AI technologies in Kaspersky SIEM | Kaspersky official blog
It’s a rare company these days that doesn’t boast about using artificial intelligence (AI). And often no explanation is forthcoming as to why AI is needed or, more importantly, how it’s implemented — just the mere presence of AI, it seems, is enough to make a product more valuable, innovative and high-tech. Kaspersky advocates a different approach: we don’t just say “we use AI”, but explain exactly how we deploy machine learning (ML) and AI technologies in our solutions. It’d take too long to list all our AI technologies in a single post given that we have an entire expertise center — Kaspersky AI Technology Research — that deals with all aspects of AI. So my sole focus here will be on those technologies that make life easier for SIEM analysts working with the Kaspersky Unified Monitoring and Analysis Platform.
SIEM AI Asset Risk Scoring
In traditional systems, one of the most resource-intensive tasks of the SIEM analyst is prioritizing alerts — especially if the system has just been installed and works out of the box with default correlation rules not yet fine-tuned to the infrastructure of a specific company. Big data analytics and AI systems can help here. Armed with SIEM AI Asset Risk Scoring, monitoring and response teams can prioritize alerts and prevent potential damage. The module assesses asset risks by analyzing historical data and prioritizing incoming alerts, allowing to speed up triage and generate hypotheses that can be used for proactive searches.
Based on information about activated correlation rule chains, SIEM AI Asset Risk Scoring lets you build patterns of normal activity on endpoints. Then, by comparing daily activity with these patterns, the module identifies anomalies (for example, sudden traffic spikes or multiple service requests) that may signal a real incident and prompt the analyst to take a deeper look into these alerts. This way, the problem is detected early, before any damage is done.
AI-Powered OSINT IoCs
Analysts working with the Kaspersky Unified Monitoring and Analysis Platform also have the option to use additional contextual information from open sources through the Kaspersky Threat Intelligence Portal. After the latest update, the portal now provides access to threat intelligence collected using a generative AI model.
It works as follows: let’s say you’ve found a suspicious file during a threat hunt. You can take this file’s hash and look it up on the site, and if someone else has already encountered it during an incident investigation and published something about it, the technology will instantly show you indicators of compromise (IoC) and key facts about the threat. Without such an automation system, it can take the analyst many hours to find and review this information — especially if there are lots of materials and they’re written in different languages. Our system, built on an internal LLM model, can automate this process: it analyzes all reports and mentions of the threat whatever the language, extracts the essence, and presents a summary: the nature of the threat, the date it was detected first, cybercriminal groups associated with it, industries most often targeted using the file, and so on. This saves the analyst an enormous amount of time on searching and researching.
What’s more, the analyst has access to other Kaspersky Threat Intelligence data, including information generated using AI technologies and big data analytics. Our threat intelligence databases are continuously updated with the results of manual APT research, live data from the darknet, information from the Kaspersky Security Network, and regular analysis of new malware. All of these technologies help users minimize the potential damage from cyber-incidents and reduce the Mean Time to Respond (MTTR) and the Mean Time to Detect (MTTD).
We continue to improve the usability and performance of our SIEM system, with a focus on deploying AI to free information security employees from even more routine tasks. Follow updates of the Kaspersky Unified Monitoring and Analysis Platform on the official product page.
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