AI Cyber Security: Securing AI Systems Against Cyber Threats

Cyber security in AI refers to the measures and technologies designed to protect artificial intelligence systems from cyber threats and ensure their secure operation. This includes safeguarding the data AI systems are trained on, protecting the integrity of AI algorithms, and ensuring that AI applications are not used for malicious purposes. 

As AI technologies become increasingly integrated into various aspects of digital infrastructure, the importance of cyber security measures tailored to these technologies grows. The goal is to prevent unauthorized access, manipulation, or misuse of AI systems, which could lead to privacy breaches, misinformation, or other forms of cyberattacks.

The field encompasses a broad range of activities, from securing the data pipelines that feed into AI models, to protecting the models themselves from tampering and theft. Given the complexity of AI systems and their potential to process sensitive information, cyber security in AI also involves ethical considerations, such as ensuring that AI systems do not inadvertently violate privacy rights or become biased in their decision-making processes. 

As AI systems become more powerful and autonomous, the challenge of maintaining robust security measures that can adapt alongside these evolving technologies becomes increasingly critical.

Security Risks Facing AI Systems

Here are some of the emerging threats facing modern AI systems:

Prompt Injection

Prompt injection is an attack vector specific to AI models based on natural language processing (NLP). It involves manipulating the input given to an AI system to trigger an unintended action or response. This can be especially problematic in large language models (LLMs) where the injected prompts can lead to the generation of biased, inaccurate, or malicious content. The challenge lies in the model’s inability to discern the malicious intent behind the inputs, leading to potential misuse or exploitation.

Mitigating prompt injection attacks requires robust input validation and context-awareness mechanisms. AI developers must implement safeguards that can detect and neutralize attempts to manipulate model outputs. This might include monitoring for unusual input patterns or incorporating logic that recognizes and rejects inputs designed to exploit the model’s vulnerabilities.

Evasion Attacks

Evasion attacks are a form of cyber threat where attackers manipulate the input data to AI systems in a way that causes them to make incorrect decisions or classifications. These attacks are particularly concerning because they exploit the model’s vulnerabilities without necessarily altering the model itself or the underlying algorithm. 

For example, in the context of image recognition, attackers could slightly alter an image in a way that is imperceptible to humans but leads the AI to misclassify it, such as mistaking a stop sign for a yield sign in autonomous vehicle systems. The danger of evasion attacks lies in their subtlety and the ease with which they can be executed, requiring only modifications to the input data.

To counter evasion attacks, developers can use techniques such as adversarial training, where the model is exposed to a variety of manipulated inputs during the training phase. Additionally, implementing continuous monitoring and analysis of the inputs and outputs of AI systems can help detect and mitigate evasion attempts.

Training Data Poisoning

Training data poisoning involves introducing malicious data into the dataset used to train an AI model. This can lead to compromised models that behave unpredictably or in a manner beneficial to an attacker, such as by misclassifying malicious activities as benign. The subtlety of this attack makes it particularly dangerous, as the poisoned data may not be readily identifiable amidst the vast quantities of training data.

Protecting against data poisoning requires careful curation and validation of training datasets, as well as techniques like anomaly detection to identify and remove suspect data. Ensuring the integrity of training data is crucial for maintaining the reliability and security of AI models.

Model Denial of Service

Model denial of service (DoS) attacks aim to overwhelm AI systems, rendering them unresponsive or significantly slower by inundating them with a high volume of requests or complex data inputs. This can disrupt AI services, affecting their availability for legitimate users and potentially causing critical systems to fail.

Defending against model DoS attacks involves implementing rate limiting, monitoring for unusual traffic patterns, and designing scalable systems to deflect sudden surges in demand. This ensures that AI services remain available and reliable, even under attack.

Model Theft

Model theft refers to unauthorized access and extraction of AI models, often with the intent to replicate or reverse-engineer proprietary technologies. This not only poses a direct financial risk to organizations that invest in AI development, but also a security risk if the model is used to identify vulnerabilities for further attacks.

Securing AI models against theft involves a combination of access controls, encryption, and potentially watermarking models to trace unauthorized use. Ensuring that models are protected both at rest and in transit is essential for safeguarding intellectual property and maintaining the integrity of AI systems.

Tips from the expert

Steve Moore is Vice President and Chief Security Strategist at Exabeam, helping drive solutions for threat detection and advising customers on security programs and breach response. He is the host of the “The New CISO Podcast,” a Forbes Tech Council member, and Co-founder of TEN18 at Exabeam.

In my experience, here are tips that can help you better enhance cybersecurity measures for AI systems:

Rotate and harden encryption keys for model and data storage
Frequently rotate encryption keys and apply hardware security modules (HSMs) for storing model artifacts and datasets, reducing the risk of model theft or unauthorized access.

Implement differential privacy for data protection
Apply differential privacy techniques when training AI models to ensure sensitive data cannot be reverse-engineered from the model. This helps protect the privacy of individual records while maintaining the model’s utility.

