AI-Native MDR: What It Is and How It Compares to an AI SOC

AI-native MDR is a managed detection and response service built around AI-led investigation. The provider monitors your environment around the clock, investigates the alerts agreed in the contract, and responds where the contract allows, extending your team. AI agents do most of the investigative work, and experienced human reviewers handle the complex cases.

Security operations teams are under pressure to implement AI, and for good reason: backlogs keep growing, alert fatigue isn't improving, and analysts are stretched thin. The usual response is to evaluate AI SOC tools, talk to vendors, and run pilots.

According to Oliver Rochford (ex-Gartner, now Cyberfuturists), whose research with Daylight underpins this guide, that skips an earlier decision: whether to build and run AI yourself or consume it as a service. AI-native MDR is the service option. Teams that never make the choice explicitly drift into one model through tool purchases, and the path gets harder to reverse once it is set.

TL;DR:

• With AI-native MDR, the provider runs the AI, builds the context it depends on, and owns investigation and response within the scope of the contract.
• Most major MDR providers now market agentic AI. The distinctions that still matter are architecture, who owns the decisions the AI makes, stack coverage, and how the service is priced.
• An in-house AI SOC keeps those decisions with your team, along with the ongoing work of building and maintaining the context that makes AI investigation reliable.
• The hidden cost of building is the ongoing labor of keeping AI accurate. Delegating trades that labor for dependence on a provider whose decisions you need to be able to audit.

Ownership Comes Before Tooling

AI changes the SOC question from who runs the SOC to who owns the decisions the system makes. AI systems prioritize alerts, suppress signals, assemble investigation context, and influence outcomes, often before a human is involved, so the choice is about ownership and governance as much as tooling.

If you deploy AI SOC tools internally, you are responsible for how those decisions are made and whether they are correct. If you adopt AI-native MDR, you are delegating part of that responsibility and need to understand how those calls get made for you.

Why Running an AI SOC Is Harder Than It Looks

AI SOC tools can triage and investigate at scale, correlate signals across systems, and cut manual effort, while the response decision and its consequences stay with your team. How well they do it depends on a context system that is easy to underestimate.

AI does not investigate well on raw logs alone. It needs to understand which identities are privileged or sensitive, how assets relate to each other across environments, what normal behavior looks like over time, and how historic knowledge from past investigations can improve future decisions. Without this, AI is only processing incomplete information faster.

This is where AI SOC deployments often plateau. The system produces output, but it is inconsistent, difficult to explain, or not actionable enough to rely on. Analysts compensate by double-checking results, adding exceptions, and gradually losing confidence in the system's ability to operate independently. The limit they hit is usually the organization's ability to supply the context AI needs, long before model capability.

What AI-Native MDR Changes

AI-native MDR moves that work to the provider, which builds and maintains context across environments, operates the AI day to day, and improves decision quality from a broader set of data and investigations. AI agents typically investigate most alerts end to end, and people step in on the complex or ambiguous cases. Providers that investigate autonomously this way usually escalate around 1-3% of cases per month.

Delegating this work brings faster time to value and more predictable outcomes, particularly for organizations that do not have the resources or expertise to build these capabilities internally.

Why Agentic AI No Longer Separates MDR Providers

Over the past year, most large MDR providers have moved onto the same ground. CrowdStrike now markets Falcon Complete as "agentic MDR," with its analysts building and deploying AI agents to automate security workflows. Rapid7 acquired Kenzo Security in March 2026 to bring multi-agent alert investigation into its MDR operations.

SentinelOne's Wayfinder managed services added Claude-powered threat detection and Wayfinder MDR Workflows in August 2026. Sophos launched Fusion in July 2026 as what it calls an "AI-native" defense system and runs its own agentic SOC on it.

Whether a provider uses AI is now settled, and the label on the service says less than it did. The questions that still separate providers are architectural and commercial:

• Whether the investigation layer was designed around AI from the start or added to a service built for human-led triage and escalation.
• Who is accountable for each automated call, and whether you can see and audit the reasoning behind it.
• Whether the service investigates across your whole stack or is tuned for the provider's own endpoint or XDR platform.
• What the price is tied to, since the pricing unit determines how cost moves as your environment grows.

Each of these is a question to put to any provider, including the ones that describe themselves as AI-native.

Deciding Whether to Build or Delegate

Each path has its own requirements. Building internally takes the ability to structure and maintain context across systems, the expertise to operate and improve AI-driven workflows, and the capability to govern and validate the system's decisions. Delegating to AI-native MDR takes confidence in the provider's operating model, visibility into its reasoning, and clear accountability for outcomes.

Neither model is inherently better. The build-or-delegate decision turns on team maturity, 24/7 coverage, liability, custom detections, and integration depth. Choosing the wrong model for your organization's capabilities will create friction quickly, regardless of how strong the underlying technology is.

