Daylight's MDR is a commercial managed detection and response solution by Daylight Security. UnderDefense AI SOC is a commercial AI SOC solution by UnderDefense. Features, integrations, deployment and the company behind each one, side by side.
What our shortlists say about the one we picked.
Best for: Teams wanting every alert investigated to a verdict by AI and senior experts
Daylight earns its place because every alert is investigated to a verdict and the reasoning is shown, so you can check the work instead of trusting a black box. It fits mid-market and enterprise teams, and cloud-heavy companies most, that want a service on top of the tools they already run. Ask which containment steps the experts take on their own and which come back to you for approval.
From Best MDR Solutions for Business, Fall 2026
Reviewed by Nika (Nikoloz) Kokhreidze.
AI-Native MDR
Agentic AI SOC platform with 250+ SIEM, EDR & cloud integrations, with 24/7 IR support
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Based on what each listing shows: deployment, company size, price and open source.
Daylight's MDR fits if you want a cloud product.
UnderDefense AI SOC fits if you need both cloud and on-premises options.
Common questions when comparing Daylight's MDR vs UnderDefense AI SOC for managed detection and response.
Daylight's MDR: AI-Native MDR. Built by Daylight Security. Core capabilities include Agentic AI investigation and response, Contextual intelligence from identity and HR systems, End-to-end threat resolution.
UnderDefense AI SOC: Agentic AI SOC platform with 250+ SIEM, EDR & cloud integrations, with 24/7 IR support. Built by UnderDefense.
The table above shows where their features, integrations, deployment and company size fit differ.
Daylight's MDR is made by Daylight Security founded in 2024-01-01T00:00:00.000Z. UnderDefense AI SOC is made by UnderDefense. The vendor behind a product decides its roadmap, support, and longevity, so check each company's profile before you commit.
In part. Daylight's MDR and UnderDefense AI SOC are not in the same category on CybersecTools. Both cover AI SOC. Use the table above to see where features, deployment and company size fit differ.
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