AI is arriving in security operations as a productivity story: faster triage, better investigations, fewer alerts, and more automation.

That is real. But the larger change is that AI creates new actions, attack paths, and evidence that security teams need to capture and understand. The question is no longer only whether an endpoint or identity behaved suspiciously. It is also: what did an agent know, which tools did it call, under whose authority did it act, what did it change, and what happened next?

SIEM Remains The System Of Record

AI will not eliminate the SIEM or security data platform. It may make the traditional SIEM UI less important, but it makes trusted data more important. If telemetry is incomplete, identity and asset context is inconsistent, or detections are weak, AI simply reaches conclusions faster on top of uncertain inputs.

A modern SIEM should therefore be more than a log store and search interface. It should be an evidence and control layer: a place where data is reliable, context is preserved, access is governed, and consequential actions can be explained.

There may be an opportunity for the SIEM to become the authoritative home of the security context graph: not merely a visualization, but the maintained relationship between identities, devices, workloads, applications, data, permissions, ownership, and policy. AI agents need that context before they can safely reason or act.

AI Creates New Security Telemetry

Traditional telemetry tells us what happened on a system or network. AI systems require us to capture part of the decision path as well. For consequential agent activity, organizations will need to know:

• Which identity and permissions the agent used
• What information and tools it could access
• Which systems it called
• What it changed or attempted to change
• Which policies, approvals, or exceptions applied

This is not only an incident-response requirement. It matters for audit, compliance, and accountability. If an agent changes data that feeds financial reporting, accesses sensitive information, or takes a production action, an application log saying “completed successfully” is not enough. Sarbanes-Oxley may not mention agents by name, but the underlying internal-control and evidence problem is obvious.

Intent Is Interesting, But It Is Not Truth

New endpoint vendors including Neo, Glow, Ent, and others are focusing on a different layer of the endpoint: observing user and agent behavior, then inferring intent. That is worth watching closely. Ent explicitly frames its approach as intent-aware security across human, AI, and application activity, while Neo and Glow are pursuing AI-era endpoint control and prevention from adjacent angles. Ent, Neo, and Glow are useful examples of the emerging category.

Traditional EDR is still needed for attacks that reveal themselves in low-level evidence: process execution, network activity, and known attacker techniques. But those signals can be insufficient when developers, administrators, and AI-enabled workers legitimately perform highly variable and sensitive work. We can find many attacks at the system layer, while insider-risk activity or misuse of legitimate access may remain hard to distinguish.

Intent signals can add context. Is a sequence of actions consistent with a developer deploying software, an administrator performing maintenance, or an agent moving outside its mandate?

But intent is a probabilistic signal, not a verdict. It should complement evidence from endpoints, identity, cloud, applications, data, and agents—not replace it. That is another reason we need a central correlator or analytics platform. Let’s keep calling that a SIEM.

Security Needs Three Clocks

The need to correlate all of that evidence creates a timing problem.

An endpoint or identity control may see a risky action immediately, but the evidence needed to understand it may be spread across cloud systems, containers, SaaS applications, sandboxes, and downstream tool calls. Some context arrives seconds later. Some appears only after an agent has completed a chain of work. Some only matters when an investigator or auditor returns months later.

Security therefore needs to work across three clocks:

• First, there is inline prevention: block the clearly dangerous action before it can do damage.
• Second, there is delayed contextual assessment: reassess the event minutes later, once the surrounding identity, application, cloud, and agent telemetry has arrived and can be rejoined.
• Third, there is long-term reconstruction: retain the evidence needed for hunting, incident response, audit, legal review, and lessons learned.

The point is not to delay every decision. It is to recognize that the best immediate decision and the best informed decision may occur at different times—and both need to be explainable.

What This Changes For MDR

This need to capture agent actions, join them with broader context, and preserve reliable evidence across all three clocks changes MDR too. The future MDR is not simply an AI SOC that processes alerts with fewer analysts. It has to operate the telemetry, detection, context, and accountability loop around AI-enabled environments.

That means ensuring the right telemetry is collected, maintaining the context graph that makes it useful, testing detections, defining which actions can be automated, preserving approvals and exceptions, and proving later what the system saw and did.

AI may reduce manual work in the SOC. It does not remove the need for a trusted security operating layer. When AI acts, security has to remember not only what happened, but why.

A company strategy deck is not a value-creation plan.

Over the past year, I have worked with private equity firms, boards, MSSPs, MDR providers, and cybersecurity vendors on corporate strategy, product direction, packaging, go-to-market, AI, and operating models. The situations have varied, but the underlying problem is often the same:

Most companies do not lack ideas. They lack the operating system required to turn those ideas into results.

They may have a board strategy, product roadmap, sales plan, AI initiative, and financial target. But these often exist as separate artifacts. Product is not aligned with go-to-market. Packaging does not reflect delivery economics. Sales targets assume demand that has not been validated. The board tracks outcomes without understanding their causes.

In a private equity-backed company, the value-creation plan should be the management system that connects the investment thesis to strategic choices, operating initiatives, accountable owners, leading indicators, and financial outcomes. It is not another presentation. It is how the company runs.

TL;DR

• Most strategy problems become operating problems.
• Product, packaging, sales, and delivery have to reinforce one another.
• Growth problems require diagnosis before more investment.
• AI matters when it changes customer outcomes or company economics.
• The CEO owns enterprise priorities, cross-functional trade-offs, and resource allocation; the board tests assumptions and improves the quality and speed of consequential decisions.

