AI in cybersecurity has been around for years, flagging anomalies, parsing logs, and powering threat detection models. Since the public release of GenAI, it has been used to pull more data in at scale and draw conclusions from massive amounts of telemetry. But at the end of the day, SOCs are still left to assemble the pieces and put them all together in a cohesive threat investigation workflow. But the recent rise of generative AI has accelerated what’s possible. Now, AI tools can analyze vast telemetry across identity, cloud, and endpoint sources, and draw conclusions faster than any human ever could.
Still, analysis alone isn’t enough. Security teams are drowning in data and alerts, many of which lack context or clear next steps. The burden of stitching it all together into a cohesive investigation still falls on the SOC.
And that takes time, especially when AI gives you so much data to work with. Now, AI is being used in sector-specific threat detection, and it’s doing it by super-charging SOC automation.
Gartner highlights this shift, noting that 33% of respondents are currently using advanced AI within their SOCs, and nearly 40% cite increased threat detection as the primary benefit.
Here’s how it’s working out in a few sector-specific use cases.
AI SOC Agents in Financial Services
AI SOC agents come in and handle Tier 1 and Tier 2 alerts and investigations, making AI SOC adoption a “no brainer” for Carvana CISO Dina Mathers. Imagine the implications when applied to a highly regulated sector like finance.
In a financial enterprise, AI agents detect and correlate fraudulent wire activity, phishing attempts, and account takeover patterns across multiple financial systems in real time. This is work that typically would take human-staffed SOCs hours (if not days), and been riddled with errors, missed leads, and inconsistencies.
Thanks to AI and its adaptive learning approach, financial security teams can know that the data coming in via multiple security tools, APIs, and software agents is entirely examined in real time, with nothing missed. Now, SOCs can spend their time chasing threats, not looking for them.
AI-Driven Compliance and Threat Correlation in Healthcare
In a healthcare system, security demands are similarly high, and data-gathering tools similarly present. As we are seeing now across the board, this data overload is proving to be a problem.
AI SOC platforms can scan for instances of non-compliance, shadow data and assets, and rules that are out of alignment with policy. Amid a barrage of alerts and never-ending logs, AI SOCs don’t tire; they aggregate and analyze multiple telemetries from across the organization’s security stack for anomalies that signify phishing and ransomware attempts targeting patient records.
The result is a non-stop source of security oversight that helps healthcare providers maintain HIPAA-compliant operations and identify sources of foul play that human eyes might miss.
Human brains are needed to bring experience, perspective, and insight to the table at the final stages of threat hunting. But with an AI SOC at the wheel, all the preliminary, mundane stages – from consulting external threat feeds to de-duplication to enriching threats – are taken care of.
AI for Cloud-Native Environments
Chasing down threats in the cloud is a multi-pronged process. Telemetry comes in from all sources; multiple clouds in a multi-cloud environment, myriad SaaS applications, Infrastructure-as-a-Service (IaaS) and more.
AI SOC agents unify these disparate threats and combine all telemetry into one centralized whole. Typically, it would take vigilant SOCs working around the clock to get the same effect; then there’s the work of sterilizing the data, suppressing false positives, and prioritizing critical alerts.
By creating a single source of truth, AI SOC agents can warn SOC analysts of cross-tenant anomalies, API abuse, and credential misuse faster than manual workflows alone. In a study by the Cloud Security Alliance, SOCs that leveraged AI in simulated cloud security scenarios were:
- 22-29% more accurate than human-only counterparts
- Faster at completing investigations by 45-61%
- Able to provide longer reports over time; manual teams eventually dropped by 20-27%
The Future of AI SOC Agents in Regulated Industries
Right now, AI SOC agents get better by training in the sector or environment they serve in. In the future, predictive models will evolve to anticipate sector-specific threats and come to the table (or industry or sector) pre-loaded with a host of use cases and ready-made scenarios and context banks.
This continued evolution will also lead to more “knowledgeable” models giving back beyond the threat investigation workflow alone. Insights will be shared to inform and improve security awareness programs. And over time, the cost of maintaining compliance will go down as a “learn as you go” agent increasingly aligns internal policies within regulatory boundaries.
Final Thoughts
The unique advantage of putting AI directly into SOC operations is that an AI SOC is as customizable as it is powerful. As we’ve mentioned, in finance, it can prevent fraudulent wire transfers. For healthcare, it can help maintain HIPAA compliance. And in the cloud, it can spot abusive behavior no matter how many SaaS apps are plugged in.
Most importantly, it gets better as it goes. In the absence of being able to hire a bunch of new experts to staff a SOC at scale, teams can rely on AI SOCs to get the job done, and to do it better than if humans were on those specific tasks.
AI SOC Platform company Prophet Security points out that “Integrating AI SOC agents into security operations workflows” creates a “skill-based collaboration, where human analysts function as supervisors, strategists, and quality controllers, partnering with AI agents to scale security outcomes.”
As Pete Shoard, vice president analyst at Gartner, notes in Cybersecurity Dive, “Unfortunately, AI isn’t magic. I don’t think it ever will be…But it is going to improve things for us in the SOC.”
An ardent believer in personal data privacy and the technology behind it, Katrina Thompson is a freelance writer leaning into encryption, data privacy legislation, and the intersection of information technology and human rights. She has written for Bora, Venafi, Tripwire, and many other sites.
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