AI SOC vs. SOAR: What the Architectural Difference Means for a Team Evaluating Both

Q1. AI SOC vs SOAR vs MDR: What’s the Actual Difference, and Where Do SIEM, XDR, and EDR Fit?

A CISO messaged me last month with a screenshot of three vendor decks open side by side. All three said “autonomous.” All three said “AI-driven.” She asked one honest question: “Are these the same thing wearing different logos?” That confusion is the real starting point, so let me clear it before we compare anything.

SOAR, AI SOC, and MDR are three different layers, not three versions of the same product. SOAR is a tool that runs predefined response playbooks. An AI SOC is a tool that investigates alerts by reasoning through them instead of following a decision tree. MDR is a service that runs detection and response for you. SIEM collects the logs, EDR and XDR generate the detections, and these three decide what happens next. Most mature teams layer them.

🧩 Why These Three Feel Identical

The categories blur because vendors keep renaming their products. An MDR provider bolts on a model and calls itself an AI SOC. A SOAR vendor adds an agent and calls itself autonomous. The label moves faster than the architecture underneath it.

So buyers end up comparing marketing words, not machines. That is exactly why I lead every scoping call by asking what a tool actually does to your alert queue, never what the box on the website says.

🔍 A One-Line Example of Each Layer

Here is each layer doing its real job, in plain terms.

• SIEM: collects and stores logs from your endpoints, cloud, and identity systems.
• EDR / XDR: watches those systems and fires a detection when something looks wrong.
• SOAR: runs a scripted response, like “isolate this host, disable this account.”
• AI SOC: investigates the alert, gathers evidence, and decides if it is real.
• MDR: a human-run service that does detection and response on your behalf.

One caution I keep repeating to peers: a SOAR playbook is a set of “if and else” decision trees. Real alert investigation needs improvisation and dynamic planning, which a fixed tree cannot supply.

⚙️ Layers, Not a Menu to Choose From

The mistake I see most is treating this as a single pick. Teams ask “SOAR or MDR or AI SOC?” when the honest answer is usually “which combination?”

Think of AI agents as your foot soldiers and your human engineers and analysts as the generals directing them. The agents do the heavy, repetitive gathering. The humans set direction and own the hard calls. This framing governs the rest of this article.

At UnderDefense, we built the UnderDefense Agentic AI SOC to sit as an investigation layer on top of your existing SIEM and XDR, so you keep the stack you already own and add reasoning where the queue actually hurts. You can see how that layering works on the platform page.

Q2. Why Does the Architectural Difference Decide Whether You Stop a Breach or Just Document It?

I once watched a team with a beautiful SOAR setup lose an hour to a novel alert their playbooks never anticipated. The automation ran flawlessly. It just ran the wrong path, quickly, while the intrusion kept moving. That day taught me the point of this whole section.

Architecture decides the outcome, because attacks now move faster than any playbook can be written. Median break-in time has fallen to roughly 48 minutes, and the fastest break-in we have seen is around 51 seconds. A SOAR playbook only fires for scenarios someone already anticipated. Dynamic-reasoning investigation adapts to the alert in front of it. When the attacker improvises and your automation cannot, you document the breach rather than stop it.

⏰ The Speed Mismatch Is the Whole Problem

A 51-second break-in against a 30 to 60 minute managed response is not a fair race. The attacker finishes before your first human even opens the ticket.

That gap is structural, not a staffing issue. You cannot hire your way out of a timing problem this severe. The response model itself has to change.

🌳 Why Decision Trees Break on Novel Threats

A playbook is a map of roads someone already drove. When the attacker takes a road nobody mapped, the playbook has no branch to follow, so it defaults to “escalate to a human” and the clock keeps running.

Novel intrusions demand improvisation. Reasoning-based investigation can pull an unexpected log, question it, and follow the thread. A fixed tree simply stops where its author stopped thinking.

