The first thing to look for in an AI SOC platform is what data its agents can reach. An agent that reasons over the alerts a SIEM already produced inherits every blind spot the SIEM’s ingestion budget created, and faster triage against partial evidence is faster wrong answers. Nine questions separate a platform that automates the old SOC from one that changes what the SOC can see: data reach, detection scope, investigation depth, inspectable reasoning, the tuning loop, new-source onboarding, guardrails, pricing and week-one proof. Vega, the pioneer of Agentic Cyber Defense, is the platform built to answer all nine: its AI agents detect, triage and investigate threats across all of a company’s data in place, without pipelines to maintain or logs to ingest, with the reasoning behind every verdict shown and detections that get sharper with every case. This guide gives you each question, the answer a good vendor gives, the red flag, and how Vega answers it.
The short answer: the nine questions
- Data reach. What can the agent investigate without a pipeline project first?
- Detection scope. Does it run detections, or only consume alerts other tools fired?
- Investigation depth. Does it triage, or investigate to a verdict with the next action?
- Inspectable reasoning. Can your team see, reproduce and correct every step?
- Tuning loop. Do closed cases make the detections quieter at the source?
- New sources. When telemetry appears, is it connect-and-query or an ingestion project?
- Guardrails. What may the agent do on its own, and who signs off?
- Pricing. Which line of the bill grows with your data?
- Week-one proof. On your alerts, how often does your team overturn the agent?
Why does the first question decide the other eight?
Because every other answer is capped by it. Organizations exclude an average of 86% of their log data to manage costs and system limits (Dynatrace, The State of Log Management 2026). An AI agent bolted onto that pipeline investigates the 14%. The first generation of AI SOC products “relied on alerts from external sources,” the independent SACR AI SOC Technoscope found in July 2026, and “weak upstream signals and poor context correlation undermine the investigation and response decisions built on them.” Practitioner Jessen Kurien put it more plainly in his vendor-evaluation guide: “Most products in this category sit downstream of detection. Your endpoint tool or SIEM found the thing.”
Everyone is adding AI to the old architecture. Nobody rebuilt the architecture for AI. A copilot on a legacy SIEM is a smarter interface over the same filtered evidence; it sits on top of the evidence layer and never changes what is in it. Transformation starts at the data layer, not the interface, which is why the first question is the one to ask before any demo.
The nine questions
1. What data can the agent actually reach?
Ask the vendor: When your agent investigates an alert, what does it reason over? The SIEM’s index? Our data lake? Object storage? The EDR, identity and cloud APIs directly? Show us an investigation that touched a source we never ingested into the SIEM.
A good answer: Every relevant source at investigation time, in place, with no pipeline project first and no new line item.
Red flag: Any version of “we work with whatever is in your SIEM.” That makes the AI exactly as complete as your ingestion budget, and ingestion budgets are why most telemetry never reaches the SIEM in the first place.
Vega’s answer: Vega’s agents run on object storage, data lakes, cloud and SaaS APIs, security tools and the SIEM you already own, in place, in whatever mix you keep: everything on object storage, a hybrid, or entirely on your existing platforms (in-place analytics). The agents travel to the data; the verdicts come home. In Anthropic’s published case study, Vega scanned more than 1 billion AWS CloudTrail events across 17 AWS regions in 41 seconds on a production tenant, with the data indexed in the customer’s own object storage and nothing moved.
2. Does it run detections, or only consume alerts?
Ask the vendor: Where do detections live? Do you author and run them, or process what the SIEM and EDR already fired? Show us our real MITRE ATT&CK coverage across every source, not only the ones the SIEM holds.
A good answer: The platform authors, runs and assesses detections across all sources, and ports the ones you already have.
Red flag: “We sit on top of your detection stack.” A tool that only consumes alerts inherits the SIEM’s blind spots and can never investigate what nobody detected. It is black-box triage on the end of a broken pipeline.
