AI security is the practice of leveraging artificial intelligence to improve an organization's security posture. But the practice is multifaceted and takes many different shapes.

From one angle, you can harness AI to strengthen your broader cybersecurity defenses, such as automating threat detection and response at machine speed. Alternatively, this practice also involves defending your organization against cyberattackers who now weaponize AI to launch faster, more convincing attacks. It’s fighting fire with fire: AI plays both offense and defense in cybersecurity.

‍

AI security can also be seen as protecting your own AI systems and models from theft or misuse. These threats are a call to action, and CISOs and security leaders are taking notice. A BCG report found that 60% of organizations may have encountered AI-enabled cyberattacks in the past year, but only 7% are using AI in defense.

‍

Despite mounting awareness of the risks AI poses, the adoption of adequate cyber defense is struggling to keep pace with the speed and sophistication of AI-driven attacks. Here, we examine the nuances of AI security and explore best practices to use AI as a proactive defense ally.

What does AI security include?

"AI security" is an expansive term that covers many areas, and that's the point. It's multiple interrelated disciplines that work together toward a unified set of objectives to keep your organization safe while preventing AI from turning vulnerabilities into breaches.

Using AI for cybersecurity

Perhaps the biggest advantage that AI delivers to security teams is speed. Machine learning (ML) models can quickly alert analysts to abnormal behavior across the network. These ML models can detect subtle changes in a user's behavior that may indicate an advanced threat, signals a human analyst might otherwise miss. Once identified, AI can help SOC teams prioritize potential threats based on severity and exploitability, enabling them to focus on threats that require immediate attention rather than chasing false positives. 

‍

Additionally, AI has greatly improved the investigation process. AI can correlate signals across various sources, such as endpoint devices and log files. Once a possible threat has been identified, AI security models can initiate automated actions to isolate potentially compromised workloads, preventing further lateral movement by attackers.

Securing AI systems

Your AI systems need their own defense plan. That means protecting models, training data, prompts, APIs, and the applications and pipelines built around them from tampering, theft, and misuse. An attacker who gains access to an organization's training data can manipulate its outputs or steal proprietary information outright. 

‍

According to a 2026 survey of over a thousand cybersecurity and IT professionals worldwide, 54% of respondents said they experienced an AI-related security event, and almost a quarter (24%) of them could not confirm the source due to lack of visibility. With AI ever-present, securing your development pipeline and supporting infrastructure is no longer optional.

Defending against AI-powered attacks

Threat actors are using AI technology to increase the velocity of attacks against organizations. They can accelerate reconnaissance activities and more quickly identify vulnerable areas of an organization’s environment. With AI, cyberattackers can improve the likelihood of successful phishing campaigns by creating more believable email messages and generating new malware variants.

‍

While many AI-powered attacks will likely include some form of human input into the decision-making process, the use of AI does significantly improve the speed at which attacks occur. In fact, recent studies have shown that AI-generated spear phishing email messages have resulted in click rates of 54%, which is far above traditional phishing benchmarks averaging 12%.

Why is AI security important?

Every new AI tool you adopt opens an additional entry point into your environment, and that expansion happens fast. AI systems introduce non-human identities, model APIs, SaaS integrations, and ephemeral cloud services that traditional security platforms were not designed to monitor. 

‍

Then you have the human element, which introduces shadow AI and opens a huge can of worms in your network. In fact, one recent analysis discovered that over 70% of employees already use AI tools at work, often without formal approval or oversight.

‍

Without implementing robust AI security practices, organizations will be vulnerable in numerous areas:

‍

• Sensitive data exposure: The prompts used during conversations, files and images uploaded during interactions, and data stored within training datasets can be improperly disclosed to unauthorized parties when appropriate controls are not put in place.
• Model integrity: Tampering with an AI model can result in producing unreliable results that include bias, or even intentionally manipulating the output to deceive users making decisions based upon those results.
• Service availability: Attacks on the inference endpoints or infrastructure behind your AI-based applications can take those applications offline.
• Regulatory compliance: Fines for serious violations under regulatory frameworks such as the EU AI Act may reach as high as €35M (or 7% of global revenue).
• Customer trusts: Your customers will notice. As many as 87% of consumers say they would be unwilling to conduct business with companies where they do not believe that their security has been properly protected.

