What was once a futuristic concept portrayed in literature and movies has now become a tangible reality, embedded in our daily lives. AI, particularly in the realms of Machine Learning and generative AI, is poised to reshape our productive landscape and lifestyle. However, it’s crucial to understand the security risks accompanying this transformation.
In recent years, particularly in 2023, various organizations have ramped up efforts to address AI security risks and provide guidelines for successful prevention. The European Union Agency for Cybersecurity (ENISA) has released frameworks, methodologies, and reports focusing on AI security risks. Similarly, the US National Institute of Standards and Technology (NIST) has formulated a framework to manage AI security risks, and the OWASP Foundation has initiated a project dedicated to addressing AI security risks.
On the regulatory front, the European Union is progressing towards finalizing the Artificial Intelligence Act, highlighting the interconnectedness of cybersecurity and AI.
This article delves into the primary AI security risks that companies engaged in AI development need to consider to identify threats, prevent security incidents, and adhere to an increasingly stringent regulatory framework.
AI as a Crucial Ally in Cybersecurity
The year 2023 witnessed the prominence of generative AIs like ChatGPT, Midjourney, DALL-E, and Copy AI. These AIs, capable of generating content and responding to user requests, have garnered widespread attention.
However, the roots of AI trace back to Alan Turing, with decades of research in Machine Learning, neural networks, Deep Learning, and natural language processing.
AI is already embedded in numerous devices and technologies used by companies and individuals to automate tasks and enhance decision-making. It has become a vital ally for cybersecurity professionals, strengthening defensive capabilities, automating threat detection, optimizing security assessments, and predicting attack patterns.
1.1. AI in Cybersecurity
With the growing importance of AI in business and daily life, AI security risks have become a critical concern for cybersecurity. This includes cybercriminals targeting AI systems and increasing risks associated with the AI supply chain.
Data, Models, and Cyber Attacks: AI Security Risks
Fundamental to AI, especially Machine Learning and Deep Learning systems, is data. These systems rely on models trained with data. The quality and integrity of the data used for training are paramount for optimal performance.
This article distinguishes between threats targeting AI systems and the malicious use of AI tools for cyber attacks against software, systems, or individuals.
Risks to AI Systems
The OWASP project identifies various dangers, emphasizing potential attacks against AI models.
Data Security Risks
The AI pipeline is considered a new attack surface, incorporating data science. Robust security controls are crucial to prevent data leakage, intellectual property theft, or supply chain attacks.
Attacks Against AI Models
Protecting the AI development process, hiding model parameters, limiting access, and implementing monitoring systems are essential to prevent attacks.
OWASP categorizes attacks against AI models, including data poisoning, input manipulation, membership inference, model inversion, model theft, and model supply chain attacks.
Maintainability of AI Code
AI code often lacks readability, making it challenging for analysis and vulnerability management. Collaboration between data scientists, software engineers, and cybersecurity experts is essential.
Complexity of the AI Supply Chain
AI’s integration complicates the software supply chain. The AI Bill of Materials (AIBOM) is proposed to complement the traditional Software Bill of Materials (SBOM).
Reuse of External AI Code
Open-source code is commonly used in AI development, but thorough control is necessary to address weaknesses and vulnerabilities.
AI Cyberattacks and Offenders’ Optimization
AI not only faces attacks but can also be used by cybercriminals to optimize their capabilities. This includes using AI for generating deep fakes, manipulating information, and enhancing the efficiency of malware.
Types of Actors Exploiting AI Security Risks
ENISA categorizes malicious actors into seven typologies, each with varying characteristics and goals.
Groups with the primary goal of economic gain, leveraging AI for attacks or targeting AI systems directly.
2. Actors Threatening Social and Economic Systems
Government actors, state-sponsored groups, and terrorists aim to cause damage, ranging from attacking critical sectors to destabilizing democratic systems.
3. Friendly Fire and Competition
Employees and suppliers may intentionally or unintentionally sabotage AI systems, and rival companies may seek to steal intellectual property.
Hostile actors with ideological motivations who hack AI systems to highlight vulnerabilities.
How AI Risks Differ from Traditional Software Risks
- While AI systems are software, their unique characteristics introduce complexities that expand the attack surface of traditional software.
New and More Complex Cybersecurity Challenges
Challenges include unreliable data representation, dependence on training data, modifications during training impacting performance, obsolescence of training data, mismatch between AI and traditional software, privacy risks, and difficulties in security testing.
Implementing a Security Strategy for AI Systems
Organizations need to adopt strategies to manage cybersecurity and privacy risks throughout the AI lifecycle. This includes threat modeling, risk analysis, training, static and dynamic analysis, code analysis, pentesting, and Red Team exercises.
The Security of AI: A Significant Issue
- As AI becomes integral to various sectors, from large enterprises to SMEs, addressing AI security risks is paramount. Successful attacks can have severe consequences, necessitating a comprehensive security-by-design approach throughout the AI lifecycle.
Security-by-Design and Throughout the Lifecycle
ENISA recommends integrating cybersecurity controls, mechanisms, and best practices early in the design and development of AI systems. This includes security testing, secure coding practices, data processing security, and comprehensive monitoring of the AI supply chain.
In conclusion, the incorporation of AI into diverse sectors requires a robust approach to AI security risks. Leveraging the expertise and methodologies developed by cybersecurity professionals is essential.
Introducing security controls from the initial phases of the AI lifecycle, forming multidisciplinary teams, and monitoring the AI supply chain are critical steps. As AI continues to evolve, so must cybersecurity strategies to effectively protect against emerging threats.
Q1: What is the current state of Artificial Intelligence (AI) in our society?
AI is on the brink of revolutionizing our economy and way of life. It has transitioned from a once-futuristic concept in literature and movies to a tangible reality, embedded in our daily lives. Particularly in Machine Learning and generative AI, AI is reshaping our productive landscape and lifestyle.
Q2: Are there organizations actively addressing AI security risks?
Yes, various organizations have intensified efforts, especially in 2023, to tackle AI security risks. Notably, the European Union Agency for Cybersecurity (ENISA), the US National Institute of Standards and Technology (NIST), and the OWASP Foundation have released frameworks, methodologies, and reports focusing on AI security risks.
Q3: How is the European Union addressing AI security through regulation?
The European Union is progressing towards finalizing the Artificial Intelligence Act, emphasizing the crucial relationship between cybersecurity and AI. This regulation aims to provide a framework for managing and mitigating AI security risks.
Q4: How does AI contribute to cybersecurity?
AI, with generative AIs like ChatGPT, Midjourney, DALL-E, and Copy AI, has become a crucial ally in cybersecurity. It automates threat detection, strengthens defensive capabilities, and optimizes security assessments and predictions of attack patterns.
Q5: What are the primary AI security risks for companies in AI development?
The article highlights several key AI security risks, including threats to AI systems, data security risks, attacks against AI models, maintainability of AI code, complexity of the AI supply chain, and the reuse of external AI code.
Q6: How are AI systems vulnerable to cyber attacks?
AI systems face various types of attacks, such as data poisoning, input manipulation, membership inference, model inversion, model theft, and model supply chain attacks. These vulnerabilities require robust security measures.
Q7: What types of actors exploit AI security risks?
ENISA categorizes malicious actors into seven typologies, including cybercriminals, government actors, terrorists, employees, suppliers, rival companies, and hacktivists.