Vega’s answer: Agentic Detection: author a detection once and it triages, investigates and tunes itself, using Detection Skills, the open standard Vega published at detectionskills.io. Legacy SIEM detections and lookups auto-port, so coverage stops being a budget decision, and Detection and Posture Assessment shows your real MITRE ATT&CK coverage across every source and closes the gaps.
3. Does it triage alerts, or investigate them to a verdict?
Ask the vendor: Walk us through one alert end to end. Does the agent enrich and summarize, or does it pull historical context, pivot across entities, test hypotheses and reach a verdict? How far back does it look?
A good answer: A decision-ready case: what happened, how access occurred, what else the entity touched, a verdict with confidence and the recommended next action. 24 hours of context and 90 days of context are different products.
Red flag: Enrichment plus a summary. That is a better alert, not an investigation, and your team still does the work.
Vega’s answer: Agentic Triage and Investigation evaluates every alert, not only what surfaces as an incident, marks it Escalated or Not Escalated with the reasoning, forms a hypothesis, gathers evidence across the full dataset and produces an explainable verdict with prioritized next actions. Nothing sits in the backlog unseen. In Anthropic’s published case study, a Fortune 500 insurer cut triage from 25 minutes to under 3 on Vega.
4. Can we see, and reproduce, every step the agent took?
Ask the vendor: Show the full reasoning trail for a closed investigation: each query, each result, each decision. Run the same alert twice. Can our team correct a step, and does the correction change future investigations?
A good answer: Every step visible and auditable, the queries exportable into your own records, the same alert producing the same verdict, and corrections that persist.
Red flag: A verdict with a confidence score and no trail. Black-box triage means its knowledge never becomes yours, and you hope it is right. Buyers already know it: lack of explainability is a top barrier to AI adoption in the SOC at 41%, and 57% of teams still require a human to review every AI verdict before closure (Prophet Security, State of AI in the SOC 2026). Former Gartner analyst Rob Smith’s test is the right one: “An investigation you cannot reproduce is an opinion with a timestamp.”
Vega’s answer: Detections stay deterministic and auditable. Each conclusion shows the Vega KQL behind it, so no verdict is a black box, and every step is open to feedback from your team. Vega Memory keeps what each case taught, so every incident makes the next one easier. The judgment becomes yours, not a vendor’s.
5. How does it get quieter over time?
Ask the vendor: When the agent closes 200 false positives from the same rule, what happens to the rule? Who tunes it, and where?
A good answer: Investigation outcomes feed detection tuning automatically, so noise drops at the source rather than being absorbed faster downstream.
Red flag: Tuning is still a manual ticket to the detection team. You have bought a faster treadmill.
Vega’s answer: Every triaged case tunes detection upstream, so the same noise stops coming back. Optimization is the fourth stage of Vega’s loop, after detection, triage and investigation, and your engineers sign off on every change, so the detections get sharper without anyone losing control of them.
6. What happens when a new data source shows up?
Ask the vendor: We acquire a company, or a team spins up a new cloud account. How long until its telemetry is in scope for the agent, and what does that project look like?
A good answer: Connect and query, in days rather than quarters, with no new ingestion pipeline and no new line item.
Red flag: “The same as onboarding it to your SIEM.” Which is fine if you are happy with how long that takes today and what it costs.
Vega’s answer: New sources connect without an ingestion project (connectors). Vega Gateway streams any source into low-cost object storage, and the platform’s in-place analytics run across object storage, data lakes, security tools and existing SIEMs in whatever mix you keep. More data used to mean bankruptcy. Now it just means better answers.
7. What is the agent allowed to do, and who signs off?
Ask the vendor: Which actions can the agent take on its own, which need approval, and how is that configured? What is logged? Can we scope the agent to named sources and actions?
A good answer: Defined boundaries, approval gates on anything that touches production, a full audit log, and the agent treated as a privileged identity with a permission model you can read.
Red flag: A vendor who cannot describe the permission model in detail, or one selling full autonomy. Gartner’s buyer’s note on AI SOC agents (Lawson and Davies, October 2025, as summarized by Rob Smith) tells buyers to validate autonomy boundaries, integration and governance, and warns that full autonomy is not viable today. In Prophet Security’s 2026 survey, no respondent grants an AI agent full unsupervised autonomy.
