Artificial Intelligence

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Artificial Intelligence (AI) in the context of cybersecurity refers to the use of AI technologies, primarily Machine Learning (ML) and Deep Learning (DL), to enhance the defense of computer systems, networks, programs, and data against cyber threats.

Unlike traditional security systems that rely on pre-defined, static rules (signatures) to identify known threats, AI systems are designed to learn, reason, and make decisions from vast amounts of data. This allows them to automate security tasks, detect novel threats, and respond to incidents at a scale and speed impossible for humans alone.

Key Aspects of AI in Cybersecurity

1. Enhanced Threat Detection and Analysis

The most significant use of AI is in identifying malicious activity.

  • Anomaly Detection: AI/ML algorithms establish a baseline of "normal" behavior for networks, users (User and Entity Behavior Analytics or UEBA), and endpoints. They then continuously monitor activity and flag any deviation from this baseline. This is crucial for detecting zero-day threats (attacks exploiting previously unknown vulnerabilities) and insider threats that bypass signature-based tools.

  • Malware Analysis: AI can analyze the behavior and code structure of files, rather than just matching a signature. This helps it identify and classify new or polymorphic (shape-shifting) malware variants that traditional antivirus software would miss.

  • Predictive Analytics: By analyzing historical data, threat intelligence feeds, and real-time network traffic, AI models can forecast potential attack vectors, allowing organizations to proactively patch vulnerabilities or adjust firewall rules before an attack is launched.

2. Automation and Response

AI significantly reduces the time between threat detection and mitigation, often automating the response entirely.

  • Security Orchestration, Automation, and Response (SOAR): AI/ML powers SOAR platforms to automatically execute pre-defined actions, or "playbooks," when a threat is detected. For example, an AI system can instantly quarantine an infected endpoint, block a malicious IP address at the firewall, or force a password reset on a suspicious account.

  • Alert Triage and Prioritization: Security teams often suffer from "alert fatigue" due to the massive volume of security alerts. AI analyzes and correlates alerts from various sources, filters out false positives (benign activities flagged as threats), and prioritizes the truly high-risk events, allowing human analysts to focus on critical investigations.

3. Proactive Defenses

AI strengthens security measures across different domains:

  • Phishing and Email Security: Natural Language Processing (NLP), a subset of AI, is used to analyze the tone, grammar, sender patterns, and content of emails to detect sophisticated, personalized spear-phishing and social engineering attacks that bypass simple keyword filters.

  • Identity and Access Management (IAM): AI models analyze user login patterns (time of day, location, device) and requested resources. If a user logs in from an unusual country or attempts to access sensitive data outside their regular work hours, the AI can trigger step-up authentication (like multi-factor authentication) or block access entirely, defending against stolen credentials.

  • Vulnerability Management: AI can scan code and network configurations to identify and prioritize weaknesses more efficiently than human analysts, recommending the most critical patches first.

In essence, AI helps cybersecurity transition from a reactive posture (waiting for an attack to occur and relying on known signatures) to a proactive, adaptive, and autonomous defense system capable of learning from, predicting, and responding to the rapidly evolving landscape of cyber threats.

ThreatNG, as an all-in-one External Attack Surface Management (EASM), Digital Risk Protection (DRP), and Security Ratings solution, provides a powerful defensive shield against both the infrastructure that supports Artificial Intelligence (AI) and the AI models themselves, while simultaneously offering intelligence on how adversaries use AI for offensive purposes.

AI Defense via External Attack Surface Management

ThreatNG’s capabilities would help secure an organization's AI/ML ecosystem by continuously identifying and assessing the internet-facing components that could lead to a compromise of valuable models or training data.

  • External Discovery and DNS Intelligence: The External Discovery feature and DNS Intelligence are critical for locating the often-forgotten infrastructure supporting AI development and deployment.

    • Through Domain Record Analysis, ThreatNG can enumerate AI Model & Platform Providers (e.g., Anthropic, Cohere, OpenAI, Stability AI) and AI Development & MLOps vendors (e.g., GenTrace (AI), Pinecone, Hugging Face). This instantly creates an inventory of third-party AI/ML services that are part of the attack surface, many of which may be "Shadow AI" or unknown to the security team.

