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Ampsight Enhances Cybersecurity Protection with AI-Driven Threat Intelligence
Learn how Synaptiq helped Ampsight enhance cyber threat intelligence and security operations with AI.
Problem:
Ampsight provides enterprise-level cybersecurity solutions for clients in highly regulated industries, including critical infrastructure, healthcare, and financial services. Their Security Operations Center (SOC) employs expert threat hunters and analysts who defend client environments against sophisticated cyber threats.
However, like many modern SOCs, Ampsight's team faced a critical challenge: the overwhelming volume of security data generated daily from multiple sources - including threat feeds, indicators of compromise (IOCs), and open-source intelligence (OSINT). With threat data growing exponentially, Ampsight recognized the need to revolutionize their threat monitoring and analysis capabilities to maintain their high security standards while scaling their operations efficiently.
Solution:
Threat intelligence scoring system using LLMs
By extracting data from multiple sources and analyzing the unstructured data using Large Language Models (LLMs), Ampsight can use the sophisticated threat intelligence scoring system Synaptiq developed to automatically evaluate and prioritize security reports based on their relevance to client environments. At the heart of this system is a specialized algorithm which ranks reports, IOCs, and various other intelligence on a scale across four key dimensions: product version matches, product matches, vendor matches, and unstructured text matches.
The extracted data is then compared against client environments. The scoring system prioritizes precision over volume, ensuring that highly specific matches receive higher scores than general matches, making it easier for threat hunters to form actionable hypotheses.
After extensive testing, Ampsight experienced significant efficiency gains in threat analysis workflows. The system effectively filtered out irrelevant advisories while highlighting critical threats.
Predictive threat hunting
Using a sophisticated knowledge graph that maps relationships between different Tactics, Techniques, and Procedures (TTPs), Ampsight threat hunters can now anticipate and prevent future attack steps rather than just responding to known incidents.
This advanced predictive analysis system captures both the chronological progression and the interconnected nature of cyber attacks. The system also analyzes high-quality threat intelligence from sources like CISA advisories to build a comprehensive network of attack patterns. By using a partial attack pattern and generating probability-weighted predictions of likely next steps, threat hunters can now proactively investigate and mitigate potential vulnerabilities.
During testing, Ampsight’s threat hunters were excited to see a significant acceleration of threat investigation workflows and data-driven guidance for hypothesis development, resulting in increasing the number of potential attacks threat hunters could investigate and prevent.
Outcome:
With Synaptiq’s help, Ampsight has new, more effective ways to ensure they are looking at the most important data that’s relevant to their systems they are entrusted with protecting. By not being inundated with irrelevant information and distracted with too many areas to monitor, Ampsight can more proactively protect their clients more effectively than ever.
By utilizing LLMs to review and categorize documents, employing knowledge graphs to model attacker behavior based on vast datasets, and automating repetitive, error-prone tasks, Ampsight can now significantly enhance team efficiency and effectiveness as well as more proactively ensure cybersecurity.
AI IS HOW WE DO IT,