A decade ago, it would have been hard to believe that artificial intelligence could do what it can do now. However, it is this same power that introduces a new attack surface that traditional security frameworks were not built to address. As this technology becomes embedded in critical operations, companies need a multi-layered defense strategy that includes data protection, access control and constant monitoring to keep these systems safe. Five foundational practices address these risks.
1. Enforce strict access and data governance
AI systems depend on the data they are fed and the people who access them, so role-based access control is one of the best ways to limit exposure. By assigning permissions based on job function, teams can ensure only the right people can interact with and train sensitive AI models.
Encryption reinforces protection. AI models and the data used to train them must be encrypted when stored and when moving between systems. This is especially important when that data includes proprietary code or personal information. Leaving a model unencrypted on a shared server is an open invitation for attackers, and solid data governance is the last line of defence keeping those assets safe.
2. Defend against model-specific threats
AI models face a variety of threats that conventional security tools were not designed to catch. Prompt injection ranks as the top vulnerability in the OWASP top 10 for large language model (LLM) applications, and it happens when an attacker embeds malicious instructions inside an input to override a model’s behaviour. One of the most direct ways to block these attacks at the entry point is by deploying AI-specific firewalls that validate and sanitise inputs before they reach an LLM.
Beyond input filtering, teams should run regular adversarial testing, which is essentially ethical hacking for AI. Red team exercises simulate real-world scenarios like data poisoning and model inversion attacks to reveal vulnerabilities before threat actors find them. Research on red teaming AI systems highlights that this kind of iterative testing needs to be built into the AI development life cycle and not bolted on after deployment.
3. Maintain detailed ecosystem visibility
Modern AI environments span on-premise networks, cloud infrastructure, email systems and endpoints. When security data from each of these areas is in a separate silo, visibility gaps may emerge. Attackers move through those gaps undetected. A fragmented view of your environment makes it nearly impossible to correlate suspicious events into a coherent threat picture.
Security teams need unified visibility in every layer 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 feeds into a single view, analysts can connect the dots between an anomalous login, a lateral movement attempt and a data exfiltration event not seeing each in isolation.
Achieving this breadth of coverage is increasingly nonnegotiable. As the NIST’s Cybersecurity Framework Profile for AI makes clear, securing these systems requires organisations to secure, thwart and defend in all relevant assets, not the most visible ones.
4. Adopt a consistent monitoring process
Security is not a one-time configuration because AI systems change. Models are updated, new data pipelines are introduced, user behaviours change and the threat landscape evolves with them. Rule-based detection tools struggle to keep pace because they rely on known attack signatures not real-time behavioural analysis.
Continuous monitoring addresses this gap by establishing a behavioural baseline for AI systems and flagging deviations as they happen. Consistent monitoring can flag unusual activity in the moment, whether it’s a model producing unexpected outputs, a sudden change in API call patterns or a privileged account accessing data it normally shouldn’t. Security teams get an immediate alert with enough context to act fast.
The change toward real-time detection is critical for AI environments, where the volume and speed of data far outpace human review. Automated monitoring tools that learn normal patterns of behaviour can detect low-and-slow attacks that would otherwise go unnoticed for weeks.
5. Develop a clear incident response plan
Incidents are inevitable, even with strong preventive controls in place. Without a predefined response plan, companies risk making costly decisions under pressure, which can worsen the impact of a breach that could have been contained quickly.
An effective AI incident response plan should cover containment, investigation, eradication and recovery:
- Containment: Limits the immediate impact by isolating affected systems
- Investigation: Establishes what happened and how far it reached
- Eradication: Removes the threat and patches the exploited weakness
- Recovery: Restores normal operations with stronger controls in place
AI incidents require unique recovery steps, like retraining a model that was fed corrupted data or reviewing logs to see what the system produced while it was compromised. Teams that plan for these scenarios in advance recover faster and with far less reputational damage.
Top 3 providers for implementing AI security
Implementing these practices at scale requires purpose-built tooling. Three providers stand out for organisations looking to put a serious AI security strategy into practice.
1. Darktrace
Darktrace is a premier choice for AI security, largely because of its foundational Self-Learning AI. The system builds a dynamic understanding of what normal looks like in an enterprise’s unique digital environment. Rather than relying on static rules or historical attack signatures, Darktrace’s core AI looks for anomalous events, reducing the false positives that plague more rule-based tools.
A second layer of analysis is provided by its Cyber AI Analyst, which autonomously investigates every alert and determines whether it is part of a wider security incident. This can reduce the number of alerts that land in a SOC analyst’s queue from hundreds to just two or three critical incidents that need attention.
Darktrace was among the earliest adopters of AI for cybersecurity, giving its solutions a maturity advantage over newer entrants. Its coverage spans on-premise networks, cloud infrastructure, email, OT systems and endpoints – all manageable in unison or at the individual product level. One-click integrations from the customer portal mean brands can extend that coverage without long, disruptive deployment cycles.
2. Vectra AI
Vectra AI is a strong option for organisations running hybrid or multi-cloud environments. Its Attack Signal Intelligence technology automates the detection and prioritisation of attacker behaviours in network traffic and cloud logs, surfacing the activity that matters most not flooding analysts with raw alerts.
Vectra takes a behaviour-based approach to threat detection, focusing on what attackers do in an environment, not how they initially gained access. This makes it effective at catching lateral movement, privilege escalation and command-and-control activity that bypasses perimeter defenses. For teams managing complex hybrid architectures, Vectra’s ability to provide consistent detection in on-premise and cloud environments in a single platform is an advantage.
3. CrowdStrike
CrowdStrike is recognised as a leader in cloud-native endpoint security. Its Falcon platform is built on a powerful AI model trained on an extensive body of threat intelligence, letting it prevent, detect and respond to threats at the endpoint, including novel malware.
In environments where endpoints make up a large chunk of the attack surface, its lightweight agent and cloud-native setup make it easy to deploy without disrupting operations. Its threat intelligence integrations also help security teams connect the dots, linking what’s happening on a single device to a larger attack pattern playing out in the whole infrastructure.
Chart a secure future for artificial intelligence
As AI systems grow more capable, the threats designed to exploit them will also grow more sophisticated. Securing AI demands a forward-thinking strategy built on prevention, continuous visibility and rapid response – one that adapts as the environment evolves.
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