⚠️ A Faster Way to Be Wrong

Here is the part the category avoids saying out loud. If your detection logic is weak and you bolt expensive automation on top, you have bought a faster way to be wrong. You scale the noise, not the outcome.

My current read, and I have watched this fail up close, is blunt. If the same humans review the same number of alerts a little faster, that is not transformation. Real transformation means eliminating whole classes of work so nobody triages that alert type again.

🧟 You Do Not “Win,” You Keep the Doors Boarded

I tell CISOs that cybersecurity is a bit like a zombie apocalypse. You do not win. You keep the doors boarded up until the sun comes out.

That mindset changes what you buy. You stop shopping for a magic stopper and start asking which architecture keeps the doors holding when a threat you never scripted for shows up. This is where the UnderDefense Agentic AI SOC earns its place: its agentic model reasons through the alert to reach a verdict, so it resolves cases that pure playbook tools escalate right back to your team. The platform is built around that reasoning-first design.

Q3. What Can a SOAR Playbook Actually Automate, and Where Does It Hit a Wall?

I owe SOAR real credit before I critique it. Early in my career I helped write well over 10,000 lines of PowerShell to drive automation forward, and when a playbook fits the job, it is genuinely beautiful to watch. So this is a fair accounting, not a takedown.

SOAR automates the response you already planned for. It fires deterministic playbooks, like isolate a host, disable an account, or open a ticket, for known scenarios, and it does this reliably. The wall is coverage. Playbooks typically automate only the 30 to 40% of alerts that match an existing branch, and every API change or new detection can break them. SOAR executes a process. It does not investigate whether the alert is real.

✅ What SOAR Genuinely Does Well

For repeatable, well-understood incidents, SOAR is hard to beat. It brings speed and consistency to work that would otherwise burn analyst hours.

• Deterministic containment: the same clean action every time, no fatigue, no drift.
• High-volume known tasks: ticket creation, enrichment lookups, account disables.
• Audit consistency: a repeatable, logged sequence auditors like to see.

🕳️ The 30 to 40% Ceiling and the Maintenance Tar Pit

The ceiling is the honest problem. A playbook only covers what its author foresaw, so the long tail of novel alerts falls straight through.

Maintenance is the second cost, and it is a tar pit of manual toil. I have seen an unexpected character passed into a script cause devastating consequences and a full-on outage. Every upstream API change is a new chance for a brittle playbook to snap.

⚖️ So When Is SOAR Still the Right Tool?

Use SOAR where the world is predictable. It shines when the response is deterministic, the volume is high, and the scenario is already well understood.

Best for ✅	Struggles with ❌
Known, repeatable incidents	Novel, never-seen alerts
Deterministic containment steps	Alerts needing improvisation
High-volume routine tasks	Deciding if an alert is real
Stable, unchanging integrations	Frequently changing APIs

At UnderDefense, we keep SOAR-native response orchestration for exactly this deterministic work, and we pair it with agentic investigation for everything the playbook never anticipated. That means no rip-and-replace of your existing Phantom, Tines, or Torq workflows, since the UnderDefense Agentic AI SOC adds reasoning on top rather than swapping out what already runs. The mechanics are on the platform page.

SOC

WHERE THIS IS HANDLED

We run a SOC that reasons through alerts instead of stopping at the 30-40% a playbook expected.

If your playbooks keep breaking on the alerts nobody scripted for, this is work we do every day, and the door’s open if you want a look.

Talk to our SOC team →

Q4. How Does an AI SOC Investigate an Alert Differently From a SOAR Playbook?

The question I get most from technical leads is skeptical, and rightly so. “Is this just a GPT wrapper that restates my alert in fancier words?” It is a fair fear, so let me open the box and show what real investigation looks like under the hood.

An AI SOC investigates the way an analyst does. It reasons recursively instead of following branches. Rather than matching an alert to a prewritten path, it pulls the right logs, gathers evidence across identity, endpoint, cloud, and SaaS, validates each finding, maps it to MITRE ATT&CK, and produces a verdict. Patents now describe exactly this: LLM-planned investigation that learns from past cases and infers root cause.