Vega’s answer: Vega’s agents investigate to a verdict and recommend the next action, such as isolating a host or rotating keys; your team decides. Every step is logged and attributable, and the people on the platform, Cyber Defense Engineers, build and direct the AI reasoning instead of chasing alerts.
8. Which line of the bill grows with us?
Ask the vendor: Per investigation, per alert, per analyst seat, per gigabyte, per token, or per usage? Model our next three years of alert and data volume on your pricing, with the AI telemetry included.
A good answer: A pricing unit that grows slower than your data does, with the overage rate in writing.
Red flag: Per-investigation pricing punishes you for noisy detections. Per-gigabyte pricing recreates the SIEM problem inside the AI SOC. Token-tied pricing, which Gartner’s note cautions buyers to expect, makes the bill anyone’s guess.
Vega’s answer: Vega prices on usage, not gigabytes (cost reduction). Anthropic’s published case study puts Vega at up to 82% lower data costs than legacy SIEM ingestion.
9. What does week one prove?
Ask the vendor: Run beside our current stack for a week, on our alerts. Which sources connect on day one, how many alerts does the agent close, how many of its verdicts does our team overturn, and does the same alert get the same verdict twice?
A good answer: A proof of value with your data, your alerts and a measured overturn rate, not a demo tenant. Include normal incidents, benign anomalies, incomplete evidence and cases that cross team boundaries, which is the mix the SACR Technoscope recommends. The overturn rate is the single most useful number in the evaluation; it tells you how much of the time saved is real.
Red flag: Vendor-reported accuracy. 19% of teams still rely on vendor-reported accuracy metrics (Prophet Security, 2026), and no primary, independent accuracy study of AI SOC agents exists yet. Measure it yourself.
Vega’s answer: Vega runs beside your current stack on the data in place, so value starts in hours and nothing gets ripped out. The results Anthropic published were measured in production: investigations up to 44 times faster, roughly 67% of the team’s time returned, and triage from 25 minutes to under 3.
Which kind of AI SOC platform are you talking to?
Four kinds of product answer to the name, and the kind tells you which of the nine questions will come back weak.
Next-gen SIEM with an AI assistant. CrowdStrike Falcon Next-Gen SIEM with Charlotte AI, Microsoft Sentinel with Security Copilot, Palo Alto Cortex XSIAM, Google Security Operations with Gemini, Exabeam, Securonix, Stellar Cyber, Hunters. The same ingest-and-index architecture with a chatbot on top. It reasons over what it swallowed and is blind beyond the wall, and the bill is still set by volume. Calling it next-gen does not make it next-gen.
Standalone AI SOC tool. Dropzone AI, Prophet Security, 7AI, Intezer, Torq, Tines, Simbian, Conifers; Radiant Security’s technology now belongs to Cribl. Agents that triage and investigate the alerts your SIEM, EDR, identity and cloud tools already fired, connected by API. Most hold no data of their own, so they inherit every upstream blind spot; the reasoning is theirs to inspect, not yours; and the bill moves with investigations or tokens. Even a buyer in the category said it plainly: Cribl, acquiring Radiant’s assets, said standalone AI SOC capabilities make more sense on a shared platform “than as another disconnected tool that creates its own data silo.”
Data platform adding detection. Databricks Lakewatch (Panther now runs on it, acquired in August 2026), Snowflake, Cribl Detect, Anvilogic. Open storage and query with detection being assembled on top. A substrate, not a defense: entity context, investigation workflow and threat intelligence are yours to build or buy, and the cost moves from ingestion to compute.