    • For example, if the Subdomain Intelligence reveals a subdomain like devops.mycompany.com using a Code Repository like GitHub or an AI Development tool like Pinecone, the security team immediately gains visibility into a high-risk asset that may host a model's API or sensitive training data.

  • External Assessment and Exposed Credentials: Once discovered, the External Assessment immediately identifies vulnerabilities on these assets.

    • Cyber Risk Exposure specifically factors in Code Secret Exposure, which is paramount for AI security. It investigates code repositories for the presence of Access Credentials (e.g., AWS Access Key ID, AWS Secret Access Key, Google Cloud Platform OAuth), Database Exposures (e.g., SQL dump file, MongoDB credentials), and other secrets like Potential cryptographic private keys within public code repositories. The exposure of an AWS Access Key ID in a public GitHub repository, for instance, could grant an attacker initial access to the cloud environment hosting the AI model's training pipeline or inference server.

    • The Dark Web Presence module checks for Associated Compromised Credentials, which, if linked to an ML engineer, could lead to a model tampering or data poisoning attack.

  • Proactive Vulnerability Management: The Overwatch system instantly performs impact assessments across an entire portfolio to identify and prioritize exposure to critical CVEs in the underlying AI infrastructure. The Vulnerabilities intelligence repository (DarCache Vulnerability) provides context from KEV (vulnerabilities actively being exploited) and EPSS (likelihood of exploitation), allowing the security team to focus on patching vulnerabilities in high-risk AI components, like a known vulnerability in a Docker container use in the MLOps pipeline, before attackers can use it for initial access.

Intelligence on Offensive AI

ThreatNG helps an organization understand and defend against how adversaries use AI to launch sophisticated attacks, treating the Conversational Attack Surface as a source of threat intelligence.

  • BEC & Phishing Susceptibility: The score for BEC & Phishing Susceptibility is highly relevant to countering AI-enabled social engineering. By analyzing Domain Name Permutations (homoglyphs, vowel-swaps, etc.) across numerous Top Level Domains (TLDs) with Targeted Key Words like login or payment, ThreatNG identifies the infrastructure attackers could use to launch highly personalized, AI-generated phishing attacks (e.g., mycompany-auth.com on a lookalike TLD).

  • Adversarial Mapping and Reporting: The External Adversary View and MITRE ATT&CK Mapping translate raw findings, such as an exposed Admin Directory found via Search Engine Exploitation, into an adversary narrative. This directly uncovers how an attacker might achieve initial access and establish persistence, which is the first step an attacker would take before launching an AI-specific attack (like a poisoning or evasion attack) against the internal model. The Reporting features, including Prioritized Reports and External GRC Assessment Mappings, provide the necessary business context to justify security investments to the boardroom.

Complementary Solutions

ThreatNG's EASM and DRP findings can be combined with other cybersecurity solutions to form a holistic AI security strategy.

  • By integrating ThreatNG's Compromised Credentials intelligence (DarCache Rupture) with a Security Orchestration, Automation, and Response (SOAR) platform, any credential belonging to an employee of an AI/ML vendor (found via the Supply Chain & Third Party Exposure module) that appears on the dark web can instantly trigger an automated workflow to force a password reset and revoke session tokens, mitigating the risk of a supply chain attack that targets AI infrastructure.

  • ThreatNG's MITRE ATT&CK Mapping of external exposures (e.g., leaked API Keys) can be fed into a Security Information and Event Management (SIEM) system to enrich internal logs and prioritize alerts. For instance, if ThreatNG reports an exposed API key associated with a Databricks instance and the SIEM detects anomalous internal activity on that same platform, the combined intelligence provides a high-fidelity alert, allowing the security team to stop a model extraction or data exfiltration attempt more rapidly.

  • The continuous discovery of unmanaged Cloud and SaaS Exposures and misconfigurations from ThreatNG can be shared with a Cloud Security Posture Management (CSPM) tool to provide a full-spectrum view. For example, ThreatNG might identify an Open Exposed Cloud Bucket of AWS used for storing training data, and the CSPM tool can then confirm and enforce the required security policies, closing the external attack surface gap before an attacker can corrupt the data for a poisoning attack.

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