See for yourself how Prophet AI can supercharge your security operations, accelerating alert investigation and response
Key benefits:
Lowers MTTR with AI-driven automated alert triage & investigation
Lowers risk by prioritizing critical alerts for analyst review
Eliminates manual effort, freeing analysts to focus on high-impact security tasks
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In today’s fast-paced world of cybersecurity, Security Operations Centers (SOCs) teams are essential in monitoring and protecting organizations against cyber threats. However, with more cyber threats popping up every day and limited resources to tackle them, relying solely on human effort isn’t practical anymore. The rise of SaaS apps, public cloud usage, and the quick spread of ransomware are overwhelming security teams with a flood of alert data.
That’s where SOC automation comes in.
By automating repetitive and tedious tasks, SOC automation streamlines processes, helps sift through the noise to identify real threats, and reduces overall risk. For example, automation can handle threat intelligence enrichment or IP reputation lookups, giving analysts the context they need without manual effort. Many Endpoint Detection and Response (EDR) tools can also automatically contain hosts, block file execution, and kill processes based on set rules, which speeds up response times.
But automation isn’t a magic bullet. Despite automation advancements, investigating alerts is still mostly a manual job, and the number of alerts has only gone up over the past five years. Some automated tools meant to lighten the load for analysts can actually add to it by generating even more alerts that need human attention.
In this blog, we’ll dive into the wins and challenges of SOC automation, giving you a clear picture of the present state of SOC automation and what tools can be used to get the highest impact.
It’s too challenging to keep up with the security perimeter and cyber threat pacing using just smart people. In fact, we’ve discussed in detail some of the leading challenges SOC analysts and managers experience on a regular basis going toe-to-toe with adversaries.
What you’re hoping to get out of automation is:
In the absence of strong SOC automation, teams can be left with:
Today teams are left with two primary options (or a hybridization of the two) to improve their automation – homegrown solutions or using a SOAR tool. Both present challenges:
Checking the box for an integration is easy, but security teams are often wanting more flexibility from out of the box integrations from SOAR tools or have to expend the energy to develop the integration themselves in-house.
Depending on your team skillset, the engineers responsible for building your integrations are likely not the same detection engineers responding to the investigations they produce. This forces your security engineers to pull away from security tasks and become pseudo-Product Managers to build requirements for the automation pipeline – which isn’t ideal for net productivity.
Whether you create your own homegrown solution from API calls and Jupyter notebooks or use a commercially available SOAR, it requires dedicated headcount to maximize, maintain, and improve either solution.
Changes in vendor APIs, and bugs in your internal stack all lead to deliverable impact and introduce risk.
New alert types? You’re likely having to codify a new runbook or set of enrichments to perform on a given investigation. Your homegrown or off-the-shelf solution aren’t going to be smart enough to pull patterns from existing alert types and automatically deliver the best investigation. Each requires bespoke tuning by the team.
If you read the heading of this section and gagged a bit, you’re not alone. Despite the fact that “AI” has been the two-letter acronym that’s been security product buzzword bingo for the last 10 years, collectively investigations have been getting more challenging, not less. In other words, the promise of AI hasn’t always materialized.
So what’s different now? Like any tool, AI isn’t the mythical solution that’s going to solve all of our problems, but with the rapid development of large language models (LLMs) alongside more traditional machine learning, SOC teams can start to realize more of the benefits that have been touted for the better part of a decade.
Rules-based SOAR or homegrown implementations may fall short in achieving the desired automation if they require users to hand-build playbooks for each variant of an investigation. However, modern public LLMs (when steered correctly) alongside more traditional classifiers can do an impressive job managing variation and building out investigative plans for alerts that don’t require your team’s intervention.
It also extends the opportunity for more complex decision making outside of simple conditional statements, while learning from your environment and user feedback as it continues to get better. These capabilities of AI have been out of reach for security teams up until now.
At Prophet Security, we envision a world where security analysts aren’t spending their time writing integrations, building runbooks, or meticulously tuning detections. With Prophet AI for Security Operations, we automatically investigate your alerts by leveraging your existing data sources with no integrations or workflow-building required. Request a demo of Prophet AI to learn how you can triage and investigate security alerts 10 times faster.
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Grant Oviatt
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Grant is the Head of Security Operations at Prophet Security.
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