What Are AI Security Concerns?

Growing adoption of AI introduces new security risks for businesses as companies deploy AI models, copilots, and automation within corporate security operations and IT workflows. AI tools are increasingly trusted to make critical decisions regarding the management of IT resources and responding to potential attacks.

AI systems are useful tools; however, they’re not perfect. AI systems can make mistakes and can be targeted by attacks designed to manipulate them and their decision-making. Understanding the risks associated with AI is essential to implementing security controls and governance policies to manage them.

What Types of Risks Fall Under AI Security Concerns?

AI-powered tools carry a range of potential security concerns. Three of the most significant are attackers manipulating the AI model, crafted prompts tricking an AI tool into making incorrect decisions, and data leaking from AI tools.

Model Poisoning and Misinformation Injection

AI models are trained by feeding them training data and allowing them to extract information and patterns from this dataset. In general, the quality of the AI model depends on the quality of the data provided to it. Attackers can exploit this by corrupting training data to introduce incorrect information, either during initial training or ongoing learning. By doing so, they can change the underlying model to make incorrect decisions, potentially leading to false positive or false negative detections.

Most AI models aren’t explainable, making it difficult to determine how they reached a particular conclusion and whether that thought process was correct. For this reason, identifying and filtering out incorrect or corrupted training data before it is encoded in the model is essential to managing this risk.

Model Manipulation and Prompt Injection

Model manipulation and prompt injection attacks target production AI systems, attempting to steer them toward incorrect classifications or bad advice. These attacks involve the attacker abusing their understanding of how a model works to create a prompt that causes unanticipated results in the AI model.

With AI, outputs can vary greatly based on the details of the prompt and other factors, such as the temperature (level of desired randomness) of the outputs. By taking advantage of this fact, attackers may be able to identify and exploit a loophole in AI guardrails or force the AI model into an undesirable state.

Data Exposure and Sensitive Information Leakage

AI systems commonly have access to highly sensitive information as a key part of their ability to provide benefits to the business. Some data access may be intentional, but employees may also enter or copy-paste sensitive information into AI tools, both internal and external.

This access to sensitive data introduces the risk of data leaks via logs, training artifacts, poorly governed chat interfaces, and ongoing training of AI systems. Organizations should manage AI access to sensitive data and implement data loss prevention (DLP) to ensure compliance with regulatory requirements.

How Do AI Security Issues Affect SOC Workflows?

AI tools increasingly support security operations center (SOC) workflows by helping to automate and streamline alert triage and correlation, incident investigation, and reporting. While these tools can help to reduce noise, they can also make mistakes, overlooking vital information or making incorrect decisions. For this reason, AI should be used to enhance the effectiveness of human analysts rather than replacing them entirely with automation.

Triage Acceleration With AI Copilot Support

For SOC teams, AI copilots work as assistants, summarizing alerts, recommending next steps, and supporting investigations. This reduces the workload associated with incident triage and investigation and reduces mean time to response (MTTR).

Copilots are well-suited to well-known and common situations, such as large volumes of repetitive alerts with clear patterns. A copilot can identify deviations from this pattern that indicate potential threats while filtering out the rest of the noise. However, copilots are less suited to situations where an organization is facing a rare, high-impact incident. In these situations, even small details can be important to proper threat triage and response, but may be accidentally filtered or summarized away by an AI copilot.

Analyst Over-Reliance and Decision Drift

AI copilots are useful tools, and analysts can grow dependent on them over time. This becomes dangerous if AI recommendations are accepted and approved without proper review, especially when analysts are operating under time pressure.

AI security tools can produce incorrect recommendations due to hallucinated indicators, severity misclassifications, and a lack of important context about an organization’s environment. Once an AI frames the narrative, human analysts may unconsciously suffer from confirmation bias, looking for evidence that supports the theory rather than trying to determine the true root cause. While AI can be useful for streamlining response processes, its recommendations shouldn’t be treated as 100% accurate.

What Governance Controls Reduce AI Security Concerns?