‍

A compromised AI application does not usually remain confined. Since it likely interacts with your data, other applications, and underlying infrastructure, when an attacker gains entry through one vector, they are able to pivot into anything else connected to that initial point of entry.

How does AI security work?

AI security operates as an ongoing cycle built on five core functions that work in unison, and the framework behind it depends as much on people, processes, and governance as it does on technology.

‍

1. Discover AI assets and dependencies: You can't effectively secure what you can't see. A recent study found that 91% of the AI tools used by enterprises operate outside their control. In turn, identifying a comprehensive inventory of both sanctioned (approved) and shadow (unauthorized) AI is step one.
2. Assess vulnerabilities and business risks: The National Institute of Standards and Technology (NIST) provides frameworks such as the AI Risk Management Framework to map, measure, and prioritize the risk associated with each phase of the AI lifecycle, including but not limited to harmful bias and model drift.
3. Protect models, data, applications, and infrastructure: Layer controls — encryption, access control, segmentation — across every level of the AI stack.
4. Monitor AI behavior and system activity: Continuous monitoring identifies anomalies in model outputs and unauthorized access prior to escalation. A recent global survey found that 80% of respondents stated that their AI agents had already performed actions exceeding their defined scope.
5. Respond to and contain security incidents: Microsegmentation allows for quick isolation of affected systems during an incident, limiting lateral movement and reducing the blast radius associated with an incident.

What are the most common AI security risks?

AI has spurred an avalanche of new risk categories that are virtually foreign to traditional security models. Here are some of the most common AI security risks that you need to have on your radar.

Prompt injection and insecure outputs

Prompt injection occurs when an attacker puts malicious instructions into the prompt to circumvent the instructions provided to the AI system. Direct prompt injection means entering those instructions straight into the AI system. Indirect prompt injection includes embedding malicious code, text, or even emojis in the content of a web page or email that the AI system processes. Whether direct or indirect, malicious prompts can produce dangerous outputs and trigger unintended actions after processing.

‍

Illumio's Paul Dant demonstrated this firsthand when testing ChatGPT's guardrails. “What I've found is that if you really put an educational spin on your prompts, it will more than likely not see what your ultimate intentions are and actually help you automate the creation of a ransomware script.”

Data poisoning and model manipulation

Malicious actors can corrupt an AI’s training data, fine-tuning data, or retrieval data to gradually shift how the model behaves. For example, a corrupted dataset could train an AI model to classify threats incorrectly, disclose confidential information, or produce biased results. Unlike traditional security threats, the damage isn’t always visible.

Sensitive data exposure

Sensitive information can leak from an AI system at many points. For instance, a malicious actor can obtain sensitive information via a prompt, a response, log files, embeddings, a training dataset, or through a connection to another application.

‍

Michael Adjei, Illumio's Director of Systems Engineering for EMEA, explained just how exposed a typical AI system is. “Data gets pushed into the input layer and moves to the hidden layer where the AI 'magic' happens. Then, it ends up at the output layer where the consumer can interface with it.” Asked where vulnerabilities can appear in that process, Adjei didn't hesitate. “Pretty much at every stage.”

Model theft and model inversion

AI models represent a significant investment, which makes them an attractive target. Attackers can steal proprietary model architecture and weights l outright, or use model inversion to infer the sensitive data used during training.

AI supply chain risks

Each third-party model, dataset, library, plugin, or API your organization relies on is a potential attack vector. An open-source dependency can be compromised, and a pre-trained model can also be tampered with —  introducing vulnerabilities into your environment well before deployment.

Excessive agency and insecure integrations

AI agents that can collect sensitive data, invoke external tools, or perform actions autonomously require strict guardrails. Overly permissive behavior turns a single compromised agent into a springboard for further exploitation of systems accessible by that agent.

Shadow AI and misconfiguration

Unauthorized AI tools and unknown models create blind spots you can’t defend. Weak cloud configuration and exposed endpoints create additional blind spots.

