Machine Learning and Cybersecurity: A Double-Edged Sword
In recent years, the intersection of machine learning (ML) and cybersecurity has become more and more important. While machine learning offers valuable ways to enhance an organization’s security measures, it also poses unique challenges and risks.
Currently, AI and machine learning are supporting businesses with everything from fraud detection and medical diagnoses to text parsing. Today, we’ll dive into the complex relationship between machine learning and cybersecurity, exploring how machine learning can serve as both a powerful ally and a dangerous vulnerability for security teams.
What is machine learning?
Machine learning is a subset of artificial intelligence that uses algorithms to iteratively make decisions and then learn from those decisions. Building ML models is a highly complex and resource-intensive process that includes data gathering, data cleaning, feature selection, training, testing, and validation.
There are three key types of machine learning:
- Unsupervised. Unsupervised learning is widely used to create predictive models. It works without labeled training sets and instead looks for more subtle patterns in data.
- Supervised. In supervised learning, data scientists feed labeled data (historical input and output data) into an algorithm to help it learn.
- Reinforcement. This approach mimics human learning, with the ML algorithm or agent learning by interacting with its environment and then receiving a positive or negative reward.
We’re also seeing the rise of innovative approaches to machine learning, many of which will have broad applications in the coming years. Some of these approaches include:
- Automated machine learning, or AutoML, which automates some of the most complex aspects of building ML models.
- Tiny machine learning, or TinyML, which runs machine learning algorithms on edge devices like wearables and Internet of Things (IoT) devices to allow for real-world data analysis and automated decision making with minimal computational resources.
- No-code ML, which uses drag-and-drop interfaces to allow organizations to automatically build ML models, automating processes like data collection, data cleansing, model selection, and model training without relying on data scientists.
- Neural networks, which use input layers, hidden layers, and output layers to emulate the human brain and power deep learning algorithms.
Top benefits of machine learning for cybersecurity
Certain types of machine learning have already existed for years. They power our social media recommendations, our virtual personal assistants like Google’s Siri and Amazon’s Alexa, and image recognition in our photo apps. Machine learning in data security is a somewhat newer field, but it’s growing fast.
Advanced threat monitoring and detection
Machine learning algorithms excel at analyzing large amounts of data to identify patterns and perform anomaly detection, making them perfect for advanced threat detection. By learning the baseline activity in a given system, ML tools can easily identify deviations and enhance security practices like intrusion detection, endpoint monitoring, and network traffic monitoring. ML models can also produce threat intelligence reports, perform behavior analytics, learn to recognize unusual user behavior, and flag potential security threats before they escalate.
Real-time incident response
Machine learning allows cybersecurity professionals to perform rapid incident response with faster analysis of security incidents. It also supports SIEM systems in analyzing security events across an organization, identifying problems like unauthorized access that might otherwise go unnoticed. This speed and accuracy is crucial in an era where cyberattacks can evolve rapidly and a single hour of downtime can cost in the tens of thousands.
Avoiding human error
Even the best organizations aren’t infallible, and human error is one of the top causes of security incidents. As Verizon’s Data Breach Investigations Report points out, 82% of surveyed breaches involve human error, from phishing and credential misuse to simple mistakes.
The Harvard Business Review noted that machine-learning algorithms can already classify malignant email attacks with 98% accuracy and recognize network intrusion with 99.9% accuracy. What’s more, natural language processing models can identify phishing activity and perform malware detection at a high level of accuracy through keyword extraction. Many of these tools are still in their early stages, so we can expect to see continued advancements in the ways ML helps avoid human error.
Adaptive security mechanisms
Finally, ML can help organizations adjust and evolve their security systems to keep up with emerging threats — without the need for manual intervention. This adaptability allows organizations to stay ahead of cyber adversaries by continuously improving their defense mechanisms. For example, adaptive authentication allows companies to individualize their multi-factor authentication requirements based on a user’s risk profile, location, and network security, increasing productivity while maintaining strong security operations.
Top risks of machine learning for cybersecurity
It’s not all bright spots for machine learning, unfortunately. ML tools also pose substantial risks, not least because they can be wielded by cybercriminals to aid malicious activities. Here are a few of the top data security challenges that machine learning presents.
Hackers are already using machine learning and artificial intelligence tools to facilitate cybercrime, including AI voice cloning to impersonate employees for voice-based phishing (vishing) attacks, customized email-based phishing attacks, and low-code and no-code ransomware variants to lower the barrier to entry.
Unfortunately, the combination of ML capabilities and malware is a potent combination. Experts predict that ransomware gangs will increasingly rely on these technologies to exploit the human element and take advantage of vulnerabilities in organizations.
Data privacy concerns
The large datasets used to train AI/ML models often contain sensitive data, which raises significant concerns about personal privacy. Most nations haven’t implemented AI- or ML-specific data privacy laws yet, and new technologies can increasingly infer sensitive information like a person’s geolocation and identity. While AI- and ML- assisted tools offer many ways to support society — predictive analytics for better decision making and public risk management, for example — they can also amplify risks to privacy, fairness, and equality.
Adversarial attacks on ML models and training datasets
Machine learning models are highly susceptible to adversarial attacks, in which adversaries manipulate input data to deceive ML algorithms. Notably, research shows that injecting as little as 8% poisonous training data can decrease a model’s accuracy by 75%. For this reason, developing robust defenses against adversarial attacks is both a significant challenge and a critical imperative.
How to protect your ML models and critical data from cyberattacks
AI/ML models and training data are highly valuable — and highly sensitive to attack. To create effective models, organizations must devote tens of millions of dollars to data acquisition, cleaning, labeling, and augmentation.
The ShardSecure platform for data security, privacy, and resilience can help safeguard these AI/ML models and training data. With advanced data protection, separation of duties for data sovereignty, robust data integrity, high availability, and agentless integration, the platform mitigates common cyber threats like ransomware, adversarial data tampering, and cloud provider outages.
To learn more about how our technology protects both ML data and any other mission-critical IP, visit our resources page.