Use adversarial example detection tools
Implement tools that actively monitor and identify adversarial inputs designed to mislead AI systems. By training a secondary model to detect anomalies in input data, you can safeguard systems against subtle evasion attacks.

Leverage model explainability to detect anomalies
Use explainability techniques like SHAP or LIME to continuously validate AI decision-making processes. Sudden deviations in feature importance or decision paths could indicate tampering or exploitation attempts.

Employ federated learning for sensitive environments
In industries like healthcare or finance, federated learning allows AI models to be trained across decentralized data sources without sharing sensitive data, reducing exposure to data poisoning risks.

Which AI Systems Are Most Vulnerable to Attack? 

Any AI system used for sensitive or mission critical purposes requires security measures. But some AI systems are more vulnerable than others. Here are a few systems that require special cybersecurity protection:

Large Language Models (LLMs)

Large Language Models (LLMs) like OpenAI’s GPT (Generative Pre-trained Transformer) and Google’s Gemini have transformed the AI landscape, offering advanced capabilities in natural language understanding and generation. Securing LLMs is paramount, as they can process and generate vast amounts of information, some of which may be sensitive or proprietary, and can be used to spread misinformation or perform sophisticated social engineering. 

Ensuring the security of LLMs involves preventing unauthorized access, protecting the data they are trained on, and ensuring that the models are not manipulated to produce biased or harmful outputs. One aspect of LLM security is to implement fine-grained access controls and use encryption to protect user data. Additionally, monitoring the inputs and outputs of LLMs for signs of manipulation or bias can help maintain their integrity.

Autonomous Vehicles

Autonomous vehicles rely heavily on AI systems for navigation, obstacle detection, and decision-making. These systems, which include computer vision, sensor fusion, and machine learning algorithms, are prime targets for cyber-attacks due to their critical role in safety and mobility. An attack on an autonomous vehicle’s AI could lead to misinterpretation of sensor data, leading to incorrect navigation decisions, or even accidents. Given the potential for physical harm, securing these systems is of utmost importance.

Protecting autonomous vehicles from cyber threats involves multiple layers of security, including the encryption of data transmissions between the vehicle and control centers, robust authentication mechanisms to prevent unauthorized access, and real-time monitoring for signs of cyber-attacks. Additionally, implementing fail-safes that can take control in case of a detected threat, and ensuring redundancy in critical systems, can help mitigate the impact of potential breaches.

Financial AI Models

Financial AI models are used for a wide range of applications, from algorithmic trading and fraud detection to credit scoring and personalized banking services. These systems handle sensitive financial data and make decisions that can have significant economic implications. As such, they are attractive targets for attackers looking to manipulate market conditions, steal sensitive data, or commit financial fraud. The vulnerability of financial AI systems can lead to financial losses, erosion of customer trust, and regulatory penalties.

Securing financial AI models involves implementing stringent data protection measures, such as encryption and access control, to safeguard sensitive information. Regular audits and monitoring are essential to detect and respond to suspicious activities promptly. Additionally, financial institutions should employ AI systems that are transparent and explainable, allowing for the easy identification and correction of biases or errors that could be exploited by attackers.

Healthcare AI Systems

Healthcare AI systems, used in diagnostics, treatment recommendations, patient monitoring, and drug discovery, handle highly sensitive personal health information (PHI). The vulnerability of these systems to cyber-attacks can lead to privacy breaches, incorrect medical advice, and even endanger patient lives.

To secure healthcare AI systems, it is vital to comply with healthcare regulations and standards for data protection, such as HIPAA in the United States, which mandates strict controls on the access, transmission, and storage of PHI. Encryption, secure authentication, and regular security assessments are critical components of a comprehensive cybersecurity strategy. 

Additionally, healthcare organizations should invest in training for staff to recognize and prevent cyber threats and ensure that AI systems are transparent and have mechanisms in place for detecting and correcting inaccurate AI recommendations.

AI Security Regulations Around the World 

Governments are waking up to the risks raised by AI systems, and are working on legislation and guidance to ensure they are safe. Here are a few regulations that impact the security of AI systems:

European Union AI Act

The European Union AI Act represents a comprehensive framework aimed at regulating the deployment and use of artificial intelligence across EU member states. It categorizes AI systems according to their risk levels, imposing stricter requirements on high-risk applications, such as those impacting public safety or individuals’ rights. The act emphasizes transparency, accountability, and the protection of citizens’ rights, requiring AI developers and deployers to adhere to specific standards regarding data quality, documentation, and human oversight.

This regulatory approach seeks to ensure that AI technologies are used in a manner that is safe, ethical, and respects privacy and fundamental rights. For organizations operating within the EU, compliance with the AI Act involves conducting risk assessments, implementing robust data governance practices, and ensuring that AI systems are transparent and understandable to users. The act sets a precedent for AI regulation, potentially influencing similar initiatives globally.