Packaging Exposes the Operating Model

I recently looked at a cybersecurity services portfolio that had grown organically over many years. The company had strong capabilities, valuable customer relationships, and deep domain expertise. It also had a large menu of services that could be combined in many different ways.

The initial question was how to improve the packaging. The deeper issue was the operating model.

Salespeople had too many options. Customers struggled to understand what they should buy. Delivery teams supported exceptions, custom combinations, and unclear boundaries between recurring services, add-ons, and projects. We worked backward from the customer: What problem caused the customer to buy? Which outcomes mattered? What should be included by default? What belonged in a fully managed service, a co-managed model, an add-on, or a paid project?

That led to a simpler product architecture with clearer entry points, packages, ownership boundaries, and upgrade paths. But this was not merely a marketing exercise. Packaging determines what sales sells, what customers understand, what delivery has to support, and where exceptions enter the system. It reveals whether product strategy and delivery economics are actually aligned.

A complicated catalog is often presented as customer choice. In reality, it can be evidence that the company has avoided making strategic decisions.

Diagnose Growth Before Funding It

In another situation, a company wanted to accelerate growth across several market segments. Two segments were underperforming, but for very different reasons.

In the first, the company was generating interest but failing to convert enough opportunities. The market recognized the problem, but the offer, proof, packaging, or sales process was not strong enough. In the second, too few qualified opportunities were entering the funnel. The company occasionally won business, but the market was not consistently responding to the message.

One was primarily a conversion problem. The other pointed toward ICP, positioning, channel, or market attractiveness. They required different responses. More marketing will not fix a broken buying process. More sales capacity will not fix a weak market position. Better enablement will not create urgency where customers do not feel it.

Yet companies often respond to a missed target by demanding more pipeline, hiring more salespeople, changing compensation, or launching another campaign. Sometimes that works. Sometimes it simply adds cost to a weak market thesis.

Before allocating more capital, management and the board have to establish what is actually true.

Operating Leverage Is the Real Test

For MSSPs, MDR providers, and other technology-enabled services businesses, productization is not defined by having a portal, integrations, proprietary technology, automation, or an AI story. The question is whether the delivery model becomes more scalable as the company grows.

The clearest test is what the next customer requires. If each new customer brings more tuning, analyst work, custom reporting, manual integration, and delivery exceptions, growth remains labor-dependent. If that customer can be served through reusable detections, workflows, automation, and product improvements, operating experience begins to compound.

I have seen enormous expertise trapped inside analysts, tickets, scripts, spreadsheets, and customer-specific processes. Productization means turning that knowledge into standard workflows, captured context, response policies, and automation so experts handle the exceptions rather than repeatable work. Done well, customer outcomes, consistency, and margins improve together.

AI should be judged by the same standard. It matters when it removes an economic or customer constraint: less repetitive work, faster product development or implementation, lower service-delivery costs, better sales productivity, or a meaningfully better customer outcome. If none of the underlying numbers move, the company may have an AI initiative. It does not yet have an AI operating model

The Board Needs Causality. The CEO Must Integrate.

I have seen board reporting with dozens of accurate metrics but limited operational clarity. The board can see that growth, gross margin, or retention is below plan, but not why. If gross margin is deteriorating, is it pricing, vendor cost, implementation effort, support burden, utilization, or delivery exceptions? If bookings are weak, is the problem pipeline, conversion, sales capacity, positioning, product competitiveness, or retention? And if churn is creeping into the customer base, why are we losing these customers? Probably one of the harder questions to answer, but just saying that they went with another competitor is not going to help build a sustainable business.

A useful value-creation plan makes those relationships visible. It connects the investment thesis, strategic priorities, accountable executives, leading indicators, financial outcomes, and decisions required.

The board should not run the company. It should help management confront the right facts and make consequential decisions quickly. Strong board members do more than review dashboards. They improve the quality, speed, and accountability of decisions.

The CEO’s role is different. Functional leaders optimize their areas. The CEO has to optimize the whole company. A custom feature may help close a deal but increase delivery cost. A broad portfolio may expand theoretical market size but weaken sales effectiveness. Cost reductions may improve short-term EBITDA while damaging product relevance. More services may increase revenue but make the company less scalable. The CEO’s unique responsibility is to connect market reality, strategic choices, product, go-to-market, organizational capability, operating cadence, and capital allocation. This is true in a high-growth company, a private equity portfolio company, a founder transition, or a turnaround. The urgency differs, but the work is remarkably similar: establish what is true, decide what matters, stop what does not, align the organization, and turn the strategy into measurable value.

These are the situations I find most interesting: a strong company entering its next phase, a cybersecurity business that has outgrown its original operating model, or an organization where product potential and market opportunity have not yet translated into consistent execution.

If you are a private equity sponsor, board member, or cybersecurity CEO working through one of these transitions—or looking for an operator to lead the next phase—I would welcome the conversation.

Strategy sets the direction. The operating system creates the value.

In the last post, I argued that AI maturity is not about tool count. The real question is whether AI is changing how the company works.

That leads to the next question: if AI is becoming a company operating system layer, what actually belongs inside that layer? The answer is not “a chatbot for every department.” It is a set of shared capabilities that help the company use context, signals, decisions, workflows, and governance more effectively.

The context layer

Every company has operating context, but most of it lives in places AI cannot reliably use: board decks, strategy documents, Slack threads, CRM notes, offsite summaries, and executive memory.

That context matters because it tells the company what good looks like. AI systems need to understand the company’s ICP, strategic priorities, product direction, pricing logic, market narrative, board-level constraints, and risk appetite. Without that, agents can still generate output, but they are not operating from the company’s actual logic.