Exploiting AI requires attackers to interact with AI training data or a production model. Strong governance can help to alleviate AI security concerns by controlling access to sensitive resources and monitoring interactions for indicators of suspicious or malicious activity.

Access Controls and Policy Enforcement

Strong access controls are the foundation of an AI security policy, limiting access to AI data sources and tools to authorized users. Role-based access control (RBAC) implements this by assigning access based on what a user needs to fulfill their role in the business.

Access controls can be augmented by policies that define what legitimate users can do with AI tools. This includes restricting the type of data that can be entered into AI tools, reducing the risk and impact of potential data leaks.

Monitoring and Behavioral Evaluation

In addition to preventing threats to AI systems, organizations also need the capability to detect and remediate them. For this reason, logging prompts, responses, and key decisions tied to AI recommendations is essential to provide access to the data needed for incident investigation and response.

These logs should be monitored for suspicious anomalies or patterns, such as attempted prompt injection or unusual data access. Additionally, the enterprise should periodically evaluate model behavior against known test cases to identify model drift and perform red teaming to detect potentially exploitable weaknesses.

What Enterprise Scenarios Increase Exposure?

AI security risks are the greatest where AI plays a major role in the business, automating critical or high-risk activities, such as incident response workflows, customer-facing tools, or identity and access management (IAM). Risks are also heightened for unknown use of AI, such as shadow AI, where employees use AI tools without proper authorization or management.

High-Impact Automation Pipelines

Full automation can dramatically streamline various processes, reducing the load on security personnel. For example, an AI may identify a potential threat or inspect a ticket, make changes to remediate the issue, and close the issue.

This becomes problematic if the AI makes mistakes. This could result in missed incidents, inappropriate access management, or breaking changes to the organization’s IT environment. For this reason, automation of critical or high-risk processes should maintain a human in the loop or strong safeguards against accidents.

Unvetted Shadow AI Usage

Shadow AI is the use of AI tools in the business without formal approval or oversight. Employees may adopt convenient “free” tools, allowing them to process sensitive information.
When security teams only learn about this practice after the fact, shadow AI risks can result in data leaks, missing audit trails, inconsistent policy enforcement, and regulatory non-compliance. For this reason, enterprises should proactively search for unauthorized AI usage and deploy controls designed to prevent sensitive data from being entered into these tools.

FAQs about AI Security Concerns

What Is the Difference Between AI Security and Traditional Cybersecurity?

AI security addresses security threats associated with AI tools, such as the potential for non-deterministic responses to similar inputs and attackers targeting and exploiting AI systems. This builds on top of traditional cybersecurity, integrating responses to these threats alongside existing controls for application, data, and identity security.

Are AI Copilots Safe for SOC Teams to Use?

The safety of AI copilots for SOC teams depends on how the tool is used, secured, and governed, rather than the tool itself. Copilots are well-suited to certain tasks, such as looking for anomalies or patterns in common alerts, but they may be less effective for rarer, higher-risk incidents. For this reason, copilots should be used to reduce toil and assist human operators while leaving human analysts in control of critical decisions and accountability.

How Can Organizations Detect Abuse or Misuse of AI Systems?

Logging interactions with AI systems, including prompts, access attempts, and training data access, can help provide insight into the use and abuse of these systems. These logs can be monitored and analyzed for anomalies or patterns of abuse, such as attempted prompt injection or unusual or inappropriate access to sensitive data. AI telemetry should integrate with existing SIEM, NDR, and SOC workflows, not live in a separate silo to provide valuable context and take advantage of existing security controls.

Why Do AI Security Concerns Matter Today?

As enterprises increasingly become reliant on AI and automation, the scope and impact of AI errors and attacks can grow. Allowing copilots and agents to make key decisions introduces the risk that their blind spots, hallucinations, and other failures could cause data breaches, downtime, and other negative impacts to the business.

As AI is integrated into everything, companies don’t have the option to “opt out” entirely. Instead, security teams must implement controls and policies to manage AI security risks. AI is helpful, but only with disciplined governance, monitoring, and clear accountability for human decision-makers.