European Commission Guidelines for Trustworthy AI

The European Commission’s Guidelines for Trustworthy AI outline key principles for developing and deploying AI systems in a way that earns users’ trust. These principles include transparency, fairness, accountability, and respect for user privacy and autonomy. The guidelines emphasize the importance of ethical considerations in AI development, urging organizations to ensure their AI systems are human-centric and aligned with societal values and norms.

Adhering to these guidelines involves conducting ethical impact assessments, engaging with stakeholders to understand their concerns, and implementing mechanisms to address potential ethical issues. The guidelines serve as a voluntary framework, encouraging organizations to adopt responsible AI practices that contribute to the development of technology that is beneficial for society as a whole.

U.S. Algorithmic Accountability Act (AAA)

The U.S. Algorithmic Accountability Act (AAA) is proposed legislation aimed at regulating the use of automated decision-making systems, including AI, to prevent discrimination and ensure fairness. The act would require companies to conduct impact assessments of their AI systems, evaluating risks related to privacy, security, and biases, particularly in areas such as employment, housing, and credit. The goal is to hold organizations accountable for the outcomes of their AI systems, ensuring they do not perpetuate unfair practices or harm vulnerable populations.

Compliance with the AAA would involve transparent documentation of AI decision-making processes, regular audits to identify and mitigate biases, and the implementation of safeguards to protect individuals’ rights. While the act is still in the proposal stage, its consideration reflects growing concerns about the impact of AI on society and the need for regulatory oversight to ensure ethical and equitable use of AI technologies.

U.S. National Artificial Intelligence Initiative Act

The U.S. National Artificial Intelligence Initiative Act is part of a broader effort to promote the development of AI in the United States while ensuring appropriate governance. The act aims to accelerate AI research and development, establish standards for AI systems, and ensure that the United States remains a leader in AI innovation. It emphasizes the importance of collaboration between government, industry, and academia to advance AI technologies while addressing ethical, legal, and societal implications.

The initiative supports the creation of AI research institutes, the development of AI-related educational programs, and the establishment of guidelines for ethical AI use. Organizations involved in AI development can benefit from participating in this initiative by accessing research funding, collaborating on standards development, and contributing to the formulation of policies that guide the responsible use of AI.

How to Prevent AI Attacks 

Implement AI Security Standards

Implementing AI security standards is crucial for mitigating risks associated with AI systems. This involves adopting recognized security protocols and frameworks that guide the development, deployment, and maintenance of AI applications. 

Standards such as the ISO/IEC 27001 for information security management help ensure that AI systems are developed with security in mind, from data handling to access controls. By adhering to these standards, organizations can create a secure environment for AI operations, reducing vulnerabilities to cyber threats.

Control Access to AI Models 

Controlling access to AI models is essential to prevent unauthorized use and tampering. This means setting up strict access controls and authentication mechanisms to ensure only authorized personnel can interact with AI systems. 

Implementing role-based access control (RBAC) and multi-factor authentication (MFA) can help in securing AI models against unauthorized access, providing an additional layer of security by verifying user identities and restricting access based on user roles and permissions.

Secure the Code

Securing the code of AI applications involves implementing best practices in software development to minimize vulnerabilities and prevent potential attacks. This includes regular code reviews, vulnerability assessments, and the use of secure coding standards. 

Additionally, adopting DevSecOps practices can integrate security into the software development lifecycle, ensuring that security considerations are addressed early and throughout the development process. By securing the code, organizations can protect AI applications from exploits and reduce the risk of security breaches.

Consult with External Security Experts

Consulting with external security experts can provide valuable insights and expertise to enhance the security of AI systems. External experts can offer a fresh perspective on potential vulnerabilities and recommend best practices and innovative solutions to mitigate risks. They can also assist in conducting thorough security assessments and penetration testing to identify and address security gaps.

Encrypt Model Data

Encrypting model data is crucial to protect the integrity and confidentiality of the information processed by AI systems. Encryption ensures that data, both at rest and in transit, is unreadable to unauthorized individuals. Applying strong encryption algorithms and managing encryption keys securely can safeguard sensitive data from interception and unauthorized access. 

Exabeam: Enhancing Threat Detection with Advanced Security Analytics

The Exabeam Security Operations Platform delivers a powerful combination of SIEM, behavioral analytics, automation, and network visibility to transform how organizations detect, investigate, and respond to threats. By correlating firewall logs with data from endpoints, cloud environments, identity systems, and other security sources, Exabeam provides deeper insights into evolving threats that would otherwise go undetected.

Behavior-driven analytics enable Exabeam to go beyond static rules and signatures, identifying anomalous activity that indicates credential misuse, insider threats, or lateral movement across the network. By analyzing normal user and entity behavior over time, Exabeam surfaces high-risk activities that traditional security tools may overlook.

Automated investigations streamline security operations by linking disparate data points into comprehensive threat timelines, reducing the time analysts spend piecing together incidents manually. This allows teams to quickly identify the root cause of an attack and respond with precision.

Learn more about Exabeam SIEM