This is where many AI efforts stay shallow. They automate tasks without encoding the context that should guide those tasks.

The signal layer

A company AI OS also needs signal. That signal comes from the systems where reality shows up:

• Sales calls and win/loss data
• CRM fields and pipeline movement
• Product usage and telemetry
• Support tickets and implementation friction
• Customer success notes and renewal risk
• Roadmap tradeoffs and engineering throughput
• Financial data, margin structure, and cost trends
• Market signals, competitor movement, and regulatory change

The point is not to dump all of this into a model. The point is to make the right signals available, governed, and useful. A company that cannot connect its customer conversations, product reality, and financial outcomes will struggle to make AI operationally meaningful.

The decision layer

The next layer is decision support. This is where AI should help the company reason across functions instead of just accelerating isolated tasks.

For example, sales may hear that a feature is winning deals, support may see that the same feature is causing implementation pain, product may be planning adjacent roadmap work, and finance may care about the gross margin impact. In many companies, those signals never meet in time to shape a better decision.

A useful AI operating layer should help surface those tensions earlier. It should not replace executive judgment, but it should improve the quality and speed of the inputs that judgment depends on.

The execution layer

Once context, signal, and decision support exist, AI can start to push work forward. This is where agents, workflows, and automations become useful.

The execution layer might help:

• Draft follow-ups from customer calls
• Turn product feedback into structured roadmap input
• Identify sales patterns across calls and accounts
• Prepare weekly operating reviews
• Monitor project slippage
• Summarize customer risk
• Suggest next actions for account teams
• Generate board-ready updates from operating data

This is where AI starts to feel less like a productivity tool and more like part of the company’s operating rhythm. But it only works if the earlier layers are in place. Otherwise, execution becomes more automation noise.

The governance layer

The governance layer is often treated as a blocker, but it should be part of the operating system itself. Companies need to define which models are approved, which data sources can be used, what retention rules apply, where human approval is required, and how agent behavior is monitored. The old version of this is a policy page that few people read. The better version is policy that agents can actually access and follow.

That does not remove the need for human governance. It makes governance executable.

The real test

A company AI OS is not a single platform. It is the connective tissue between company context, business signal, decisions, workflows, and governance.

The test is not whether the company has deployed AI tools. The test is whether AI helps the company operate with better speed, clarity, consistency, and control.

If AI only generates more content, it is still a feature layer. If it helps the company sense, decide, execute, and learn, it is becoming an operating layer.

In a previous post, I argued that AI is becoming a company operating system layer. The natural next question is how to tell whether that is actually happening inside a company.

The answer is not how many copilots are deployed, how many employees have access to ChatGPT, Claude, Cursor, or Codex, or how many AI experiments appear on an executive dashboard. Those are adoption signals. They are not operating model signals.

The better question is whether AI is changing how the company actually works.

Start with the workflow, not the tool

One mistake I see in AI maturity conversations is that they start with the wrong question: “How are you using AI?” It sounds reasonable, but it often produces a shallow answer. People list tools. Teams describe experiments. Executives get a dashboard. Everyone feels like something is happening.

A better diagnostic starts earlier. Before asking about AI, you have to understand how the company operates today:

• How does engineering ship?
• How does product decide what to build?
• How does sales learn why it wins or loses?
• How does customer success detect risk?
• How does finance understand cost and margin impact?
• How does governance decide which data AI systems can access?

If those workflows are unclear, inconsistent, or poorly instrumented, AI will not magically fix them. It will usually amplify the fragmentation.

AI activity is not AI operating leverage

Most companies already have plenty of AI activity. Engineers are using coding agents. Marketing teams are generating copy. Sales teams are summarizing calls. Product teams are drafting requirements. Support teams are experimenting with automation.

The problem is that much of this is happening independently. Different teams use different tools. Different people create different prompts. Different functions maintain different versions of the truth. Governance is often a policy page somewhere, not something agents can actually read and enforce. Management may see usage, but not impact.

That is not an AI operating model. It is distributed experimentation. A company can have high AI usage and still have low AI maturity.

What a real diagnostic should look for

A useful AI maturity assessment should not just ask who has licenses. It should look at whether AI is improving the operating system of the company.

That means asking:

• Are existing workflows documented well enough for AI to assist them?
• Are teams using shared patterns, or is every function inventing its own?
• Are the right data sources available, clean, and governed?
• Can the company measure whether AI improves speed, quality, cost, or predictability?
• Do agents know what data they are allowed to use, or are rules trapped in static documents?
• Is AI helping the company learn from customer calls, support tickets, product usage, roadmap decisions, and financial outcomes?

The point is not to create a more elaborate AI dashboard. The point is to understand whether AI is becoming part of how the company senses, decides, executes, and learns.

The board-level question

For CEOs, boards, and investors, the question should not be: “Do we have an AI strategy?” It should be whether AI is creating operating leverage.

A board should be asking:

• Is AI reducing decision latency?
• Is it improving engineering throughput?
• Is it helping sales understand why deals are won or lost?
• Is it improving retention signal?
• Is it exposing risk earlier?
• Is it changing the margin structure of the business?

If the answer is no, the company may still be in the experimentation phase, even if AI usage looks high.

That is why AI maturity is not about tool count. It is an operating model assessment. The companies that get this right will not be the ones with the longest list of AI tools. They will be the ones that turn AI into a repeatable way to improve workflows, decisions, governance, and execution. And last but not least, companies with clean workflows that are ideally already documented, will win in adopting AI quickly.

