By: Eda Bolturk, Ph.D., Lara Dinc and Baris Ercan
Published: May 31, 2024 | Updated: May 31, 2024
Read time: 5 min
Introduction
The latest paper from Mastercard Advisors’ Cyber and Digital Resilience Practice explores how artificial intelligence (AI) and machine learning (ML) can help organizations improve their security posture with robust tools to:
- boost threat detection
- enhance risk management
- enable proactive cyber defenses
These technologies significantly improve threat detection by analyzing vast datasets, recognizing patterns and continuously learning from real-time inputs. Organizations are then empowered to mitigate vulnerabilities, reduce response times and ensure a faster recovery from cyber incidents, thereby protecting digital assets and customer data more effectively.
AI technologies also enable organizations to automate repetitive tasks easily. This allows cyber security teams to focus on more sophisticated threats and events, promoting a proactive cyber security environment.
Mastercard has dedicated responsible AI teams that review models to make sure they are being used correctly and responsibly. Mastercard aims to ensure that AI can help us protect our customers and advance the safety and security of our systems while minimizing the risks that it poses.
Three AI technologies reshaping cybersecurity
For organizations to make the most of these emerging opportunities, they should be aware of three key AI and ML trends and how they can be used in cyber security:
Deep Reinforcement Learning (DRL)
DRL can enhance an organization’s cybersecurity systems in three main ways:
- Threat detection
- Threat response automation and incident management
- Risk assessment optimization
Threat detection
By leveraging deep neural networks, DRL models can significantly expand an organization’s threat detection capabilities by improving anomaly detection, intrusion identification and malware classification.
DRL can identify non-linear patterns in data and adapt to dynamic environments through reinforcement learning. This empowers the models to improve their performance continually over time.
One of the key advantages of DRL models is their capacity to handle the complex and dynamic nature of cyberspace, enabling them to detect new and emerging threats that may elude other systems. By analyzing vast amounts of data and learning from feedback, DRL models can quickly adapt to changing attack vectors and novel cyber threats, upgrading the ability of organizations to stay ahead of more sophisticated adversaries.
Threat response automation and incident management
By learning from historical data and observing expert decision-making processes, DRL algorithms can autonomously make informed decisions during cyber incidents, enabling faster and more effective response actions.
This reduces response times, minimizes the impact of breaches and facilitates the restoration of normal operations.
Risk assessment optimization
By analyzing a multitude of variables and continuously learning from real-time inputs, DRL algorithms provide adaptive risk assessments. This enables organizations to:
- Identify and prioritize vulnerabilities more accurately
- Allocate resources efficiently
- Implement targeted security measures based on the current threat landscape
Large Language Models (LLMs)
LLMs, such as GPT, are evolving rapidly and have become an essential part of any cybersecurity toolkit. They currently have three main applications:
- Threat detection
- Security analysis automation
- Development and implementation of cybersecurity strategies
Threat detection
LLM models are another tool, along with deep learning based on knowledge graphs data, that can enable the identification and analysis of natural language-based threats like phishing attacks. By processing vast amounts of textual data, LLMs can quickly recognize suspicious patterns and help stop potential cybercriminal activities.
Security analysis automation
LLMs are instrumental in automating security analysis processes. By scrutinizing source code and documentation, these models excel at uncovering patterns and anomalies that may indicate security vulnerabilities. This capability empowers organizations to proactively address software weaknesses, thus mitigating the risk of security breaches and fortifying their defenses against future attacks.
Development and implementation of cybersecurity strategies
LLMs contribute to the development of robust cybersecurity policies. Leveraging their language generation capabilities, these models can assist companies in either summarizing and simplifying the discovery of complex policies, or crafting comprehensive and well-documented cybersecurity policies that align with industry best practices.
They can also aid in the implementation of widely recognized cybersecurity frameworks like OWASP (Open Web Application Security Project), ensuring that applications are built with security in mind right from the development phase. This is useful for delivering more secure products and product features.
Other Advanced Deep Learning Architectures
By leveraging advanced techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs), GenAI models contribute significantly to strengthening cybersecurity measures, in particular:
- Defensive data generation
- Cyberattack simulations
- Analysis and understanding of virus variations
Defensive data generation
GenAI enables defensive data generation, where synthetic data is created to train machine learning models. This approach involves generating realistic (yet fabricated) data, aiding organizations to build robust models capable of effectively detecting and classifying threats. By augmenting the accuracy and reliability of cybersecurity systems, GenAI safeguards sensitive customer information.
Analysis and understanding of virus variations
GenAI can generate synthetic variants of known viruses, enabling security teams to study the behavior and characteristics of different ‘strains’. This knowledge is invaluable for developing robust antivirus solutions and effectively countering the risks posed by evolving virus variations.
Cyberattack simulations
In combination with other AI technologies, GenAI allows organizations to simulate authentic cyber attack scenarios for training.
‘Red teams’* can leverage AI and ML to devise highly realistic attacks and simulations, mirroring the rapidly evolving world of cyber threats. These technologies simplify and speed up threat modelling and vulnerability assessments, leading to more efficient and accurate detection of potential security flaws.
‘Blue teams’* also benefit from these technologies by enabling real-time identification and prediction of potential cyber attacks, capabilities beyond human capacity. ML algorithms can use historic data to identify anomalous activities and predict potential threats, significantly boosting incident response times. Finally, AI can assist ‘blue teams’ to automate repetitive tasks, allowing them to concentrate on more complex security challenges.
By adopting this proactive approach, organizations can strengthen their preparedness and resilience against real-world cyber attacks.
Read the full report, “AI and Machine Learning in Cybersecurity: Risks, Opportunities, and Safety Considerations,” on the Market Trends platform to find out more about the impact of AI technological advancements on cyber security, with a focus on three overarching themes: 1) efficiency, 2) safety and 3) the future evolution of AI and ML.