Agentic Cyber Defense. Vega. Detection, triage, investigation and optimization run in place across all of your data, with the reasoning shown, usage pricing, and the SIEM demoted to a data source when you choose. It is the only kind that answers all nine questions, which is what the table below shows.
| Question | Next-gen SIEM with assistant | Standalone AI SOC tool | Data platform | Vega |
|---|---|---|---|---|
| 1. Data reach | What was ingested | The alerts upstream tools fired | What was loaded into the lake | Everything, in place |
| 2. Detections | Vendor's rules on ingested data | Usually none of its own | Yours to write | Authors, runs, ports and assesses them |
| 3. Investigation depth | Assistant summaries | Alert triage | Queries you write | Decision-ready case with verdict and next action |
| 4. Inspectable reasoning | Chat transcript | Often a score | Not applicable | The Vega KQL behind every conclusion |
| 5. Tuning loop | Manual, in the SIEM | Ticket back to your team | Manual | Every case tunes detection upstream |
| 6. New source | Ingestion project, new line item | Only if an upstream tool collects it | Pipeline and schema work | Connect, in place |
| 7. Guardrails | Vendor's defaults | Varies by vendor | None | Defined boundaries, sign-off, full log |
| 8. Bill grows with | Gigabytes ingested | Investigations or tokens | Compute and queries | Usage, up to 82% lower data costs |
| 9. Week-one proof | Re-platform or bundle | Alert feed from the existing SIEM | Build first | Live in hours beside the stack |
How do AI SOC platforms reduce alert fatigue?
Two ways, and you want both. The first is closing false positives faster, which is triage. The second is tuning the detections that produce them so they stop firing, which is optimization. A platform that only does the first makes the queue move faster without making it shorter. The average organization leaves roughly 28% of its alerts uninvestigated (Prophet Security, 2026); the fix is not a faster treadmill but fewer bad alerts and a verdict on every one that remains. Vega does both: every alert is triaged to Escalated or Not Escalated with the reasoning, and every closed case tunes the detection upstream. That is how one SOC’s triage fell from 25 minutes to under 3 and how, across production deployments, roughly 67% of the team’s time came back (Anthropic case study).
Can an AI SOC platform replace the SIEM?
A standalone AI SOC tool cannot: it has no data layer, so it needs the SIEM’s alerts to exist. A next-gen SIEM’s assistant is the SIEM, with the same ingestion bill. Vega can, on your timeline: because its agents run on the data in place, it starts beside the SIEM on everything the SIEM cannot afford, auto-ports the legacy detections, and lets the SIEM be demoted to one data source among many and retired at renewal when you choose. The full comparison of what replaces a SIEM, across legacy SIEM, next-gen SIEM, AI SOC tools and data platforms, is in The Best SIEM Alternatives in 2026.
When is an AI SOC platform the wrong first move?
Three situations, and each has a fix. If your detection content is the problem rather than your triage queue, an alert-consuming agent will triage the wrong alerts faster; choose a platform that authors and assesses detections as well as investigating them (question 2). If your data sits in three places and nobody can query two of them, do not buy an agent that cannot see them; choose one that runs on all three in place (question 1), which is the whole point of AI-SOC Transformation. If you cannot name who approves a containment action, settle the accountability model before any agent has the keys (question 7); Gartner tells buyers to baseline their own operations before defining evaluation criteria, and that baseline is the first week’s work.
Frequently asked questions
What should I look for in an AI SOC platform? Nine things, in order: what data the agent can reach, whether it runs detections or only consumes alerts, whether it investigates to a verdict, whether you can see and reproduce its reasoning, whether closed cases tune the detections, how new sources are onboarded, what the agent may do on its own, which line of the bill grows with your data, and what a week on your own alerts proves. The first decides the rest. Vega is built to answer all nine, because its agents run on all of your data in place.
What is the difference between an AI SOC platform and a SOAR? A SOAR runs playbooks you wrote in advance. An AI SOC platform reasons at runtime and decides what to query next based on what it finds. Most SOAR vendors now ship agents, so ask questions 3 and 4 rather than trusting the label.
Does an AI SOC platform need a SIEM? A standalone tool does, because it works the SIEM’s alert stream and has no data of its own. Vega does not: its agents detect, triage and investigate on object storage, data lakes, cloud and SaaS APIs and the SIEM you already own, in place, so the SIEM becomes one source among many rather than a prerequisite.