In the last two MDR posts, I looked at how MDR has to move beyond alert triage. First, the natural MDR extensions: asset and control coverage, exposure management, SIEM and data quality, cloud and identity posture, response orchestration, and proof that risk actually went down. Then, the deeper architecture: next-gen MDR as an AI-native SecOps control plane, where services become more productized, SIEM becomes symbiotic, and detection has to move into governed response.

This post is the next layer out.

Most conversations around AI in the MSP ecosystem still focus on copilots, workflow automation, and operational efficiency. Those are important, but they miss the larger transition underway. AI is forcing a redesign of the operational infrastructure underneath managed services itself, creating new platform dynamics and potentially large capital market opportunities across the SMB technology stack.

If MDR is becoming an AI-native SecOps control plane, what happens to the broader MSP market? My view is that the same operating shift is coming there too, but with a wider scope. The best MSPs will not just add AI to tickets, service desks, and support workflows. They may become the AI operations layer for SMB and mid-market infrastructure. That is a much bigger idea than “AI for MSPs.”

Services Are Becoming Software-Defined

Historically, software scaled and services did not. Services were people-heavy, exception-heavy, and hard to standardize. Good MSPs could improve process, stack discipline, and technician leverage, but the model still depended heavily on human execution.

AI changes part of that equation.

Support, remediation, governance, workflow routing, escalation, documentation, compliance evidence, and customer reporting can all become more software-defined. That does not eliminate the human operator. It changes the operator’s job from doing every step manually to designing, supervising, validating, and improving the operating loop.

This is why the boundaries between MSP, MDR, SaaS, outsourcing, and consulting start to blur. A managed service that is telemetry-driven, API-connected, AI-assisted, and outcome-accountable begins to look less like a traditional services business and more like an operating platform with humans in the loop.

MDR is one version of this. MSP is the broader version.

Copilot Is Not The End State

A lot of current AI messaging still sounds like copilot: summarize a ticket, draft a response, help an analyst investigate faster, recommend the next step, generate a report. That is useful, but it is not the final state. The customer is not really buying a better chat interface. They are trying to buy operational confidence.

For MSPs and MDR providers, the value stack is moving in this direction:

This is where MSP and MDR converge. The customer does not want a PSA queue, RMM console, SIEM, EDR tool, dashboard, or pile of alerts. They want uptime, resilience, recoverability, compliance, secure productivity, fewer surprises, and someone accountable when the system does not behave the way it should. The product becomes operational confidence.

The Hard Part Is Operational State

This is also where a lot of “AI-first MSP” thinking is still too shallow. The hard part is not putting an LLM on top of a ticket queue. The hard part is knowing enough about the operating environment to take action safely. To automate IT and security operations, the system needs to know what exists, who owns it, what it connects to, what is business-critical, what policy applies, what telemetry is missing, what action is allowed, what could break, who can approve, how to roll back, and whether the action worked.

That requires an operational middleware layer:

This should sound familiar from the MDR discussion. The same architecture problems show up again: context, telemetry, workflow, policy, action, proof. The difference is that MSPs operate across a wider surface area. They touch endpoint management, identity, SaaS administration, backup, patching, compliance workflows, help desk, procurement, user onboarding, security tooling, and business continuity. That makes the AI opportunity larger, but it also makes the operating problem harder.

This is also why I am a proud advisor to FlowMind. They are tackling this problem from exactly the right angle: building a context graph across IT, network, cloud, and security knowledge, then using that context to automate ticket triage, investigation, and response. The important part is not just classifying the ticket faster. It is understanding the live customer environment well enough to recommend or execute the right fix.

AI-First MSP Is A Refounding, Not A Feature Upgrade

The phrase “AI-first MSP” can make the transition sound easier than it is. A true AI-native provider probably needs a different labor model, pricing model, knowledge system, telemetry stack, escalation chain, customer contract, operating metric set, and governance architecture. That is closer to a refounding than a modernization project.

The old MSP model optimized for human leverage: more endpoints per technician, better ticket workflows, tighter vendor standardization, and stronger recurring revenue. The next model has to optimize for governed automation: more safe actions per operator, better telemetry coverage, faster remediation loops, clearer authority boundaries, and stronger proof of outcome.

That also changes what investors and strategic acquirers should underwrite. Scale alone is not enough. Recurring revenue alone is not enough. A large MSP platform with fragmented tools, weak data architecture, inconsistent telemetry, and human-heavy workflows may not be an AI-native operating platform. It may just be a large labor organization with good retention.

The quality question becomes more operational: can this provider turn distribution, trust, and access into a compounding operating system?

The Practical Test

The test is not “does this MSP have AI?” That question is already too shallow. The better test is whether the provider can answer a few operating questions consistently:

This is where diligence should move. Not just gross retention, endpoint count, technician utilization, or EBITDA margin, but whether the operating architecture is actually becoming more software-defined over time.

Distribution Is The Hidden Asset

The strongest strategic asset in the MSP market may not be the software stack. It may be the distribution layer.

MSPs already have something most AI vendors want and cannot easily build: trusted access into the SMB technology environment. They often have admin rights, endpoint access, SaaS integrations, procurement influence, business continuity responsibility, and a working relationship with the customer. That matters because SMBs generally do not have the internal capacity to manage AI transformation, AI governance, security operations, identity, SaaS posture, and workflow automation on their own. If AI agents become part of the workforce, somebody has to manage the operating environment around them. Identity, permissions, logging, workflow access, data boundaries, compliance, security, and incident response all have to extend from human employees to semi-autonomous systems.

