Introduction
As AI-powered Security Operations Centers (AI SOCs) gain recognition from enterprise security leaders and industry analysts alike, cybersecurity leaders in adjacent categories are looking to cash in on the hype. While some legacy malware analysis vendors have rebranded as AI SOC solutions, others have attempted to reinvent themselves as AI-powered managed security solutions, including categories like Managed SOAR, Managed XDR, (and most recently, AI-powered MDR).
To make matters more confusing for newer security analysts (or even the seasoned CISO that’s more focused on their organization’s cyber health than overall industry trends), many of these buzzwords are often used interchangeably. And unfortunately, the difference between these terms can be the difference between choosing a traditional human-powered solution and a truly autonomous detection and response platform that doesn’t require human intervention. But conceptually, many AI SOCs are just Managed SOAR platforms under the hood.
So what really sets these solutions apart, and how do you identify what fits your security team’s needs best?
The Difference Between AI SOCs and Legacy Solutions
To answer that, we need to explore how an AI SOC platform actually works.
On the surface level, an AI SOC platform operates a lot like a Managed SOAR product. They both involve ingesting log sources (and they can involve a Security Information and Event Management [SIEM] for alerting or context) to generate alerts and respond to suspicious or malicious activity based on playbooks or predetermined response actions. Moreover, both solutions even turn to AI when they use language models to infer whether an alert is a false positive, suspicious, or malicious and requires escalation.
For instance, if the alert is for "impossible travel," a corresponding "impossible travel" playbook is executed. That playbook will query the SIEM for additional context like user activity, geolocation, login timestamps, etc.
While this seems intelligent, most of the playbooks are predefined by humans, and the summarized reports are often based on pre-packaged outcomes. The actual decision-making is either based on hardcoded logic that can only handle a limited number of scenarios, or off-the-shelf AI models which can produce inconsistent results, especially when dealing with novel queries or ambiguous data.
Unfortunately, this is where the Managed SOAR approach hits its limits. Relying on pre-defined playbooks and legacy alerting platforms makes it impossible to scale quickly to new or custom use cases, make the most of emergent integration sources, or scale combinations of multi-source queries into customized alerts.
And of course, security teams relying on SIEM queries and API integrations will run into the familiar pains that come with rate limits and compounding data retention costs.
At a glance, AI SOC products sound revolutionary. But when you break down what’s really happening behind the scenes, it’s more automation than autonomy.
So Is the Dream of AI for the SOC Dead? Not Exactly
While some of these reskinned legacy solutions may make the prospects of an AI SOC solution feel discouraging, an AI-powered, autonomous SOC is more achievable than you think. It just needs to consider AI from the beginning, not as an afterthought or a repositioning.
The products claiming to be in the AI SOC category today are built to rely on a SIEM as a single source of truth. But at the end of the day, a SIEM is just an alerting platform - and not where you start to build an autonomous SOC. AI is only as powerful as the data it has access to. So what if it was the foundation of your security operations? What if it could ingest, normalize, and contextualize data directly, without being limited by what your SIEM is ingesting and alerting on?
That way, the AI SOC isn’t just reacting to alerts. It’s understanding patterns and behavior over time.
After all, if an agentic security solution is responsible for multiple attack surfaces (think your endpoints, your identities, your network, or your cloud workloads), it needs to be more flexible than a static playbook – and it should definitely provide more context and value than a basic summary. It needs to independently decide what it should be investigating, and act on those decisions before a threat actor has a chance to do real damage. Put simply, a good AI SOC solution will have a list of accurate queries, and will determine which ones are relevant to its investigation. These queries should come from a structured understanding of your environment, not just a prewritten script.
And unlike the cybersecurity solutions using untrained or pre-existing LLMs, SOCs need AI models that have been purposefully trained to interpret attack surface telemetry and security alerts, contextualize with logs and TTPs from previous incidents and known attackers, and are equipped with the data they need to make and validate their decisions. It should include long-term behavioral tracking across multiple sources, and be focused on actively identifying and reducing your potential security risks - not running through a set of if/then statements.