🔁 Recursive Reasoning, Not a Branch

Reasoning means the system asks a question, reads the answer, and decides the next question based on what it just learned. That loop repeats until it reaches a defensible conclusion.

To give a sense of depth, a serious agentic system can make over 100 distinct large language model calls to investigate a single alert. That is the difference between real recursive reasoning and a thin wrapper that just paraphrases the alert.

📜 What the Patents Actually Describe

This is not vaporware, and the patent record backs it up. Filed methods describe an LLM generating an investigation plan, executing it, learning from past investigations, and inferring root cause in reverse.

The same shift is visible inside managed offerings, where machine-learned alert triage has been patented for MDR-enabled SOCs. The reasoning layer is becoming the core of the work, not a demo feature.

🕵️ How to Spot an “LLM Wrapper”

I might be wrong on a few edge cases, but from what surfaces when you actually run these tools, wrappers give themselves away fast. Ask three questions and watch the answers.

• Who are your data scientists? Wrappers dodge this.
• How do you run evaluations? Wrappers rarely have a real answer.
• How do you manage context? Wrappers change the subject.

A wrapper restates the alert. A real system investigates it and hands you a verdict.

🧱 Own the Logic, Skip the Black Box

My strong preference, earned the hard way, is to own the reasoning. I want all the Lego bricks that make up the hard pieces of an AI SOC, then build the platform my own way, so no segment is a black box I cannot audit. This is also why we help teams inspect every investigative step rather than trust an opaque model.

That philosophy is exactly how we built the UnderDefense Agentic AI SOC: six specialized AI Teammates, 200+ agent skills, 400+ AI tools, and 700+ MITRE ATT&CK-mapped investigation workbooks that deliver investigation-grade verdicts you can inspect step by step. You can trace the architecture on the platform page.

Q5. Is MDR Just an AI SOC With People Attached, and Where Do the Lines Blur?

Here is a take the category avoids saying out loud. The clean three-way split between MDR, AI SOC, and SOAR is partly a marketing artifact. The standard read gets this backwards, because the real question was never the label on the box.

The lines genuinely blur. MDR is a service, but most MDR providers now run machine-learning triage under the hood, and some patent it, so AI SOC capability increasingly lives inside MDR. The distinction that still matters is ownership. Does the AI reason transparently on your stack, do you keep your logic and data, and does a human actually respond, or do you just get a ticket back?

🔬 The Patents Give the Game Away

Watch what the biggest vendors file, not what they say. Machine-learned alert triage built specifically for MDR-enabled SOCs is already patented. Probabilistic triage using alert-similarity features shows up in the filings too.

So when a provider says “MDR” and a rival says “AI SOC,” they may be describing the same engine. My current read is that the category names are drifting faster than the technology underneath them.

⚠️ The “AI Washing” Problem

Plenty of vendors renamed a product and called the rebrand innovation. At UnderDefense, our stance is blunter: we rebuilt the SOC and the outcomes, rather than swap the label.

The bigger gap is coverage. Many legacy tools watch the endpoint, but do not monitor what Claude, Copilot, Cursor, or a custom AI agent is doing in production. Humans click, but agents swarm, and a developer can run ten autonomous AI agents on a laptop before lunch. This is exactly the visibility gap our MDR for AI is built to close.

✅ The Axes That Actually Matter

Stop grading vendors on the acronym. Grade them on three honest axes instead.

• Ownership: do you keep your detection logic and your data?
• Transparency: can you audit every step the AI took?
• Real response: does a human contain the threat, or just forward a ticket?

We built the UnderDefense Agentic AI SOC to answer all three: an agentic AI SOC paired with a human Tier 3 and Tier 4 response team, watching the full attack surface including AI agents in production, with every investigative step visible. Less black box, more blue team. You can see how it works on the platform page, and dig deeper into our approach to explainable investigation.

