Businesses can benefit from AI/ML, but threat actors can also utilize it to expand attack surfaces. Cybercriminals increasingly embrace ML technologies to improve their attacking techniques and use ML defenses to trick and confuse the systems.
Machine Learning (ML) is helping companies better prepare for issues posed by cyber-threats. Threat actors are also finding it helpful and integrating it into cyber kill chain components like weaponization and reconnaissance.
Furthermore, when threat actors attempt to deceive AI and “confuse” ML solutions, ML defenses evolve into just another attack surface. Organizations must secure ML resources, defend ML processes, and develop adversarial testing protocols, in addition to knowing how to employ ML for defense.
Threat Actors Leveraging AI/ML for Malicious Objectives
Threat actors use ML to target learning activities and enhance their attacks compared to traditional routes. Here are a few ways threat actors can utilize AI and ML to improve their techniques:
- Understanding the target is necessary for successful spear-phishing efforts. ML automates this procedure and creates lure content by scraping the social media profiles of the target. Creating new banking accounts, approving funds transfers, and other financial-related tasks using deepfakes and synthesizing voices and faces is another potential application of machine learning.
- Threat actors can leverage ML to cloak malware behavior under maliciously generated traffic that appears legitimate. Additionally, when ML components of an attack package learn more about the target environment, they may be able to enhance compromise methods, which can accelerate the accomplishment of attack goals.
- By analyzing a large dataset of passwords using adversarial networks, threat actors can produce variations that suit the statistical distribution, such as password leaks. In other words, ML gives threat actors a more streamlined method for password cracking, making guessing passwords more scientific.
Adversarial Machine Learning (AML)
Complex ML implementations can introduce design and configuration vulnerabilities. Complexity can create huge gaps in the attack surface when combined with the dearth of ML experts. Threat actors can exploit these gaps to draw inaccurate ML inferences and conceal further malicious actions required to carry out attack objectives.
AML can be used by businesses and vendors to decide what can be learned and how. Identifying the attack vectors that ML can learn can help understand what cybercriminals can find, enabling proactive preventative measures. AML also provides insight into how easily bad actors can breach and confuse ML defenses.
By creating adversarial examples and then training the ML model to address them appropriately, vendors and organizations can combat malicious AML. Regardless of the strategy, businesses depending on ML to defend their information assets must ensure the robustness of any solution, just as they would test any safety measure using penetration testing and risk assessments.
Defending Against Adversarial AI
The attacks against ML necessitate a comprehensive ML management strategy, which includes hardening ML processes and keeping an eye out for gaps in the defenses that will unavoidably appear. A risk assessment for each ML implementation, assignment of ML content ownership and operational control, modification of existing security policies, and establishment of new security policies are the first steps in ML defense.
MLOps is emerging as an approach to model development, design, and operations of ML solutions, including data validation and testing. It is a set of processes that resides at the intersection of machine learning, data engineering, and DevOps. It aims to strengthen and standardize model reproducibility, adherence to security policies, regulatory compliance, the accuracy of predictions, monitoring, and management.
ML is becoming a crucial part of detection, prevention, and response processes, making it a tool for threat actors and defenders and expanding attack surfaces. Standards, policies, procedures, and guidelines must be expanded to include these new components. ML standards like MLOps are essential for enhancing results and managing risk.
For more such updates follow us on Google News ITsecuritywire News