That creates a larger role for the best MSPs and MDR providers. They can become SMB operational governance providers, not just outsourced IT or security teams.

The Market Will Not Reward Everyone

The dangerous assumption is that AI automatically increases the value of MSPs. It might. But it could also compress margins, reduce switching costs, commoditize service delivery, centralize operational intelligence in large platforms, and move value toward Microsoft, hyperscalers, or AI-native software vendors. The providers that benefit most will be the ones that turn service delivery into a software-defined operating model. The providers that only add AI features to the old model may see the opposite effect: more pricing pressure, less differentiation, and less tolerance for messy delivery. This is the broader implication of the AI-native MDR thesis. MDR may become the SecOps control plane. MSPs may become the operations and governance layer for the rest of the SMB technology stack.

The categories may keep their old names for a while: MSP, MDR, MSSP, SaaS, GSI, AI SOC. But the operating model underneath them is changing. The strategic question is no longer just who manages the tools. It is who becomes trusted to operate the customer environment as AI becomes part of the workforce.

MDR is not going away, but the old model is. The old model was service-first: collect alerts, have analysts investigate them, escalate what matters, and report what happened. That still solves a real problem, but it is not where the market is going.

The future MDR has to become product-first, AI-native, and action-oriented. It cannot just sit on top of EDR alerts and add analyst labor. It has to become the SecOps control plane that connects detection, investigation, response, exposure reduction, and proof of outcome.

This is not just an “AI layer” on top of alerts. It requires a different architecture, a different relationship with the SIEM, and a different operating model for response.

The Service Business Has To Become A Product Company

Traditional MDR was built as a service business. The product was often the people: analysts, playbooks, escalations, customer calls, and reporting. That model can scale to a point, but it starts to break when the market expects software-speed investigation and response.

Next-gen MDR has to invert the model. The platform becomes the core product, and services become the trust, expertise, and exception-handling layer around it. The test is margin and labor leverage. If more customers create more exceptions, more tuning, and more analyst burden, the company is still a service business. If more customers create more reusable detections, known-good patterns, automation recipes, and response policies, the service is becoming a product. Analysts still matter, but they should supervise, improve, validate, and handle edge cases. They should not be the primary execution engine for every repetitive investigation.

That is a hard transition because many MDR providers are not software companies. They can script, tune, and operationalize, but building a real SecOps platform is a different muscle.

The Moat Is Not AI. It Is Encoded Operational Judgment.

The defensible part of next-gen MDR is not that it uses AI. Everyone will use AI. The defensible part is whether the provider has converted years of SOC judgment into productized decision systems: known-good behavior, investigation procedures, response playbooks, customer-specific rules of engagement, approval policies, and feedback loops that improve the platform over time.

The best MDR providers will not simply have analysts assisted by AI. They will have a compounding operating system where every resolved case can improve future triage, investigation, automation, and response across customers. The weak version is service history trapped in analyst memory and exception lists. The strong version is structured, versioned, measurable operational knowledge embedded into the platform.

And let’s not forget the context graph. The data structure that learns the customer environments. The intelligence layer that fuses together individual data points to deliver a high signal, low friction security outcome.

MDR And SIEM Become Symbiotic

It’s a mistake think (or build a company around) next-gen MDR replaces the SIEM.

The SIEM is there for a reason. It stores logs, supports compliance retention, enables historical search, runs detections, and gives the customer a system of record for security data. Many customers already have years of detection logic, parsers, dashboards, and workflows inside it.

But the SIEM should not be the only operating layer for MDR. If MDR depends entirely on the (customer’s) SIEM, it inherits the SIEM’s latency, schema problems, data opacity, cost model, and workflow limitations.

The future relationship is symbiotic. MDR should use the SIEM’s detection layer where it works, but also audit it, monitor it, and improve it. Are the right logs arriving? Are detections still firing correctly? Are parsers broken? Are detections duplicated, stale, or missing context? Which investigation outcomes should feed back into detection logic?

In that model, the SIEM becomes less of the SOC’s user interface and more of a data and detection layer inside a broader SecOps operating system. It’s also feasible to have the SIEM be the data backend for the AI SOC, but only if it’s under the uninhibited control of the MDR.

AI Changes The Architecture, Not Just The Interface

The visible change in the near future is that analysts and customers will interact with the system(s) through AI. They will ask questions, start investigations, summarize cases, generate response plans, even design their own dashboards, and check what changed.

But the harder shift is underneath. The backend has to be built so AI can interact with it safely and efficiently. That means the platform needs clean APIs, stable entity models, permission-aware access, auditable actions, reusable investigation workflows, and compact context that does not require dumping endless raw logs into a model. And last but not least, the backend has to be designed for AI workloads and in turn, for token efficiency and for the types of (probably very suboptimal) queries that the AIs will send down.

This is why an AI-native SecOps platform is not the same as a chatbot on top of a SIEM. The architecture has to support the way AI investigates, acts, remembers, and learns.

Detection Has To Move Into Automated Response

The current MDR model still assumes a human-centered response loop: a detection fires, an analyst investigates, and someone decides whether to isolate, disable, block, remove, patch, ticket, or escalate. That loop is too slow for many modern attacks.

The future MDR platform has to move toward detection-to-response automation, but not reckless automation. Customers will push back, and rightly so. They do not want an opaque AI system taking production-impacting actions on its own. The answer is to be precise about what “AI” means.

Not every workflow needs an LLM. Much of response should become deterministic rules, policy engines, and pre-approved playbooks. GenAI should be reserved for the places where it helps: summarization, investigation planning, evidence interpretation, query generation, and edge-case reasoning. For most alerts, the system should be predictable, auditable, and constrained.

