What is the Real Potential Effect of AI on Cybersecurity?
The real potential effect of AI on cybersecurity and exploring strengths and limitations
As artificial intelligence (AI) continues to surprise the world with its capabilities in various domains, questions arise about its potential impact on cybersecurity. If AI can produce masterpieces in art and write poetry while accessing vast information repositories, could it also threaten security protocols? The answers are complex, evolving, and far from certain. While AI offers some advantages for defending computer systems, it also presents challenges that may never be fully overcome.
Defining Artificial Intelligence and Machine Learning
Before delving deeper, it is essential to distinguish between artificial intelligence (AI) and machine learning (ML). AI refers to technology that can imitate or go beyond human behavior, while ML is a subset of AI that utilizes algorithms to identify patterns in data without human intervention. ML aims to enhance decision-making processes for humans or computers. While commercial products often refer to ML as AI, true AI involves more than ML techniques.
The Strengths of AI in Cybersecurity
AI possesses several strengths that prove immediately valuable in cybersecurity. It excels at searching and identifying patterns in large datasets, effectively correlating new events with past occurrences. Many machine learning techniques are statistical, which aligns with the statistical nature of attacks on computer systems and encryption algorithms. The availability of ML toolkits empowers both attackers and defenders to experiment with algorithms. Attackers leverage ML to search for vulnerabilities, while defenders employ ML to detect signs of potential attacks.
Moreover, AI can assist in bug hunting and code verification, surpassing traditional methods such as fuzzing. By leveraging existing knowledge and analyzing code, AI can identify weaknesses and suggest fixes without custom programming or expert guidance. Companies like Microsoft are already capitalizing on this approach by training AI models with foundational knowledge to aid in security tasks.
AI’s Limitations and Failures
Despite its promising applications, AI also has limitations and can exhibit failures. AI can only express what is in its training data and often adheres to a literal interpretation, lacking human-like contextual understanding.
Furthermore, using randomness in AI models renders them unpredictable and non-deterministic, as captured by the concept of “temperature.”
The Multifaceted Nature of Cybersecurity
Cybersecurity is a complex field that encompasses various branches of mathematics, network analysis, and software engineering. Additionally, human factors play a significant role, necessitating a comprehensive understanding of human weaknesses. Due to this multidimensionality, AI’s effectiveness in specific cybersecurity applications varies.
While it may excel at tasks like network anomaly detection, its utility in other areas may need to be improved.
AI in Weakness Identification and Encryption Breaking
AI’s potential for finding weaknesses and breaking encryption algorithms has garnered attention. By analyzing statistical relationships, AI models can resemble approaches used by cryptographers for decades. However, encryption algorithms like AES or SHA, which employ non-linear functions, were explicitly designed to resist statistical attacks. AI-based attacks may face significant challenges against hardened algorithms.
Foundational Challenges and Precision
More profound vulnerabilities may lie in the fundamental layers of computer security. Large machine learning models rely heavily on linear mathematics, while encryption algorithms prioritize non-linearity to ensure safety. Furthermore, the precision required for public-key algorithms with thousands of digits poses challenges for AI algorithms, which often prioritize imprecise operations. Adapting general AI algorithms to cryptanalysis may require overcoming these precision issues.
The Emergence of Greater Scale and Symbolic Models
The question arises whether the sheer scale of AI models can make a difference in discovering vulnerabilities. Scientists are exploring integrating large language models with logical approaches and formal methods to enhance their power. By incorporating automated mechanisms for reasoning about mathematical concepts, AI may surpass mere imitation of training patterns. Expanding AI’s symbolic reasoning capabilities could unlock new possibilities in unexplored cybersecurity-related mathematical domains.
Exploring New Frontiers
AI’s potential extends beyond known theorems and proven mathematical theories. It may excel in areas where human intuition needs to improve, such as higher-dimensional geometries. The vastness of unexplored mathematical territory suggests endless possibilities for AI contributions to cryptanalysis and other mathematical fields.
