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Threatbook: Core Capabilities and Threat Intelligence
Threatbook is a comprehensive threat intelligence platform that provides actionable security insights by aggregating diverse data sources and applying AI-powered analysis. It specializes in generating threat verdicts for IPs and domains, enriching contextual data, tracking historical adversary behavior, and detecting digital risks like brand fraud and phishing at the network edge.
Key Takeaways
Threatbook assigns verdicts (malicious/benign) using rules and extensive intelligence sources.
Contextual enrichment provides detailed data like WHOIS, DNS, and SSL certificates for investigation.
The platform tracks historical adversary behavior and infrastructure reuse to predict future attacks.
AI filters false positives and synthesizes intelligence to produce highly actionable judgments.
Edge protection, such as OneDNS, proactively blocks phishing and fraud at the network gateway.
How does Threatbook determine a threat verdict?
Threatbook determines a threat verdict by categorizing indicators such as IP addresses or domains as malicious, suspicious, benign, or unknown. This critical assessment relies on a robust combination of rule-based analysis and extensive intelligence sources. By systematically evaluating these factors, the platform provides a clear, actionable judgment necessary for immediate security response and policy enforcement, ensuring rapid identification of genuine threats.
- The categorization of IPs or domains (malicious / suspicious / benign / unknown) using rule-based + intelligence sources.
What is Contextual Enrichment and why is it necessary?
Contextual enrichment involves providing detailed background information around a specific threat indicator to help security teams detect fraud and abuse effectively. This process gathers crucial data points like WHOIS records, DNS information, SSL certificate details, and associated malware samples. By compiling this comprehensive context, analysts gain a deeper understanding of the threat actor's infrastructure and intent, facilitating faster and more accurate investigations and response actions.
- Providing detailed context around an indicator (WHOIS, DNS, SSL certificate, malware samples) so as to detect fraud/abuse.
Why is tracking Historical Behavior important for threat intelligence?
Tracking historical behavior is essential because it allows security teams to identify long-term activity patterns and the reuse of infrastructure by persistent adversaries. By monitoring past actions, Threatbook can connect seemingly disparate events and reveal sophisticated attack campaigns over time. This capability is vital for predicting future attacks and understanding the full scope of an attacker's operational footprint, moving beyond isolated incident response to proactive defense planning.
- Tracking past behaviours, reuse of infrastructure by adversaries, long-term activity patterns.
How does Threatbook handle Data Aggregation and Normalisation?
Threatbook handles data aggregation and normalization by unifying multiple intelligence sources, third-party feeds, and scanning systems into a single, cohesive platform. This process ensures that all incoming data is standardized and comparable, regardless of its origin or format. By bringing diverse information together and normalizing it, the platform significantly improves the accuracy and speed of threat detection, providing a unified and reliable view of the global threat landscape.
- Bringing together multiple intelligence sources, third-party feeds, scanning systems, unified in one platform to better detect threats.
How do AI-Powered Insights reduce noise in threat intelligence?
AI-powered insights and noise reduction are achieved through the combined efforts of artificial intelligence and human analysts working together to refine intelligence data. The AI component is crucial for filtering out false positives, which often overwhelm security teams and delay response times. This synthesis produces highly actionable judgments, ensuring that analysts focus only on genuine, high-priority threats, thereby maximizing efficiency and improving the overall quality of the security posture.
- Using AI + human analysts to filter false positives, synthesise intelligence and produce actionable judgements.
What types of Digital Risk and Brand Fraud does Threatbook detect?
Threatbook detects various forms of digital risk and brand fraud by monitoring the digital landscape for malicious impersonations and deceptive assets. This includes identifying phishing sites, fake mobile applications, fraudulent social media accounts, and brand-impersonation schemes. Utilizing its Digital Risk Protection Service (DRPS), the platform actively scans for and flags these deceptive assets, protecting the organization's reputation and preventing customer compromise across multiple channels.
- Detecting phishing sites, fake mobile apps, fake social accounts, brand‐impersonation, fraudulent phone numbers via their DRPS.
How does Threatbook provide DNS-based Detection and Edge Protection?
Threatbook provides robust DNS-based detection and edge protection by leveraging enforcement mechanisms like OneDNS. This technology operates effectively at the network edge, allowing the platform to proactively block malicious traffic before it reaches internal systems or users. By intercepting threats such as phishing and fraud at the DNS gateway level, organizations can significantly reduce their attack surface and prevent initial compromise attempts, ensuring a strong first line of defense.
- Leveraging DNS gateway/enforcement (OneDNS) to block phishing/fraud at the network edge.
What is Pivoting Analysis and how is it used in threat investigation?
Pivoting analysis is a crucial technique used to explore unknown adversary infrastructure by systematically linking known indicators to other related assets. This involves using data points like IP addresses or domains and cross-referencing them with contextual information such as WHOIS records, SSL certificates, and DNS data. This method allows investigators to map out the full extent of an attacker's network, revealing previously hidden components and expanding the scope of threat intelligence and remediation efforts.
- Exploring unknown adversary infrastructure by linking known indicators (IP, domain) to other assets via WHOIS, SSL, DNS, etc.
Frequently Asked Questions
What is the primary function of Threatbook's Verdict feature?
The primary function is to categorize indicators like IPs and domains as malicious, suspicious, benign, or unknown. It uses a combination of rule-based systems and intelligence sources to provide clear, actionable security judgments for immediate response.
How does Threatbook use AI in its analysis?
Threatbook uses AI alongside human analysts primarily for noise reduction. The AI filters out false positives and synthesizes complex intelligence, ensuring that the resulting judgments are highly actionable and relevant for security teams, maximizing efficiency.
What specific data points are included in Contextual Enrichment?
Contextual Enrichment includes detailed information such as WHOIS records, DNS data, SSL certificate details, and associated malware samples. This comprehensive data helps analysts detect fraud and abuse by understanding the full threat context.
What is the purpose of Data Aggregation and Normalisation?
The purpose is to bring together diverse intelligence sources, third-party feeds, and scanning systems into one unified platform. Normalization ensures the data is standardized, improving the speed and accuracy of threat detection across the board.
How does Threatbook protect against brand fraud?
Threatbook uses its Digital Risk Protection Service (DRPS) to detect brand fraud. This involves identifying phishing sites, fake mobile apps, fraudulent social accounts, and other brand-impersonation schemes across the digital landscape.
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