‍

As Adjei put it, “Many CISOs’ and security leaders' first response has been, 'AI is being used by the bad guys, so the good guys also need more AI.' But that's not necessarily true.”

Infrastructure compromise and lateral movement

Once an attacker has gained control of an AI workload, the concern is what comes next. All associated data stores, credentials, applications, and other workloads become available once the initial compromise has occurred, allowing a single breach to rapidly move across your entire environment.

‍

Dant's advice for getting ahead of this risk is refreshingly simple: “The first principle is to go back to basics. Ultimately, good cyber hygiene, lateral movement prevention, and Zero Trust principles will become even more valid going into the future.”

Model drift and availability attacks

Real-world data evolves over time and drifts away from training data, resulting in less reliable outputs and poor security decisions. Malicious actors can accelerate degradation through targeted resource exhaustion and denial-of-service attacks on AI infrastructure, directly affecting availability at the exact moment you need it most.

How is AI used in cybersecurity?

Security teams treat AI as another player on the SOC bench, processing volumes of data at speeds no single human analyst could match. Here's how AI pays off across specific use cases.

‍

• Threat and anomaly detection: AI identifies changes in network behavior that often indicate a breach before a human can identify them.
• Behavioral analysis: Machine learning establishes a baseline of user and machine behavior, allowing AI to flag deviations from that baseline.
• Alert correlation and prioritization: AI correlates similar signals across different tool sets and determines the relative level of severity among those correlated signals. This cuts down on alert fatigue.
• Threat hunting: AI will present potential patterns and relationships (which may be hidden to the human eye) that provide direction to the analyst to pursue threats they were not otherwise investigating.
• Phishing and fraud detection: AI models use natural language processing, behavioral characteristics of senders, and metadata to detect phishing and fraudulent emails/transactions that have evaded other filtering mechanisms.
• Vulnerability prioritization: AI uses actual vulnerability exploitation history to rank vulnerabilities by severity, rather than theoretical severity.
• Security investigation summaries: AI condenses complex logs and alerts into summaries analysts can act on.
• Automated incident response: AI isolates compromised workloads or revokes access at the time it recognizes a threat.

What AI security best practices?

AI security is about applying well-established security best practices to this new class of IT assets (i.e., models, applications, agents, APIs, datasets, and workloads). Don’t create new security policies — extend the ones you have.

• Create a comprehensive inventory: Document all of your AI models, apps, agents, APIs, datasets, and workloads. If you do not document it, you cannot secure it.
• Classify your AI assets: Prioritize your AI assets based on their value, exposure, and potential impact on the organization; this will help identify which assets should be secured first.
• Use least privilege access: Restrict access to only those who need it to perform tasks, whether that’s people or machines. That includes the non-human identities AI and ML introduce.
• Segment your AI workloads: Isolate AI systems from sensitive data and critical infrastructure to prevent a compromised model from becoming a backdoor into other areas.
• Encrypt all data: Encrypt data both while it is in motion and when it is stored, throughout the entire AI pipeline.
• Do not put secrets in AI/ML systems: Do not include credentials, API keys, or regulated data within AI prompts, source code, or logs. These items could potentially expose your organization if leaked.
• Validate your supply chain: Verify the integrity of your training data, model sources, libraries, and third-party dependencies prior to introducing them into your environment.
• Implement guardrails for generative AI tools: Deploy input controls, output controls, and tool use controls to detect manipulations prior to potential harm being caused.
• Continuously monitor: Closely monitor the behavior of your models, API activity, workload communications, and configuration changes in real time, not just during deployment.
• Develop response plans: Develop plans outlining how to respond in the event of a compromised model, exposed data, or impacted infrastructure.
• Red team your models: Perform regular adversarial testing and threat modeling against your AI models, not just once before deployment.
• Require human oversight: Ensure human approval is required prior to allowing automated actions that result in significant consequences or irreparable impacts to production systems.
• Adhere to established frameworks: Map your program to established frameworks, such as the NIST AI Risk Management Framework and OWASP's LLM and Agentic AI Top 10 lists. Stay informed regarding applicable regulatory requirements.