“The platform itself is straightforward, it pulls in data from all our existing security tools, so we didn’t have to rip and replace anything. When they escalate something, they include the context we need to understand the issue quickly.”
Verified User in Marketing and Advertising, Small-Business UnderDefense G2 Verified Review

“UnderDefense Agentic AI SOC integrates well with our systems, specifically with our SIEM, Splunk. Their team is proactive in identifying and addressing threats, providing 24/7 oversight.”
Oleg K., Director of Information Security UnderDefense G2 Verified Review

Q6. Are You Fixing Your Alert Queue, or Just Scaling the Noise Faster?

I have watched good analysts break under alert volume. One told me she had physical reactions, literally breaking out in hives, because she could not keep up with the issues stacking up. That is the human cost hiding behind every “faster triage” pitch.

If your detection logic is weak, faster automation just produces wrong answers faster. Studies find 50 to 90% of SOC alerts are false positives, which is why analysts burn out and real threats slip through. The goal is not to triage the same flood more quickly, but to eliminate whole classes of alerts. Fix detection first, then let reasoning-based investigation clear what remains.

😩 What Alert Fatigue Actually Costs

Alert fatigue is now treated as a first-class research problem, not a soft complaint. When a team drowns, something quieter breaks too.

Analysts start saying “nobody’s gonna listen to us anyway,” and that grumble becomes the operating reality. The team disconnects, and disconnected teams miss the alert that mattered.

🧘 The “Zen in Copying” Trap

I once interviewed a candidate for a role that meant copying data all day for five years. She told me, without irony, “I find the zen in copying.” That line stuck with me.

Our industry is full of teams finding zen in toil that automation should have removed years ago. Speeding up that toil is not progress. Removing it is.

✅ The Fix Is Detection Quality, Not Raw Speed

Here is the research-backed part. Human-in-the-loop tuning, where analysts correct the system and it learns, has been estimated to cut one high-volume alert class by around 90%. That is elimination, not acceleration.

The Monday-morning move is simple. Pick your single highest-volume alert type, tune it hard, and measure the drop over two weeks.

At UnderDefense, we treat detections like software, which we call Detection Logic as Code: written in Python, versioned, unit-tested, and shipped through CI/CD. In our 30-day onboarding, that approach is how we drive noise reduction toward 99% before we ever talk about speed. The mechanics live on the UnderDefense Agentic AI SOC platform, and you can compare paths in our build versus buy guide.

“The biggest win for me was getting actual control over our security alerts. Their team cleaned up our configurations and got the noise under control within the first week. Now when we get an alert, we know it’s something worth looking into.”
Verified User in Marketing and Advertising, Small-Business UnderDefense G2 Verified Review

“Before UnderDefense Agentic AI SOC, we were slightly overwhelmed with alerts and often unsure how to prioritize or respond to them. False positives have become a rarity, ensuring that our team’s focus remains on genuine threats.”
Valeriia D., Marketing Specialist UnderDefense G2 Verified Review

Q7. Can You Trust an Autonomous Investigation, and How Do You Keep It From Going Rogue?

Every CISO I talk to about autonomy asks the same thing, just phrased differently. “What stops this thing from doing something stupid at 2 a.m.?” It is the right question, and the honest answer is architecture.

You trust autonomous investigation the way you trust a junior analyst: with an audit trail and hard limits, not blind faith. Prompt-based safety fails, and the “grandma” jailbreak proves it. Real control is architectural, meaning callback functions and permissions that make dangerous actions technically impossible, plus a fully observable evidence trail. A vendor claiming their model is “unbiased” should raise your guard, because measurable bias is fixable while an invisible one is not.

💥 Why Blind Trust Blows Up

I know a founder who let an agent “vibe code” a new application. The agent went and deleted his production database, with zero audit trail to reconstruct what happened.

That is the failure mode in miniature. Autonomy without a record is not efficiency, but a liability waiting for its moment.