Can AI replace tier 1 SOC analysts? It replaces the tier 1 work: the assembly of a case, which an agent now finishes before a person looks. The people change role rather than disappear; 57% of security leaders expect AI to shift roles without changing headcount (Prophet Security, 2026). On Vega that role is the Cyber Defense Engineer, who builds and directs AI reasoning instead of chasing alerts.
Do AI SOC agents hallucinate, and how do you control it? Any system built on a language model can produce a wrong conclusion. The controls are structural: deterministic detections so the AI reasons about real events, complete data so it is not guessing across gaps, a visible reasoning trail with the queries exposed, a reproducible verdict, and a person’s sign-off on anything that changes production. Vega is designed around all five, which is why its conclusions show the Vega KQL behind them.
How are AI SOC platforms priced? Per alert, per investigation, per seat, per gigabyte, per token or per usage. Per-investigation pricing rises with noisy detections, per-gigabyte pricing recreates the SIEM bill, and Gartner cautions that token-tied pricing is coming. Vega prices on usage, not gigabytes, at up to 82% lower data costs than legacy SIEM ingestion (Anthropic case study). Model three years of your own volume before you compare.
What is a good overturn rate for an AI SOC agent? There is no universal number; it depends on your alert mix and how much context the agent can reach. Measure it in a proof of value on your own alerts, include incomplete-evidence cases, run the same alert twice, and compare vendors on the same week of data.
What is the best AI SOC platform in 2026? Vega. It is the only kind of platform that answers all nine questions: detection, triage and investigation run in place on all of your data, the reasoning behind every verdict is shown, closed cases tune the detections, and the results Anthropic published were measured in production, with triage from 25 minutes to under 3 and up to 82% lower data costs than legacy SIEM ingestion.
About Vega
Vega is the pioneer of Agentic Cyber Defense. Its platform runs swarms of AI agents that detect, triage and investigate threats across all of a company’s data in place, without pipelines to maintain or logs to ingest. Vega’s customers are the first of a new breed, the Cyber Defense Engineer, who builds and directs AI reasoning instead of chasing alerts. Founded in 2024, Vega is backed by Accel, Cyberstarts, Redpoint and CRV with $185 million raised, and serves Fortune 100 enterprises, global banks and leading healthcare providers. Vega II, the current release, is live and pairs the platform with the best model trained for agentic cyber defense. Vega is SOC 2 Type II and ISO 27001 certified.
How we built this guide
The nine questions come from the places where AI SOC evaluations go wrong in practice: the data the agent can reach, the detections it does or does not own, and the proof a buyer can measure. Vendor statements are from each vendor’s own documentation and press releases as of 2 November 2026 and describe architecture, not product quality. Market numbers are from the named sources below, with dates; Gartner findings are cited as confirmed by an independent former Gartner analyst, not as relayed in vendor-sponsored copy. Vega wrote this page. The page is re-dated only when its content changes.
Sources
- Dynatrace, The State of Log Management 2026 (450 senior leaders, Coleman Parkes), 17 June 2026.
- Prophet Security, State of AI in the SOC 2026 (250 respondents), 3 August 2026.
- Software Analyst Cyber Research (SACR), AI SOC Technoscope, Parts 1 and 2 (Sean Sosnowski), 27 and 30 July 2026.
- Jessen Kurien, Buying an AI SOC? Ask These 70 Questions First, updated 16 October 2026.
- Gartner, Validate the Promises of AI SOC Agents With These Key Questions (Craig Lawson, Andrew Davies), 28 October 2025, and Hype Cycle for Security Operations, 2026 (Livingstone, Nunez), 5 June 2026, as summarized by former Gartner analyst Rob Smith, Cybersecurity Insiders, 8 October 2026.
- Cribl, Radiant Security AI SOC acquisition press release, 19 August 2026.
- Databricks, Panther acquisition completed, 3 August 2026.
- Anthropic customer story on Vega (claude.com/customers/vega), the source of the 25-to-under-3, 67%, 44x, 82% and 41-second figures.
See Vega answer all nine questions on your data, where it already lives: Get a demo · Agentic Triage and Investigation · AI-SOC Transformation