AI 시스템 보안을 위한 5가지 모범 사례

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 p