⚠️ Prompt-Based Safety Is Theater

The “grandma” jailbreak is my favorite proof. You tell the model your grandma used to read you a bedtime story about cooking something dangerous, and the model happily plays along and coughs up the recipe.

If a simple story defeats your safety layer, the safety layer was never real. Guardrails written as polite instructions get talked around, which is why we favor architectural AI SOC guardrails.

🔒 What Real Guardrails Look Like

Here is the Monday-morning tactic I stand behind. Put controls in the architecture, not the system prompt.

• Callback functions: the agent physically cannot reach a forbidden resource. It is impossible at the architecture level, not discouraged by a prompt.
• PRD-first rule: before an agent writes anything, make it write a product requirements document listing exact libraries and steps, then you edit that plan before it runs.
• Full audit trail: every action logged and replayable.

On the bias point, my view is contrarian and earned. I would rather run a model whose flaws I can measure and correct, because a vendor selling you an “unbiased” model is either wrong or hiding something.

We built the UnderDefense Agentic AI SOC on exactly this principle: every investigative step is observable and auditable, the AI gathers context while your team decides the verdict, and on-premises or air-gapped deployment keeps your data and logic inside your perimeter. That governance model, reinforced by our human-in-the-loop design and careful data residency controls, is what makes human-AI teaming hold up under real risk review.

Q8. Which One Should Your Team Run, and in What Combination?

I get asked “SOAR, AI SOC, or MDR?” almost weekly, and my answer frustrates people at first. Wrong question. The right one is “which combination fits my biggest constraint?”

Choose by your biggest constraint. If deterministic response for known incidents is the gap, SOAR earns its place. If your team drowns triaging alerts, an AI SOC investigation layer clears the queue. If you lack 24/7 staff, a managed service provides the humans. If data cannot leave your walls, you need on-premises agentic capability. Most teams layer investigation, execution, and human expertise rather than picking one.

📊 The Layers Side by Side

Here is the honest breakdown, framed neutrally so you can map it to your own stack.

Dimension	SOAR	AI SOC	Managed service (with agentic AI SOC)
What it is	Tool	Tool	Service
It automates	Response execution	Investigation	Detection plus response
Alert coverage	~30 to 40% (playbook)	Investigates ~all alerts	Provider-run
Maintenance	High (playbook upkeep)	Low	Handled for you
Best for	Known, repeatable response	Investigation bottlenecks	Little or no in-house staff

🧭 Match the Layer to Your Real Scenario

Pick the row that sounds like your Monday. These are the patterns I see most.

1. A 2-person security team with ~10 IT helpers, unhappy with a current provider that only sends tickets. You need an agentic AI SOC plus real human response, and this is where I would put the UnderDefense Agentic AI SOC first. It is right-sized from mid-market to Fortune 500, with no 5,000-employee floor that some enterprise-only providers impose.
2. A regulated or air-gapped shop where data cannot leave. You need on-premises agentic capability, which most cloud-only services cannot offer.
3. A mature SOC drowning in volume. You need an investigation layer on top of the SIEM you already own, so you keep your logic and avoid lock-in.

✅ What to Test in a Proof of Concept

Do not buy on a slide deck. On the buy-versus-build question, building your own agent is fine for simple cases, but the complexity climbs exponentially for sophisticated environments.

• Speed: measure alert-to-triage time. Ours runs at 2 minutes, with 15-minute escalation for critical incidents.
• Transparency: can you audit the full evidence trail?
• Novel-alert handling: feed it something the playbook never saw.
• Integration upkeep: how much breaks when an API changes?

We built the UnderDefense Agentic AI SOC to be vendor-agnostic across Splunk, Sentinel, Chronicle, QRadar, and Elastic, so it layers onto your stack rather than replacing it. The integrations page shows the full list, and our evaluation questions guide helps you run a fair proof of concept.