The second answer is supervision. Some actions can be fully automated. Some should require analyst approval. Some should require customer approval. The platform has to know the difference between removing a malicious email, disabling a privileged identity, isolating a production server, and changing access policy for a sensitive application.

The third answer is guardrails. We are entering the era of validation loops for AI: agents and control layers that check what the system proposes, understand disruption risk, enforce policy boundaries, and monitor whether the action worked. Formal guardrail frameworks, policy-as-code, approval gates, rollback paths, evals, and audit trails become part of the SecOps architecture.

This is where risk becomes the control signal. A suspicious event on a low-value test asset should not trigger the same response as the same event on a privileged identity accessing sensitive data. Risk should influence not only the analyst queue, but also resource access flows: step-up authentication, zero trust enforcement, restricted data access, endpoint isolation, email removal, identity reset, or escalation to a human.

That is the important shift. Detection should not only create an investigation. It should drive protection and exposure reduction.

The Market Implication

Next-gen MDR, AI SOC, and the MSP/MSSP security platform may look like separate markets today. Architecturally, they are converging. The packaging changes by segment, but the platform direction is increasingly the same.

Enterprise buyers will usually want to retain ownership of security operations. They may use managed expertise, but they are more likely to buy an AI-native SecOps platform that compresses L1/L2 investigation, improves consistency, and accelerates response while their internal team keeps control of higher-risk decisions. There is an opportunity for AI SOC players to provide the new sec ops platform for these enterprises as the central command – AI first, SIEM symbiotic.

Mid-market buyers are the most interesting and the most difficult. They often have enough security maturity to know they need help, but not enough staff or process maturity to run a pure platform themselves. This is where productized MDR can become the managed SecOps control plane: part platform, part expert service, part automation layer. The provider has to help operate the loop, not just forward alerts. The lesson is not to bolt on every adjacent service. Exposure management, asset visibility, identity posture, vulnerability prioritization, and response orchestration all matter, but they have to reinforce the MDR operating loop. If the provider adds too much service scope without product leverage, it becomes broad, expensive, and hard to deliver. This is also where we need the vendors to educate the market. Mid-market companies need these capabilities whether they ask for them or not. If they are buying separate tools for exposure management, asset inventory, vulnerability management, identity posture, and reporting, it is reasonable to ask why the MDR provider should not help operate those workflows. The customer can still supervise decisions, set policy, and approve sensitive actions. But the MDR should increasingly coordinate the operating loop. Ideally MDR players will open up their platforms for end customers to co-deliver security results.

SMB buyers may never experience this as an AI SOC platform. They may experience it through an MSP or MSSP security platform that bundles endpoint, email, identity, compliance, patching, response, and reporting all within one simple, per user license. But underneath, the same direction applies: more product, more automation, more risk context, more proof.

The future MDR is not an AI layer on top of alerts. It is not a cheaper SOC. It is not a duplicate SIEM. And it is not a service desk with better automation. The future MDR is a productized SecOps control plane: symbiotic with the SIEM, directly integrated with the customer’s enforcement tools, risk-aware in how it prioritizes and responds, deterministic where safety matters, AI-native where reasoning helps, and measured by proof of risk reduction. The companies that win will be the ones that turn service experience into compounding product advantage. The companies that lose will still have analysts, alerts, dashboards, and AI summaries, but no real moat.

I had a few conversations over the past days that all pointed to the same conclusion: many technology companies are still being built like old SaaS companies. That is a mistake. If you are building a technology product now, the priority is not a polished frontend. It is the backend: the data layer, the ontology, the APIs, the analytics layer, the authentication model, and the infrastructure that makes AI agents fast, reliable, and cheap to run on top of the data backend. The frontend still matters, but it should not be the center of gravity anymore.

TL;DR

• Start with the backend and data model, not the dashboard.
• Build for token efficiency as a product requirement, not just an infrastructure metric.
• Expose core capabilities through APIs and agent-friendly interfaces first.
• Keep the UI light, flexible, and increasingly self-serve.
• If every deployment needs heavy forward deployed engineering, the product is not ready yet.

The Moat Is Moving Down the Stack

In the old SaaS model, a lot of value sat in the application layer. You built workflows, dashboards, role-based views, and configuration screens. In AI-native software, that is no longer enough. The durable part of the company is increasingly lower in the stack: the system that structures data correctly, retrieves the right context quickly, exposes useful actions cleanly, and does all of that in a reliable and token-efficient way.

If that layer is weak, the rest of the product becomes slow, expensive, brittle, and hard to customize. If that layer is strong, you can build a surprising amount on top of it very quickly.

The UI Should Get Thinner

A lot of teams still think about product development as: first build the dashboard, then add AI to it. I think it is increasingly the opposite. First build the backend that can answer questions, retrieve context, execute actions, and expose capabilities cleanly. Then add lightweight interfaces on top.

Initially, those interfaces may be very thin. In some cases they may barely be a product UI at all. A technical user might interact through Claude, another agent interface, or an internal tool layer. Over time, you can add more purpose-built interfaces and dashboards, but those should sit on top of a backend that already works well in a headless way.

Token Efficiency Is a Product Decision

One of the bigger mistakes right now is treating token usage as a backend optimization problem. It is not. It is a product design problem. If your system cannot give agents the right context in the right shape, the product becomes costly to operate and difficult to scale. That affects margins, response times, user experience, and the kinds of workflows that are even viable.

