What is AI Security?

Lucia Stanham - June 11, 2024

AI is poised to transform every industry and sector. As businesses oversee day-to-day operations, make critical decisions, or handle sensitive data, AI-based systems are playing an increasingly prominent role. However, with great innovation comes the potential for great risk. Enabling safe and secure adoption of AI has never been more important.

Building AI applications and infusing AI across operations presents security challenges that organizations need to address. Ensuring the security of your AI applications is essential for protecting your applications and processes against sophisticated cyber threats and safeguarding the privacy and integrity of your data.

In this post, we’ll cover the essentials of AI security. Then, we’ll look at common threats to AI systems, followed by key components and best practices in their security. By the end, you’ll have a clear understanding of how to keep your AI endeavors secure and resilient against threats.

Understanding AI security

When we talk about AI security, we’re referring to the practices and principles that protect AI systems, data, and infrastructure. Securing AI projects is essential for several reasons:

  • The growing ubiquity of AI across modern enterprises: AI technology is no longer a futuristic concept for the realm of science fiction. The age of AI has already begun. AI applications are readily accessible and are being used to drive business processes and decisions across various sectors. With growing adoption comes a higher potential impact if AI systems are attacked or if their vulnerabilities can be successfully exploited.
  • The increasing use of AI to inform data-driven decisions and recommendations: AI systems process vast amounts of data to identify patterns, make predictions, and automate decisions. The role that AI plays in informing operational decisions makes AI systems attractive targets for cyberattacks aiming to modify or alter the expected behavior of AI systems, known as adversarial AI.
  • The challenges of identifying and troubleshooting compromised AI systems: Many AI models have an inherent opacity — referred to as their “black box” nature — and this presents some unique challenges. Because modern AI models are so incredibly complex, detecting how or when they’ve been compromised can be very difficult. An attacker could manipulate an AI system in subtle ways that remain undetected for long periods, potentially leading to significant consequences.

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Common threats to AI systems

As AI is integrated across critical operations, understanding the threats it faces is essential. Here are some of the most common threats AI systems face:

  • Data threats: Great AI begins with great data. Data is foundational to training and testing effective AI systems. This is why modifying or manipulating data fed into AI systems, known as data poisoning, can be so crippling to AI systems — it can corrupt the reliability of their outputs.
  • Data pipeline threats: A compromised data pipeline can grant malicious actors unauthorized access to sensitive training data, which can lead to significant privacy breaches and other ripple effects across AI systems.
  • Model threats: AI models are vulnerable to attacks designed to exploit their learning process, often resulting in alterations to their performance designed to produce incorrect decisions.
  • Infrastructure vulnerabilities: The infrastructure supporting AI systems — from development environments to deployment platforms — can also be the targets of exploits. Attacks on these environments can disrupt AI operations, and compromised APIs and integration points can serve as gateways for broader cyberattacks.
  • Insider threats: The human element poses a significant risk that is often overlooked. Whether it’s a malicious attacker on the inside or unintentional human error from a well-meaning employee, the result can be the exposure or compromise of your AI systems and data.
  • Data privacy and compliance challenges: AI systems frequently handle sensitive information, which means the risk of inadvertently breaching privacy laws or regulations is high. Ensuring that data is not only secure but compliant with global standards is a persistent challenge for organizations.

How should your organization recognize and prepare for these common threats? Let’s consider some key components of AI security.

Key components of AI security

Securing AI projects requires a comprehensive approach that addresses the broad attack surface of AI systems. We’ll walk through this approach one attack vector at a time.

Data security involves not just safeguarding data from unauthorized access but ensuring data integrity throughout its life cycle. What does this realm of security entail?

  • Encryption for data at rest and in transit
  • Anonymizing data where possible
  • Applying strict access controls to prevent data leaks or breaches
  • Ensuring privacy and compliance

The underlying models that power AI systems — whether for machine learning, deep learning, or other AI technologies — are valuable not just for their functionality but for the proprietary insights they can provide. Therefore, model security, which aims to protect these models from theft and reverse engineering, is an imperative. Equally important is safeguarding them against attacks that seek to manipulate their behavior or decision-making. Regularly auditing and testing models for vulnerabilities can help identify and mitigate these risks.