“I used to work with many MDR solutions in the past, and so far UnderDefense is the best one. It automates many tasks, plus, with 24/7 monitoring, we know we’re always protected.”
Inga M., CEO, Mid-Market UnderDefense G2 Verified Review

“We were looking for an MDR provider and choosing EDR tools. CrowdStrike was our favorite, but after a few calls with UnderDefense we realized we could get way more value. They delivered deployment to 1200 endpoints in just 2-3 business days.”
Oleksii M., Mid-Market UnderDefense G2 Verified Review

“Their 24/7 detection and response service is fast and comprehensive, providing us with a granular, real-time view of our environment. When alerts pop up, there’s no panic, we know precisely what steps to take next.”
Valeriia D., Marketing Specialist UnderDefense G2 Verified Review

Q9. How Do the Investigation-to-Execution and Human Handoffs Actually Work Day to Day?

The fear I hear most about layering three things is chaos. “If SOAR, an AI SOC, and my humans all touch the same alert, who is actually in charge?” Fair worry, so let me walk the real flow, step by step.

The handoff runs in a chain. The AI SOC investigates the alert and produces a verdict with evidence, then hands confirmed incidents to SOAR for deterministic containment, meaning fixed, scripted actions, and escalates the genuinely ambiguous cases to a human. Context that logs cannot supply gets gathered by pinging the actual user over Slack, Teams, email, or SMS. Agents act as foot soldiers, and your engineers and analysts direct them as generals.

🔗 The Chain in Plain Terms

Picture one suspicious login at 2 a.m. The AI investigates first, pulls the evidence, and reaches a verdict.

If the verdict is clean and known, SOAR runs the scripted containment. If it is murky, a human takes it. Nobody fights over the alert, because each layer owns a clear job, which is the heart of a sound SOC decision architecture.

💬 When Logs Run Out, Ask the Human

Logs rarely tell the whole story. A login from a new country could be a breach, or your VP on vacation.

So we ping the actual user directly. At UnderDefense, the UnderDefense Agentic AI SOC validates anomalous behavior with users at scale over Slack, Teams, email, or SMS, and loops in managers to confirm security-impacting changes. That one step kills a huge share of false alarms before a human ever spends a minute.

⚖️ Who Owns the Verdict

Here is the line I hold firm on. The AI collects context, and the human decides.

Automation handles the routine gathering. Humans own the judgment calls and the containment decision on anything that carries real risk. That split is what keeps the model trustworthy, and it is why we favor human-in-the-loop design.

✅ What This Frees Your Team to Do

The point of automation is not to delete your Tier 1 and Tier 2 analysts. It gives them room to focus on more important things, like detection engineering and threat research.

We route every structured incident into your existing Teams, Jira, or Slack workflow, so nothing lives in a separate portal nobody checks. You can see the operating model on the platform page, or explore how it connects through our integrations.

“When they escalate something, they include the context we need to understand the issue quickly. We’re not wasting time piecing together what happened from different systems anymore.”
Verified User in Marketing and Advertising, Small-Business UnderDefense G2 Verified Review

“We’ve tackled potential threats directly from our Slack channels, regardless of the hour. It’s like having a security command center right in our daily chat tool.”
Alexander B., Chief Executive Officer UnderDefense G2 Verified Review

Q10. How Does Faster Investigation Change Your Compliance and Breach-Disclosure Clock?

I spent years doing PCI work, often walking into rooms where nobody wanted the auditor there. What I learned holds today: a regulator does not care how fast your tool is. It cares whether you made the call in time.

Faster investigation directly changes your disclosure math. SEC rules require reporting a material cyber incident on an 8-K within four business days of the materiality decision, and GDPR Article 33 gives you 72 hours to notify regulators of a breach. You cannot make either call without a verdict. When investigation collapses from hours to minutes, you reach the materiality decision sooner and buy back time on a fixed clock.

⏰ The Clock Starts at the Verdict

Both clocks depend on a conclusion. The SEC’s four-day window keys off your materiality decision, and materiality needs facts.

