Many security leader are asking the same question right now. We already pay for Microsoft Copilot, ChatGPT Enterprise, or Claude. Why buy anything else?
It is a fair question. These are genuinely impressive platforms. And the honest answer is that they can help with some things. Just not the things that matter most for most SOC teams.
This post is a practical guide to where generalist AI earns its place in a SOC and where it runs out of road.
Where generalist AI platforms actually add value
Let’s be direct about what generalist AI platforms do well in a security context.
They are good at drafting, incident summaries, policy documentation, communication templates, and post-mortems. If an analyst needs to translate a technical finding into plain language for an executive, a general-purpose LLM can accelerate that substantially.
They are useful for on-demand research. Asking a question about a CVE, looking up MITRE ATT&CK techniques, or getting a quick primer on an unfamiliar attack class. These are real productivity wins.
They can assist with simple scripting and query construction. Writing a KQL query for a Sentinel rule, generating a Python snippet to parse a log format. Useful, time-saving work.
The common thread is that these are assistance tasks. A human still needs to initiate the process while the AI is a capable co-pilot. And for these use cases, a general-purpose tool is perfectly appropriate.
Where generalist AI runs out of road
The problem is that none of those use cases address the actual constraint facing most SOC teams.
Security teams are not failing because analysts lack knowledge or work too slowly. They are constrained by investigative capacity. Alert volumes are rising. Environments are growing. Attacks are moving faster. And the operating model still assumes humans will triage and investigate the majority of what comes in.
When that assumption breaks down, investigation becomes selective. High-severity alerts get attention. Medium alerts accumulate. Low-severity alerts are deferred or auto-closed. And the uncomfortable truth is that real attacks frequently begin as weak signals. Credential misuse, living-off-the-land techniques, early-stage lateral movement. They rarely present as critical alerts. They appear ordinary until someone actually investigates them.
Generic AI does not fix this. Here is why.
Generalist AI is built for breadth, not depth
ChatGPT and Microsoft Copilot are built for general-purpose text generation. Forensic investigation of a suspicious process execution chain, or a cloud misconfiguration alert at 3am, requires domain-specific knowledge and structured reasoning those platforms were not designed to provide.
Generalist AI assists but does not execute
Even with a great prompt, a general-purpose AI is accelerating an analyst’s workflow, not replacing the need for one. The investigation still depends on human capacity. And human capacity does not scale as fast as the alert surface grows.
Generalist AI KPIs are increased token usage
Microsoft’s KPI, for example, is token usage. More engagement equals more revenue, regardless of whether your security outcomes improved. That is not a subtle difference. It shapes every product decision, every definition of success. And this can result in very high costs for SOC teams heavily relying on these platforms. This is in stark contrast to Intezer AI SOC which selectively uses LLMs while primarily executing forensic investigations with highly scalable tools and processes.
Read more about how Intezer Forensic AI SOC follows Anthropic’s best practices.
A practical AI decision framework
Use generalist AI when:
- The task requires drafting or synthesizing text and security context is not critical to the output
- An analyst is researching something unfamiliar and needs a starting point
- The work is advisory and a human will validate and act on every output
- Speed of completion matters more than forensic accuracy
Consider purpose-built AI when:
- You need investigation to happen without an analyst driving every step
- Alert volume has outpaced the team’s capacity to investigate manually
- Medium and low-severity alerts are going uninvestigated because there simply is not time
- You need verdicts accurate enough to act on, not just suggestions to review
The line between these two categories comes down to one question. Do you need AI assistance, or do you need AI execution?
What autonomous execution actually requires
This distinction matters because it shapes what you need from a platform.
Assistance is achievable with a good LLM and a capable prompt. Execution requires something harder: accuracy and forensic depth at investigation time.
General-purpose AI tools and many first-generation AI SOC products rely primarily on LLM analysis and SIEM queries. That is not enough to produce verdicts you can trust without a human checking every one.
Intezer AI SOC is built for the execution side of that line. Automated evidence collection, threat intelligence correlation, network forensics, endpoint forensics, and reverse engineering. That additional depth is what generates the high-confidence verdicts that allow organizations to trust the outcome without a human reviewing every decision.
Below a certain threshold of accuracy and depth, AI assists humans. Above it, organizations can safely offload Tier 1 and Tier 2 work entirely. The threshold is not crossed through breadth. It is crossed through domain specialization and forensic rigor.
Intezer’s investigations produce evidence-based verdicts with 98% accuracy. Up to 2% of alerts are escalated as real incidents while the rest are resolved automatically. That is not a productivity improvement. That is a fundamentally different operating model.
The closed loop of triage and detection engineering
There is one more dimension where general-purpose tools fall short and that is detection engineering.
When a generic AI tool helps an analyst triage an alert, that interaction is largely isolated. The outcome does not feed back into your SIEM rules. It does not surface coverage gaps. It does not help you get better at detecting the same class of threat next time.
Intezer’s investigation outcomes feed directly into detection engineering at the source, continuously identifying broken or noisy rules, flagging coverage gaps against the MITRE ATT&CK framework, and generating deployment-ready detection rules informed by real investigation results. The system improves with every alert it processes. Detection gets better based on evidence, not assumptions.
That closed loop is the difference between a productivity tool and an operating model.
Is a single generalist interface with multiple plugins the answer?
There is also an important architectural point worth making. Generalist AI platforms are increasingly effective at consolidating workflows into a single interface, and in theory, you could extend them into security operations through plugins and MCPs. The building blocks exist.
But in practice, stitching together the specialist capabilities needed for real alert triage such as forensic evidence collection, threat intelligence correlation, reverse engineering, network analysis, etc. means sourcing, integrating, and maintaining a patchwork of plugins across multiple providers. Each one has its own update cycle, its own failure modes, and its own gaps. The integration burden falls on your team, and keeping it all working reliably over time is its own operational overhead.
At some point the question becomes whether the effort of assembling and maintaining a DIY investigation pipeline inside a generalist platform is worth it — or whether it makes more sense to use a purpose-built system where those capabilities are already unified, tested, and working together out of the box.
The bottom line
Generalist AI platforms have a real role to play in the SOC. Use them for drafting, research, and analyst-driven assistance tasks. It is good at those things and it is likely already paid for.
But do not confuse that with solving the capacity problem. When investigation still depends on human bandwidth, the alert backlog does not disappear. It just accumulates more slowly.
The future SOC is one where AI executes investigation and humans supervise outcomes. Getting there requires technology purpose-built for that job.