This is why the backend matters so much. You need data structures, query systems, and analytics layers that are built for AI interaction, not just for human dashboards. A beautiful interface on top of an inefficient backend is not an AI product. It is a demo with a future cost problem.

The Goal Is Self-Serve Customization

A lot of tech companies are also running into the same trap: they need too much forward deployed engineering to make each customer successful. That is understandable for now, but it is not where you want to stay. The goal should be to make the platform configurable enough that a solutions engineer, a sales engineer, or eventually even the customer can shape the experience without constantly pulling in core backend engineers.

That only works if the system is designed the right way. If the logic, data model, and capabilities are modular and exposed well, you can let people create their own views, workflows, and operating layers on top. If not, every customer request turns into a product detour.

Build the engine first. Build the data layer properly. Make it fast, cheap, reliable, and cleanly exposed. Then let the frontend become lighter, more dynamic, and more self-serve over time. That is increasingly the difference between an AI first company and a SaaS company with an AI feature.

During my engagements with various Private Equity and Venture Capital outlets, I see a clear shift. The questions that is showing up more and more in due diligence is no longer, “What is your AI strategy?”

It is: “How far along are you in rebuilding the company around AI?”

That is a different question.

It applies to startups and incumbents alike. It applies to security companies, SaaS vendors, MSPs, and a lot of businesses outside those markets too. The point is no longer to add a few AI features, automate one workflow, or give employees access to a chatbot. The point is to rethink how the company actually operates.

AI Should Sit Under Every Corporate Function

The companies that will look strongest over the next few years are the ones treating AI as an operating system layer across the business.

That means product development, service delivery, sales and marketing, customer success, and finance and operations are all being reworked with AI in mind. Not as separate experiments, but as connected systems.

The important shift is not “where can we use AI?” It is “how should this function work if AI is built into the process from the start?”

That usually leads to a broader redesign. Workflows get compressed. Handoffs change. Data gets linked across teams. Software that used to just record tasks between humans starts becoming an orchestration layer between people and AI agents.

AI on Top Is Not Enough

Most companies are still treating AI like a feature layer. They add a copilot. They automate a few tasks. They run a few pilots in sales or support. Then they talk as if they have become an AI company. They have not.

If AI is going to matter as much as people claim, then it cannot live in isolated tools and side projects. It has to sit underneath the company as an operating layer. Product, service delivery, sales, marketing, customer success, finance, and operations all need to be rethought with AI built in from the start.

That is the real shift. Not AI as garnish. AI as infrastructure.

In practice, this means a company’s core operating logic can no longer live in forgotten decks, static docs, and tribal memory. Vision, mission, strategic priorities, ICP, and go-to-market motions need to be embedded into the AI layer itself so teams can interact with them in daily work (literally let them chat with these pieces of information via Slack!). The system should be able to explain the strategy, test whether execution matches it, and keep the company aligned as it changes. If that layer does not exist, most companies are still operating on fragments.

This Is Now a Capital Question

This is also why the conversation is changing in private equity and VC diligence.

We are not just looking for AI messaging anymore. We are looking for evidence that the operating model is changing. Is the company shipping faster? Is service delivery getting more leverage? Are teams linked better? Is software being used to orchestrate work between humans and AI agents rather than just record tasks? Is management actually rebuilding the business, or are they still presenting AI as an add-on?

Those questions now matter directly to competitiveness.

A company that keeps the old operating model and bolts AI on top will lose to one that rebuilds around it properly. The latter will move faster, learn faster, and eventually operate at a different level of efficiency.

The Companies That Wait Will Pay For It

I think this is becoming a funding imperative.

Before raising capital, before pursuing a sale, and before the board forces the discussion, management teams need to be doing the hard work of redesigning the company around AI.

Because the market is not going to wait for slow adopters to get comfortable. The companies that embrace AI as a true operating layer will look more scalable, more durable, and more investable. The ones that do not will increasingly look like they are running yesterday’s model in a market that has already moved on.

“AI lowers execution cost, not accountability. SMBs outsource accountability.”

It will affect SMB customers, the MSPs serving them, and the software vendors selling into the channel. Those are three different layers, and the impact on each one is different.

SMBs Still Do Not Want To Own the Problem

One concern is that AI makes knowledge cheaper and tooling easier, so SMBs may decide to manage more of their own IT.

I do not think that is the main outcome.

Most SMBs do not hire an MSP because they cannot access software or information. They hire an MSP because they do not want to own the problem. They want someone else to choose the tools, deploy them, keep them running, support users, stay current, and deal with issues when something breaks.

AI may make parts of IT easier. It does not remove the need for responsibility. That is why I think AI is unlikely to reduce the need for MSPs in any dramatic way.

MSPs Get More Leverage

Inside the MSP, the impact is more direct.

A lot of the work is repetitive: triage, ticket handling, investigation, internal knowledge lookup, and workflow coordination across too many systems. AI can improve all of that.

That does not mean the MSP becomes irrelevant. It means the MSP becomes more efficient.

A smaller team may be able to support more customers, resolve more issues, and operate with fewer manual steps. In security especially, where talent is scarce, that matters a lot. The near-term effect of AI on MSPs is not disintermediation. It is better leverage, better scale, and a different labor model.

MSP Software Vendors Face the Real Reset

The most interesting pressure may land on the MSP software vendors.

The issue is not whether they can add AI features. They all will. The real issue is whether they can get on the AI coding wagon fast enough and turn that into much faster product development.

AI-native development practices will increase the amount of software the market can produce. More gets built. Iteration cycles get shorter. Focused startups can come in faster. And incumbents lose some of the advantage that came from simply being large, embedded platforms.