And of course, any good agentic security should be focused on only surfacing what matters in their UI. Legacy consoles often overload analysts with information they don’t need to know – which is why an AI SOC solution needs to have an opinionated UI that enables SecOps and SOC analysts to proactively identify and investigate risky behavior, rather than get stuck on the back foot in a reactive posture.
So, How Can You Tell The Difference?
AI SOC buyers looking to tell the difference between a legacy Managed SOAR solution and a truly innovative solution can ask the following questions during an initial sales call or a technical demo.
“Tell us how you got started. What was the problem you were trying to solve?”
Generally, asking this discovery question can help separate legacy vendors that repositioned from another seven or eight-year-old use case, with a newer vendor that has prioritized autonomous and agentic security from the beginning.
“How long does it take to onboard?”
Unlike Managed SOAR solutions or human-centric software, AI SOC solutions should be able to give analysts value from the first day you purchase it, and shouldn’t require months of implementation to start making decisions, investigating, and responding to threats in your environments.
“Do I need a SIEM for this to work?”
If a vendor requires a SIEM to normalize raw logs and alerts for your AI SOC, it’s likely a legacy XDR solution or designed to support a human-powered SOC by adding AI at the end to augment the existing alert-based methodologies.
“How does your solution use AI?”
While this question may be the simplest, a salesperson’s answer can truly reveal whether you’re dealing with a company that believes an AI SOC is capable of analyzing threat intelligence and making decisions autonomously, or if a human should be taking every action after the AI makes its suggestions.
“Do you support custom detections in my SIEM?”
This will tell you whether the product is resilient enough to handle alerts without predefined playbooks. Truly AI Native solutions should be able to do this easily and mature alongside your own organization’s security posture.
The Future of Autonomous Security Is Proactive, Not Reactive
While “autonomous investigations” are being commoditized, SOCs and security teams alike need a solution that supports real outcomes. They need a true AI-powered solution that goes beyond focusing on cutting MTTD and MTTR, and offers value regardless if you’re on a red, blue, or purple team (or even the SOC manager or security leader).
Kenzo Security is the first agentic AI SecOps platform that strives to transform every SOC function. With dedicated agents for every stage of a SOC workflow, from investigation, to threat intelligence enrichment, risk-based alerting, detection, and intelligent responses, Kenzo’s revolutionary agentic architecture unifies data (like alerts, context and telemetry), dynamically generates investigation playbooks for relevant investigations, and uses AI to proactively hunt for, identify and address risks before they become an issue.
Are you ready to truly embrace AI’s potential to reduce your company’s security risks? You can see Kenzo in action now. Schedule a demo with Kenzo Security today.
Integrating data and alerting sources directly with a security operations platform offers a streamlined architecture where telemetry and stateful data flows in real time, without arbitrary query rate-limits or limits on the type of data or telemetry you can ingest. Raw telemetry, events, alerts, and context are all fair game. The main advantage is the removal of artificial limits or fees associated with data volume and query frequency, allowing security teams to operate more flexibly and thoroughly.
Best of all? Directly integrating your data sources with your agentic security solution doesn’t even have to necessarily eliminate the valuable context a SIEM can bring to the table – these direct integrations let you strategically choose what data is best suited for normalization and retention via your AI SOC, and what solutions would still benefit from the first-party SIEM integration - minimizing the legacy costs your team might need to pay.
However, this approach comes with certain trade-offs. Onboarding new data sources takes longer because the platform must be configured to process raw inputs natively, requiring more initial setup and customization effort. Additionally, there may be some discomfort with duplication of data in both a SIEM and an AI SOC solution.
The Kenzo Security Difference
At Kenzo Security, our customers trust us to provide instant time to value, offer deeper investigations at scale, and start preventing, detecting, and responding to threats immediately for every single alert even at scale.
The days of “90 day onboardings” being an industry standard or point of pride are officially over. Unlike legacy SIEM or XDR solutions, Kenzo offers full value from hour 1, and ensures you can configure integrations in 30 minutes or less.
Early AI security solutions claim to replace Tier 1 analysts – we care about providing real outcomes for your team. Kenzo’s Tier 2 SOC analyst agent can autonomously contextualize and recursively investigate reports, and make intelligent decisions based on risk levels.
Ready to embrace the future of security operations? Schedule a demo with Kenzo Security today.
