In a time when AI threats have revolutionized nearly every aspect of the cybercrime economy, it is not surprising to see alarmed organizations looking for an equally powerful response.

The problems are what they have always been – too many alerts, coming in from too many places, with investigative evidence scattered across too many sources, and not enough people to manually handle them.

Cue the solution: AI SOC platforms.

If you’re here, you know this and have been through the pain point/product journey already. You understand why AI SOC platforms are key to staying ahead of the deluge of today’s threats, and you are ready to plunk money down for an investment that hopefully lasts a lifetime – at least in terms of cybersecurity shelf life.

How do you pick the right AI SOC platform without falling for the hype that typically goes into these tools? How can you avoid falling for the “AI SOC” label alone, assuming all AI SOCs perform the same? If you remember the initial XDR hype, this won’t seem so far afield.

The answer is to separate fact from fiction in the AI SOC space and debunk the buyer’s myths that are out there.

Here are just a few.

Hype: All AI SOC platforms serve all environments.

Reality: This is incorrect. AI SOC solutions function in different environments, depending on the vendor, with the rare few being environment-agnostic. For example, tools like Microsoft (News - Alert) Copilot, Charlotte AI (CrowdStrike), Purple AI, and Darktrace AI all work best (or exclusively) within their own customer base.

If you align all your internal products with one of these vendors, that could be a potential benefit. However, if you are looking to scale beyond single-vendor solutions alone, or if you already have a patchwork stack you love, you are going to want to consider other options.

Find a solution with full-stack integration, ingesting alerts from the SIEM, cloud security, EDR, and identity tools you already have. To maximize your ROI, find an AI SOC that works perfectly well despite,  or because of, your existing investments.

Note: While you’re at it, look for AI SOCs that are data-agnostic as well. The best AI SOC tools, or the most flexible, are designed to ingest data of all sources and types – meaning no SIEM required.

Hype: All AI SOCs use the “same AI”

Reality: Again, wrong. Not all AI is created equal, but lines can be blurred between which types are, or are not, being used. For example:

• AI: Traditional artificial intelligence powerfully analyzes massive amounts of data and delivers output and analysis based on what it sees.
• Generative AI: GenAI does everything that traditional AI can do, only with the added power of being able to generate unique content. As noted in Forbes, “Traditional AI can analyze data and tell you what it sees, but generative AI can use that same data to create something entirely new.”
• Agentic AI: Agentic AI features the ability for decision-making, or “agency.” This means that agentic AI models can make proactive choices based on the data they analyze, the direction they are given, and the conclusions they draw.

Most AI SOCs will naturally feature traditional AI and boost output with generative AI as well. Look for the ones that rely heavily on agentic AI and optimally combine the types for the “full effect.” It is only through leveraging the decision-making prowess of agentic AI that AI SOC platforms truly relieve the burden on human-staffed SOCs.

For most, that goes beyond just sorting, sifting, and pruning raw alerts. The ultimate SOC task is to coordinate response across sprawling and complex workflows, and across multiple tools – in real-time, no less. This is what agentic AI can offload, and the burden SOCs should be seeking to remediate.

Hype: All AI SOC platforms automatically keep your data safe.

Reality: Not necessarily. Do they know how? Yes. Do they do it? To some extent, and that extent often depends on the vendor.

The issue of customer data safety (yours) when dealing with AI SOC platforms shouldn’t be prohibitively frightening, but it is worth looking into. If customer data is properly segmented, meaning your data cannot be ingested by the same AI models that ingest the sensitive data of other customers’ data, things are fine.

Problems happen when those models take in everyone’s data at once, using them to train on and, ostensibly, provide better outputs for all involved. That, however, puts everyone’s data at risk of being accessed by everyone else, as sensitive proprietary information is sucked into the ether.

Make sure you quiz your prospective AI SOC vendor and look for one with a privacy-first approach. Customer data should never be used to train LLMs, and all actions should be auditable, explained, and under your control. No “black box” techniques here.

Conclusion: Making the Right Choice

These three myths are only the beginning. According to Prophet Security, a leading AI SOC Platform provider, there are several weak points worth vetting before you buy. Among them are:

• “Doesn’t mimic the investigation methodology of an expert SOC Analyst”
• “Built on static playbooks”
• “Bounded autonomy means some actions still require human oversight.”
• “Limited to [its] own ecosystem... Vendor lock-in a concern.”
• “[L]acking native reasoning capabilities”

Keep these in mind as you debunk more myths about AI SOCs and make your ultimate decision. A lot of misconceptions prevail around AI SOC platforms, and this is understandable given the fact that they are so new and promise to do so much.

But (and this is not just hype) when you get a good one, they really do deliver.

About the Author:

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 (News - Alert), and many other sites.