That is where the moving-fast disadvantage shows up.

Big vendors carry legacy code, older architectures, complex customer requirements, and years of accumulated product sprawl. It is much harder for them to become truly AI-first development organizations than it is for a newer company starting fresh. There is a big difference between developers occasionally using AI and a company rebuilding product, engineering, testing, and delivery around AI-assisted speed.

If incumbents make that shift well, they will benefit enormously. If they do not, AI will not just improve their products. It will make newer competitors much more dangerous.

What Changes From Here

My current view is simple.

AI is probably a tailwind for MSP demand. It is clearly a lever for MSP efficiency. But it may be most disruptive to the software vendors serving the market, because it changes the speed of product development and lowers the barrier for new entrants.

MSPs are not going away. But the basis of competition is changing.

Update (2026-02): I released the SIEM Maturity Framework Workbook (v1.0) that turns this post into a practical scoring tool.

I have been talking to a few AI SOC and new SIEM market entrants over the past few weeks. I have voiced some opinions in previous posts but have now started to capture a list of features that I believe represent the openings existing SIEM players have created in the market for these new vendors to emerge.

Before I outline what I think those features are, let me be clear: this is my list. I am aware that existing SIEM vendors will claim that they already do many of these things. All I will say is this: market churn and capital flow suggest that these capabilities are either not as mature or not as integrated as claimed.

And to the AI SOC companies and investors: be careful about the short-term problems your investments are solving. Yes, there is real traction with MSSPs that are overloaded with false positives. And yes, many will gladly pay to reduce alert workload by 80%. But in many cases, these problems are being addressed superficially. Make sure you audit the underlying approaches and verify that the foundational infrastructure is sound. Solving this problem on top of an existing detection infrastructure doesn’t solve the problem at the core, which is the detections themselves. We need to fix those with some of the suggestions below to not needing a top-layer, alert reducer.

Without further ado, here are the items I am tracking. I welcome other opinions and additions to the list (no guarantee I will include them). Over the coming weeks, I will also try to rate some of the players across these categories to enable comparison. I could use help with that. Ping me.

A. DATA & CONTROL PLANE ARCHITECTURE

• Federation – The ability to query and reason over data where it lives, without forced centralization.
  (Another post following here at some point about the limitations of federation).
• Data Pipeline Optimization – Dynamic ingestion pipelines that enrich, route, sample, and filter data based on use case, risk, and downstream value. Not static “send everything to the lake.”
• Data Awareness – Understanding what data exists, what is missing, and what has silently degraded. The system must continuously reason about its own observability.
• Performance as a First-Class Constraint – Fast joins and low-latency queries across all relevant data. Real-time rule execution at scale. This is not about basic scalability, but about maintaining predictable performance as rule count and complexity increase, without simply throwing more compute at the problem.
• Modern AI Integration – The ability to integrate with emerging architectural patterns and frameworks, including MCP servers, vector stores, and related systems.

B. DETECTION & LEARNING SYSTEMS

• Hypothesis-Driven Hunting – Hunting should start with explicit hypotheses, not ad-hoc queries. These hypotheses should evolve, fork, and self-update based on outcomes. Agents swarms anyone?
• Automated Detection Tuning (Closed Loop) – Detections must evaluate their precision and recall over time. False positives and false negatives are signals. Humans stay in the loop, but are not the tuning engine. This also helps separate the detection engineering from the tuning that should be done by analysts.
• Environment-Adaptive Detections – Rules and models must adapt automatically to the specific environment, business processes, and user behavior and analyst feedback. Generic detections are table stakes.
• Detection Lineage and Memory – The system must remember why a detection exists, how it has changed, and what outcomes it has historically produced.

C. ENTITY-CENTRIC RISK & CONTEXT

• Asset Awareness – Effective protection and detection start with understanding what is being protected. Entity visibility is foundational: who owns this entity, what does it do, and which business processes does it support?
• Real-Time Entity Risk Scoring – Each entity has a continuously updated risk score driven by behavior, exposure, and contextual signals.
• Entity Risk Context – Risk is not a number. It is a set of properties that help explain the risk and provide context for decision making.
• Business Context Integration – Entities must be tied to business processes, ownership, and criticality, and this context must inform alert generation and prioritization. Some people have started calling this the Context Graph.

D. OPERATIONAL REALITY (SOC, MSSP, ENFORCEMENT)

• Simple Query Interface: Support for both natural language and structured query languages (such as KQL). Analysts need both.
• Alert Triage Automation – Using ‘advanced’ context to tune detections. Ideally we have business context available to continuously improve our detections.
• Blindspot Detection – The system must actively identify where detections cannot exist due to missing or degraded logs or logging configurations. This includes making sure that log sources are actually staying up and keep reporting what they have to.
• Real-Time Readiness for Enforcement – We need our systems to become preventative. Therefore, its risk model must operate in near real time. Attackers are acting too fast.

A Few Additional Comments for Context

This is not meant to be a SIEM RFP. I am intentionally not listing table-stakes capabilities such as basic scalability, data source support, or baseline detection depth.

This list is less about features than about where intelligence and control actually live in the system. I am also not being prescriptive on how these features are built. Many of them can benefit from AI / LLM / ML approaches and, in fact, should be using them.

Look at the list, then look at your AI SOC platform of choice. How much of the above does it truly cover?

If you are evaluating an AI SOC platform and most of its value proposition lives above alerts rather than below them, you should be skeptical.

Update (2026-02): I released the SIEM Maturity Framework Workbook (v1.0) that turns this post into a practical scoring tool.