Infrastructure security protects the underlying infrastructure that hosts AI systems — from on-premises servers to cloud environments — against both physical and cyber threats. This includes:

  • Ensuring secure development and deployment environments
  • Protecting the APIs and integration points through which AI systems interact with other applications and services
  • Other security measures, such as network segmentation, firewalls, and intrusion detection systems

By focusing on these key components, organizations can create a layered security strategy that protects their AI projects from various angles.

Best practices for securing AI projects

In this section, we’ll focus on three best practice recommendations for enhancing the security posture of your AI systems.

Recommendation #1: Secure data handling

Securely handling data involves anonymizing and encrypting sensitive information to protect privacy and integrity. Anonymization helps minimize the risk of personal data exposure. This practice also reduces your compliance burden.

Encryption makes data inaccessible to unauthorized parties. Implementing strong access controls and monitoring data usage are key steps in preventing data breaches and ensuring ethical use within AI projects.

Recommendation #2: Robust model development

If you want to guard against model theft and adversary manipulation, then you’ll need to implement measures to protect your AI models. This involves:

  • Adopting secure coding practices to eliminate vulnerabilities
  • Conducting regular audits
  • Conducting penetration tests to identify and fix security gaps

Such measures ensure your AI models are resilient to attacks and function as intended.

Recommendation #3: Deployment and monitoring

After completing the development of your AI project, it’s time to think about secure deployment and ongoing monitoring. These two aspects are extremely important. You’ll need to verify the model and its environment’s integrity at deployment, continuously scanning for potential threats. Effective monitoring helps detect anomalies early, maintaining the security of AI systems against evolving threats.

The role of governance and compliance

As with any other software projects that use data, your AI projects must also operate within the boundaries of legal and regulatory standards. Because of this, your AI projects will need robust governance and compliance frameworks. Understanding and adhering to relevant legal requirements and standards — such as the GDPR, CCPA, or HIPAA — ensures that your AI projects not only respect privacy and data protection laws but build trust with users and stakeholders.

What does it look like to develop a governance framework? Set clear policies and procedures for AI development and use, emphasizing ethical considerations and accountability. In the end, your framework will guide decision-making, help manage risks, and ensure that AI systems operate transparently and fairly.

By prioritizing governance and compliance, organizations can mitigate legal risks, protect their reputation, and ensure their AI projects contribute positively and ethically to their goals.

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Additional considerations for responsible AI

In addition to thinking about securing your AI systems, it’s important to consider the broader implications of AI technology on society and individuals. Keep in mind the following additional considerations for responsible AI development and deployment.

Guardrails for data privacy

Implementing strict guardrails around data privacy helps ensure that personal and sensitive information is protected throughout the AI life cycle. Along with encryption and anonymization techniques, establish policies to limit data access and usage to necessary contexts only.

Minimizing bias in model training data

Bias in training data can lead to unfair or discriminatory outcomes. Actively work to identify and minimize bias to create more equitable AI systems. This includes diversifying data sources and employing techniques to detect and correct biases in datasets.

Preventing unauthorized data exposure with role-based access control

Role-based access control (RBAC) is an approach for managing who has access to what data within a system. By defining roles and permissions clearly, RBAC helps prevent unauthorized access to sensitive information in your AI system, thereby reducing the risk of data breaches and exposure.

“Human in the loop” controls and reviews

Incorporating human oversight into AI operations can significantly reduce the risks associated with automated decision-making. “Human in the loop” controls allow humans to review critical decisions and intervene when necessary. This helps ensure that AI actions align with ethical standards and do not cause unintended harm.

Secure your AI with the CrowdStrike Falcon platform

In this post, we’ve underscored the importance of securing AI systems by applying best practices across the various attack vectors of AI systems.

CrowdStrike applies many of these best practices throughout the development of AI systems within the CrowdStrike Falcon® platform. The Falcon platform enables organizations to secure their environments with an AI-native platform that delivers advanced threat intelligence, real-time monitoring, and automated response capabilities.

To learn more about the Falcon platform, sign up to try it for free, or contact our team of experts today.


Lucia Stanham is a product marketing manager at CrowdStrike with a focus on endpoint protection (EDR/XDR) and AI in cybersecurity. She has been at CrowdStrike since June 2022.