How does Zero Trust strengthen AI security?

Zero Trust replaces the implicit trust inherent in legacy systems with continuous verification and least-privilege access for every AI user, service, workload, and dataset. Nothing gets a free pass just because it's already inside your network.

Segmentation puts Zero Trust into practice, separating critical AI assets, models, training data, and associated applications from the rest of your environment. If an attacker takes control of one workload, segmentation limits how far they can go. Continuous monitoring enhances this, revealing malicious communications or behaviors that indicate a problem exists.

While Zero Trust itself cannot prevent all attacks on your AI system, it can contain them. You’re exposed to model-specific exploits like prompt injection, but Zero Trust limits the damage when one lands. Zero Trust protects the infrastructure around your AI system so a breach has a smaller blast radius.

How Illumio supports AI security

To keep up with the pace of AI, security teams need visibility and answers, fast.

‍

Illumio's breach containment platform provides the clarity and control that AI environments demand. When suspicious activity emerges, Illumio's Zero Trust Segmentation isolates the affected workload immediately, without requiring an agent, limiting how far an attacker can spread. This containment capability is what separates a controlled incident from a full-scale breach.

‍

While other tools focus on preventing model-specific attacks, such as prompt injection, Illumio protects the infrastructure surrounding your AI systems, along with the rapid, AI-powered breach containment capabilities your team needs to reduce impact and maintain business continuity if an incident occurs.

よくある質問(FAQ)

What is AI security in simple terms?

Short for artificial intelligence security, AI security is the practice of protecting your AI and using AI to protect everything else. AI security focuses on protecting the AI systems, models, and data your organization relies on from theft, tampering, and misuse, while also using AI technologies to strengthen your broader defenses.

What is the difference between AI security and cybersecurity?

Cybersecurity protects your entire IT environment, including networks, applications, endpoints, and data. AI security is a specialized subset of cybersecurity focused specifically on the risks introduced by AI systems, such as prompt injection, model theft, and data poisoning. Every organization using AI needs to extend its cybersecurity practices to cover it.

What is the difference between AI security and AI safety?

AI security protects AI systems from external threats, such as hackers, data breaches, and adversarial attacks. AI safety protects against internal risks like biases in outputs, hallucinations, or unintended harmful behavior (regardless of whether a system was attacked). One defends against attackers. The other defends against the AI system itself causing problems.

What is generative AI security?

Generative AI security oversees the systems and data behind AI platforms that generate content (e.g., text, code, and images from ChatGPT or Perplexity) to prevent misuse or manipulation. This discipline focuses on securing the models and pipelines themselves as well as the sensitive data flowing through prompts and outputs during real-world use.

Can AI replace cybersecurity professionals?

No. AI can automate repetitive tasks like log analysis and alert triage, but it can't replace a human analyst’s strategic judgment and contextual reasoning. The emerging trend is that AI is reshaping cybersecurity roles rather than fully eliminating them. This is giving space for professionals to move toward higher-value strategic tasks.

What are the biggest AI security threats?

The most pressing threats include prompt injection, data poisoning, sensitive data exposure, model theft, AI supply chain vulnerabilities, and shadow AI operating outside IT's visibility. Attackers are also using AI to accelerate reconnaissance, phishing, and malware development, raising the speed and scale of traditional attacks.

How can organizations secure their AI systems?

Organizations need to take a thorough inventory of their AI stack and classify each application or platform by risk. From there, enforce least-privilege access and segment AI workloads from sensitive data. Other considerations that round out a strong AI security program include layering in encryption, continuous monitoring, adversarial testing, and human oversight for consequential decisions.

Which frameworks help organizations manage AI security risk?

The NIST AI Risk Management Framework is an industry standard AI security framework that offers structured guidance for identifying and managing AI-specific risks across the model lifecycle. Additionally, OWASP's Top 10 for LLM Applications is a great resource for addressing risks like prompt injection and insecure output handling. Frameworks like ISO 42001 and MITRE ATLAS are worthwhile reads for a comprehensive governance approach. It’s also worth reading about Mythos AI and brushing up on the next evolution of frontier AI.