Machine Learning

Machine Learning (ML) in the context of cybersecurity is the use of algorithms and statistical models that computer systems use to perform a specific task without explicit programming, relying instead on patterns and inference learned from massive amounts of data.

It is a core subset of Artificial Intelligence (AI) and is the engine that drives most modern security tools. Its primary value is its ability to process data, identify complex relationships, and make real-time decisions at a scale and speed that is impossible for human analysts.

Key Components and How They Function

1. The Learning Process

The effectiveness of ML in security depends on the data it is trained on:

• Training Data: This consists of historical network logs, malware samples, user activity records, and known threat intelligence.

• Algorithms: These are the models (e.g., neural networks, decision trees, clustering algorithms) that process the data.

• Supervised Learning: Trained on labeled data (e.g., "this network packet is malicious," "this email is legitimate"). It is used for tasks like malware classification and phishing detection.

• Unsupervised Learning: Trained on unlabeled data to find hidden patterns and natural groupings. It is excellent for anomaly detection, as it learns what "normal" looks like and then flags anything that doesn't fit the established clusters.

• Reinforcement Learning: Used to train agents to make a sequence of decisions in an environment to maximize a reward. In cybersecurity, this could be used to teach an intrusion prevention system how to respond to an active attack optimally.

• Feature Engineering: This process involves selecting the most relevant attributes from the data (e.g., file size, network port number, time of login) to feed into the model, which is critical for the model's accuracy.

Applications in Defensive Cybersecurity

Machine Learning has transformed defense across several critical areas:

1. Advanced Threat Detection

ML excels at detecting threats that lack a known signature.

• Anomaly Detection (Zero-Day & Insider Threats): By establishing a baseline of normal behavior for each user (User and Entity Behavior Analytics - UEBA) and network segment, ML can identify subtle deviations. For instance, a user accessing a large number of sensitive files at 3 AM or a network device communicating with a foreign IP address for the first time may be flagged as a potential insider threat or zero-day compromise.

• Malware and Ransomware Identification: Instead of relying on a database of file signatures, ML models examine the characteristics and behavior of a file or process (e.g., its intent to encrypt files, modify system registry keys) to identify even entirely new or polymorphic malware variants.

2. Security Automation

ML helps manage the overwhelming volume of security data and alerts.

• Intelligent Alert Triage: ML algorithms analyze and correlate thousands of security events (logs, alerts, network flows) to filter out false positives and prioritize the few genuine, high-fidelity threats that require immediate human attention. This combats "alert fatigue."

• Automated Incident Response: ML can be integrated into Security Orchestration, Automation, and Response (SOAR) platforms to automatically initiate response actions, such as isolating a compromised endpoint, blocking malicious traffic at the firewall, or initiating a forensic data capture the moment a high-confidence threat is confirmed.

3. Fraud and Identity Protection

ML is fundamental to protecting users and sensitive transactions.

• Phishing and Spam Filtering: Machine learning, particularly Natural Language Processing (NLP) models, analyzes the structure, tone, grammar, and metadata of emails to detect highly sophisticated, personalized social engineering and phishing campaigns.

• Risk-Based Authentication: ML continuously assesses the risk of a login attempt based on factors like the user's current location, device type, and login history. Suppose an anomaly is detected (e.g., a login from a new country 5 minutes after a login from the office). In that case, it can automatically require an extra authentication step (multi-factor authentication) before granting access.

ThreatNG's comprehensive platform offers a robust approach to managing risks associated with Artificial Intelligence (AI) and Machine Learning (ML), providing both proactive defensive security for an organization's own AI assets and actionable offensive intelligence on how threat actors use AI against them.

Defensive AI Security and Risk Management

ThreatNG helps secure the entire AI/ML lifecycle—from development to deployment—by focusing on external visibility and vulnerability assessment. The External Discovery module, which performs purely external unauthenticated reconnaissance, is the first step in protecting AI, as it maps out the sprawling infrastructure that hosts models and data, often uncovering Shadow AI projects unknown to security teams. The Investigation Modules amplify this, particularly DNS investigation capabilities within Advanced Search, which allow analysts to map the entire data science and MLOps environment quickly. For example, an analyst can perform a detailed DNS investigation to identify new subdomains like ml-data-staging.company.com and cross-reference the associated IP to determine the hosting provider, thereby successfully identifying the specific Artificial Intelligence and Machine Learning technologies in the Technology Stack that may be exposed, such as a self-hosted Jupyter Notebook or a public-facing Kubernetes cluster use for model serving.

The External Assessment then provides granular, actionable risk findings for these discovered assets. Its detailed checks for Cyber Risk Exposure are vital; for instance, it thoroughly scans GitHub Code exposure, where it might uncover a development repository containing a configuration file with a hardcoded API key for a cloud environment (like AWS Access Key ID, AWS Secret Access Key, Google Cloud Platform OAuth) used to access the model's training data lake. If such a key is found, an attacker could use it for data poisoning or model extraction attacks. Furthermore, the assessment of Archived Web Pages might reveal older, forgotten API documentation or login pages for the model's inference engine, providing an attacker with a direct path to exploit the AI service.

The Continuous Monitoring provided by Overwatch ensures that this protection remains current by instantly performing impact assessments across the discovered AI assets, identifying and prioritizing exposure to critical CVEs in the underlying infrastructure (e.g., a newly disclosed vulnerability in the Python libraries used for a model's dependencies). Meanwhile, the Intelligence Repositories, such as DarCache Vulnerability, provide context on vulnerabilities actively being exploited, allowing security teams to immediately focus on patching flaws in their most critical ML deployment platforms. Finally, Reporting features, including Prioritized Reports and External GRC Assessment Mappings, translate the technical risks—such as the severity of a cloud credential leak—into business risk for stakeholders.

Offensive AI Intelligence and Synergies

From an offensive intelligence standpoint, ThreatNG’s capabilities help organizations understand and defend against how adversaries use generative AI to launch more sophisticated attacks. The BEC & Phishing Susceptibility score, for example, enables an organization to anticipate AI-generated social engineering by identifying domain permutations (homoglyphs, typosquatting) used by attackers to host highly personalized, AI-written phishing pages. Additionally, the Dark Web Presence module identifies Associated Compromised Credentials and organizational mentions that could be precursors to attacks amplified by AI, such as a data breach being used to train an LLM to generate deepfake voice recordings for a Business Email Compromise (BEC) attack. This intelligence from the Dark Web Presence repository, combined with an assessment of the organization’s Technology Stack, allows security teams to monitor for emerging attack techniques proactively.

By working with complementary solutions, ThreatNG’s data becomes even more powerful:

• ThreatNG’s identification of an exposed Databases server via Subdomains can be fed into a Security Information and Event Management (SIEM) system, enriching internal logs and immediately elevating the priority of any internal alert related to that database, providing a clearer view of an attempted breach.

• The discovery of a compromised credential for an ML engineer from the Intelligence Repositories can be automatically sent to a Security Orchestration, Automation, and Response (SOAR) platform, which instantly executes a playbook to force a password reset and revoke session tokens, mitigating the risk before an attacker can use the credential for a model tampering attack.

• When ThreatNG identifies a publicly exposed Cloud Service (e.g., a misconfigured Web Server hosting a proprietary model) on the external attack surface, this finding can be integrated with a Cloud Security Posture Management (CSPM) solution, which can then verify internal policy compliance and automatically enforce the correct security settings to close the external exposure gap.