GDPR’s 72-hour window keys off awareness of a breach. If your team is still guessing at hour 40, you are burning the clock on uncertainty, not on the notification itself, which is exactly why investigation speed matters here.

⚠️ Documenting a Breach Is Not the Same as Concluding One

Here is the trap. A tool that logs everything in real time still leaves you exposed if it never reaches a verdict.

Raw alerts are not a decision. You can have a perfect record of the intrusion and still miss the disclosure deadline, because nobody could say “yes, this is material” in time, so a tested incident response plan is essential.

✅ Verdicts That Double as Evidence

The fix is investigation-grade output. A clear verdict with mapped evidence is something you can hand to a board, an auditor, or a regulator.

At UnderDefense, the UnderDefense Agentic AI SOC runs at 2 minutes alert-to-triage with 15-minute escalation for critical incidents, and it produces MITRE ATT&CK-mapped narratives that double as regulatory documentation. Pair that with our compliance services for ISO 27001, SOC 2, HIPAA, and PCI DSS, and the verdict becomes audit-ready by default. Details are on the UnderDefense Agentic AI SOC platform.

Q11. How Do You Prove the Value to a Board That Neither Cares for nor Understands Your Metrics?

I have sat in board meetings arguing for budget with metrics the room neither cared for nor understood. MTTR, coverage percentages, MITRE heat maps, all of it bounced off. The standard read gets this backwards, so here is what actually lands.

Stop trying to prove breach-prevention ROI, because you cannot prove a negative. Compare the cost of delivery options instead, and ask the CFO one question: what is your projected cost of business interruption per day? Then map every security dollar into NIST risk families, meaning Identify, Protect, Detect, Respond, and Recover, on a single page, so leadership sees exactly where you spend and where you have nothing.

💸 Why Breach-Prevention ROI Is a Trap

You cannot point to a breach that never happened and bill it as savings. It is a very difficult proposition to prove a negative.

So drop the exercise entirely. The comparative cost of delivery options, meaning build versus buy versus outsource, is a number the board can actually reason about, and our ROI business case guide lays out that math.

💰 The Two Moves That Land in the Room

Here are the tactics I would use this week. Both translate security into money and risk, the only two dials a board turns.

• Ask the business-interruption question. “What is our projected cost of business interruption per day?” Now the budget debate is about their number, not yours.
• Map spend to NIST families on one page. Show the CFO where you spend and where you have nothing. Gaps become obvious without a single technical term, and our 2026 cybersecurity budget playbook gives you the template.

⭐ A Free Win to Show Resourcefulness

Want a quick proof of judgment before you ask for a dollar? Hunt shadow IT for free.

Use your Microsoft 365 or Google Workspace OAuth logs to find every site where staff clicked “log in with Google.” That is a rich source of unknown vendors, at zero cost, versus buying a CASB, which is a cloud access security broker tool. Being a human who finds the cheap win first is a real flex in 2026, and it complements a broader vendor risk management effort.

✅ What This Looks Like With the UnderDefense Agentic AI SOC

This is exactly why we price the way we do. UnderDefense runs transparent, all-inclusive pricing with a live per-investigation cost view, so the board sees the delivery cost in plain numbers, without opaque enterprise quotes. You can check the figures on our MDR pricing page.

“UnderDefense is surprisingly affordable considering the level of protection we get. Their proactive threat hunting and rapid response have saved us from incidents that could have been incredibly costly.”
Verified User in Program Development, Mid-Market UnderDefense G2 Verified Review

“We love the monthly report. We gain valuable insights into security posture and incidents, and share them with the board of directors.”
Yaroslava K., IT Project Manager UnderDefense G2 Verified Review

So here is the question I am sitting with heading into the next 18 to 24 months. As agents start swarming faster than any human can click, the winning security teams will be the ones who own their logic and can explain a verdict in one sentence to a board. If that is the problem on your desk, tell us what you are defending, and we will walk your stack with you.