attacker initially gained access. This allows for effective capture of lateral movement, escalation of authority, and command and control activities that bypass perimeter defenses. Teams managing complex hybrid architectures benefit from Vectra’s ability to provide consistent discovery across on-premises and cloud environments in a single platform.
3.Cloud Strike
CrowdStrike is recognized as a leader in cloud-native endpoint security. The company’s Falcon platform is built on powerful AI models trained on extensive threat intelligence to prevent, detect, and respond to threats on endpoints, including emerging malware.
In environments where endpoints make up a large portion of the attack surface, lightweight agents and cloud-native setup make it easy to deploy without disrupting operations. Threat intelligence integration also helps security teams connect the dots and connect what’s happening on a single device to larger attack patterns that play out across the infrastructure.
Envisioning a safe future for artificial intelligence
As AI systems become more capable, the threats designed to exploit them will also become more sophisticated. Securing AI requires a forward-thinking strategy built on prevention, continuous visibility, and rapid response that adapts to the evolving environment.