What AI-Native MDR Costs Compared With an In-House AI SOC

This is also an economic decision. Rochford's report argues that AI has changed the economics of MDR itself: AI-native providers can now offer personalized, expert-grade analysis at price points most organizations could not previously reach. Whether that holds for your organization comes down to the line items below.

The Build Side Is Mostly Labor

The tool license is the most visible cost of building an AI SOC and often not the largest. The system needs sustained investment in data engineering, detection engineering, and ongoing tuning, and most of the effort comes after the initial deployment, in maintaining, adapting, and improving the system over time.

The work usually lands on existing staff or new hires. Analysts spend time validating outputs and investigating inconsistencies. Engineers shape how the system behaves, integrate new data sources, and refine decision logic. Some of the largest costs rarely appear in the evaluation at all:

• Context maintenance: every reorganization, new SaaS application, or policy change leaves part of the context stale until someone updates it.
• After-hours coverage: the tool can investigate around the clock, but the response decision stays with your team, so someone still has to be reachable at night and on weekends.
• Turnover: when the engineers who tuned the system leave, much of the knowledge about why it behaves as it does leaves with them.
• Wrong verdicts: the consequences of a missed alert stay with your organization, and the tool vendor typically carries no liability for them.

These costs grow with the environment.

The Delegate Side Is a Subscription and a Dependency

AI-native MDR turns most of that labor into a subscription. You are paying for an outcome, and the engineering and tuning work described above becomes the provider's cost.

The fee is only one piece of the cost. Connecting tools is usually the quick part of onboarding. Capturing the business context the AI relies on takes considerably longer and continues for as long as the business keeps changing. Your team's time also moves toward answering the provider's questions about the business, reviewing the cases it escalates, and acting on its findings.

Dependency is the other cost. You trade internal investment for reliance on a provider, and gain more predictable costs at the price of some control. Ask what happens to the context built during the contract, including knowledge, tuned detections, and investigation history, if you leave, since its portability decides what switching would cost later.

What the Price Is Tied To

Pricing units show how cost will move as your environment changes. MDR contracts are commonly priced per endpoint, per user, by data volume, or as a flat subscription, and some providers price add-ons such as phishing investigation, extended log retention, or incident response hours separately. Each MDR pricing model moves differently as you add devices, identities, and data sources. Price out next year's environment as well as this year's before comparing quotes.

Compare performance, then decide where you want to carry the cost and complexity of making AI work.

How to Evaluate an AI-Native MDR Provider

Traditional criteria such as coverage, integrations, and response times still matter, but they are no longer sufficient. Judge AI-native MDR on how it governs decisions as much as on what it can do.

Ask how the provider builds and maintains context for your environment, and which decisions the AI makes automatically and which it routes to people. Ask what happens when the AI gets a verdict wrong: how the miss is found, how you are told, and how the correction feeds back into later investigations. Then test the answers on your own alerts during a proof of concept.

AI-Native MDR Is an Operating Model Choice

Settle the operating model before vendor evaluations start. It decides who builds the context, who owns the AI's decisions, and where the cost of keeping AI accurate lands. The choice of tool or service follows from it.

Daylight delivers AI-native MDR, and the evaluation questions above apply to it as much as to any other provider.

Frequently Asked Questions About AI-Native MDR

Is AI-Enabled MDR the Same as AI-Native MDR?

Sometimes, but not always. Rochford's report uses "AI-enabled MDR" broadly for any outsourced service that runs AI on your behalf, and vendors apply both labels loosely. Used precisely, AI-native MDR means the investigation layer was built around AI from the start, while "AI-enabled" can also describe an established service that added AI to existing workflows. Neither label is a guarantee, so ask what AI performs in an investigation and what people perform.

Does AI-Native MDR Replace an Internal Security Team?

No. It extends the team by taking on round-the-clock monitoring, investigation, and contracted response, while your team keeps ownership of security strategy, business decisions during incidents, and any work it chooses to keep in-house, such as detection engineering. Contracts typically also define which response actions the provider can take on its own and which need your approval, such as disabling an executive's account or isolating a production server.

How Long Does AI-Native MDR Take to Deliver Value?

Live investigations usually start within days of connecting tools, and the better measure is how quickly escalations fall. Ask a provider how the share of alerts closed without escalation changed over the first months for customers with environments like yours, and confirm the trend on your own alerts in a proof of concept before signing.

Can You Move From an AI SOC Tool to AI-Native MDR Later?

Yes, and the work done while running an AI SOC is rarely wasted. Integrations, documented policies and exceptions, and notes on why past alerts were closed are exactly the context an AI-native MDR provider needs, so a team that has captured them usually onboards faster. The harder part is the ownership change: decide in advance which decisions move to the provider and which stay with your team.

Do AI-Native MDR Providers Work With Existing Security Tools?

Generally yes, since the service investigates alerts from the tools you already run, such as EDR, identity, cloud, and email security. Integration depth varies widely between providers. Ask how many alert types from each tool the provider actually initiates an investigation for, since a listed integration can still leave most of a tool's alerts uninvestigated.