The Risk Wheelhouse

S4E2: Autonomous IRM - Orchestrating Risk at Machine Speed

• Wheelhouse Advisors LLC • Season 4 • Episode 2

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Machine-speed threats demand machine-speed responses. The digital acceleration of our world has created a fundamental challenge: how do we manage risks when they move faster than any human can possibly react?

Traditional risk management approaches—with analysts reviewing alerts, manually connecting dots, and initiating responses—simply cannot keep pace with today's threat environment. The necessary evolution is towards autonomous integrated risk management (IRM), where agentic AI systems don't just detect threats but actively respond within seconds based on predefined policies. Companies like CrowdStrike are pioneering this shift with platforms such as Charlotte AI, which provides autonomous detection, triage, and response capabilities.

Yet the technological readiness far outpaces organizational readiness. While the tools exist to operate at machine speed, most enterprises find themselves stalled between coordinated and truly embedded risk management approaches. The challenge isn't simply implementing new technology—it's architecturing a comprehensive framework where autonomous actions in security seamlessly trigger appropriate responses across business functions, compliance requirements, and third-party relationships. This demands a five-layer approach: strategic oversight aligning with business priorities, business orchestration coordinating responses, threat intelligence providing real-time validation, remediation executing actions, and verification capturing evidence for audit and compliance.

The organizations that successfully bridge this gap won't merely be better at handling security incidents—they'll gain a decisive advantage in building true enterprise resilience. The future belongs to those who can ingest machine-speed signals, translate them into business context, trigger appropriate cross-domain workflows, capture evidence, and continuously learn from outcomes. Are you ready to make the leap from documenting risks to orchestrating responses at the speed required in today's digital world?



Visit www.therisktechjournal.com and www.rtj-bridge.com to learn more about the topics discussed in today's episode.

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Our YouTube channel also delivers fast, executive-ready insights on Integrated Risk Management. Explore short explainers, IRM Navigator research highlights, RiskTech Journal analysis, and conversations from The Risk Wheelhouse Podcast. We cover the issues that matter most to modern risk leaders. Every video is designed to sharpen decision making and strengthen resilience in a digital-first world. Subscribe at youtube.com/@WheelhouseAdv.


Sam Jones:

In our digital world, everything just seems to be moving faster and faster, doesn't it? It's almost dizzying.

Ori Wellington:

It really is. The pace is incredible.

Sam Jones:

And you know, with that speed comes this really critical, maybe even unsettling question how do we actually manage risk when things are happening faster than any person can possibly react?

Ori Wellington:

Yeah, it's like trying to catch smoke sometimes.

Sam Jones:

Exactly. It feels like trying to I don't know manage a flood with a teacup. The sheer speed just overwhelms the old ways of doing things.

Ori Wellington:

Uh-huh, manual processes just can't keep pace.

Sam Jones:

So today we're going to dive deep into this really profound shift happening in risk management. We're talking about moving away from, let's say, human speed reactions.

Ori Wellington:

Which are often too slow.

Sam Jones:

Right, too slow and moving towards machine speed foresight and, crucially, response. This whole area is being called autonomous integrated risk management, or autonomous IRM for short.

Ori Wellington:

And for this deep dive we're drawing heavily on some fantastic insights from a key source. It's called autonomous IRM, orchestrating risk at machine speed, put together by wheelhouse Advisors, and it really digs into how companies like CrowdStrike, for instance, are well pioneering this new era using something called agentic AI.

Sam Jones:

Agentic AI. Ok, we'll definitely need to unpack that. So our mission for you listening in is pretty clear. Today, we're going to unpack what this autonomous IRM thing really means.

Ori Wellington:

We'll look at the new capabilities it unlocks.

Sam Jones:

Yeah, and we'll lay out this architectural blueprint that organizations apparently need if they want to adopt it.

Ori Wellington:

And, crucially, touch on the big challenge for companies trying to make this leap.

Sam Jones:

Right, so get ready You're gonna get a real shortcut to understanding a super cutting edge topic, one that honestly demands attention right now.

Ori Wellington:

Absolutely, it's moving fast.

Sam Jones:

OK, so let's start unpacking this paradigm shift. The core problem, as that wheelhouse source points out, seems simple but profound. The speed of risk has just flown past human decision making.

Ori Wellington:

Completely. We're talking about these agentic systems. They can spot an incident and react in literally seconds seconds, wow, okay.

Sam Jones:

So for listeners maybe hearing this term for the first time these agentic systems, what exactly are they? How are they different from, say, the ai alerts we've maybe gotten used to?

Ori Wellington:

right, good question. So agentic ai systems? They don't just send you an alert like hey, look at this, they autonomously assess the situation, they can actually act on it and even learn from it. So they do things, yes, within predefined rules and parameters, of course, but they make decisions and take actions. That autonomy, operating at machine speed, is really the catalyst driving this whole shift.

Sam Jones:

And CrowdStrike. You mentioned them. They're right out front with something called Charlotte AI.

Ori Wellington:

That's right. Charlotte AI is what they call their agentic AI architecture. It's now built into their Falcon platform and it offers this triad of capabilities, as they put it agentic detection, triage, agentic response and agentic workflows.

Sam Jones:

Okay, that triad sounds comprehensive. Let's take one, say agentic detection triage. How does that really change things for a security team on the ground beyond just getting alerts faster? What's the? You know the machine speed insight here.

Ori Wellington:

Well, think about it. Instead of a human analyst, maybe overwhelmed, sifting through thousands of alerts, trying to connect the dots, deciding what's important.

Sam Jones:

Yeah, that sounds exhausting.

Ori Wellington:

It is the agentic AI system does that triage instantly, it prioritizes, it adds context, it might even kick off some initial containment actions automatically. True, no human needed for that first step. Wow, so the core insight. It shifts the heavy lifting of analysis and initial action from human to machine. The human role becomes more about oversight, setting the strategy, defining the policies, not being in the weeds of every single alert, second by second.

Sam Jones:

Okay, that makes sense. So that triad detection response workflows it sounds like it covers the whole automated response cycle. Now how does integrating that into the bigger picture, the whole integrated risk management or IRM framework, how does that change things for the entire business?

Ori Wellington:

Ah see, that's where it gets really profound, because we're moving way beyond just smarter security alerts or faster post-incident digging.

Sam Jones:

Right.

Ori Wellington:

These are decisions made by machines, decisions that have immediate consequences for governance, for compliance, for day-to-day operations. Think about it A machine action could trigger a business continuity plan or impact a third-party relationship instantly.

Sam Jones:

Okay, so the ripple effect is potentially huge and immediate.

Ori Wellington:

Exactly, and that absolutely demands a completely new way for enterprises to oversee risk. You can't manage machine speed decisions with monthly committee meetings.

Sam Jones:

Yeah, that seems obvious. Now you say it.

Ori Wellington:

So CrowdStrike, they've essentially built, as the source says, the signal and execution layers. That sounds like a huge step.

Sam Jones:

It is technologically.

Ori Wellington:

But if they've built that, what's the really hard part now for the let's call it the broader IRM ecosystem? Translating those super fast, autonomous security events into joined up auditable business responses. Where are the headaches going to be? That's precisely it. It's the orchestration challenge. Crowdstrike provides the lightning fast signal and response at the security level, the sort of nervous system impulse. Okay, but the rest of the IRM world, the business processes, the compliance checks, the operational adjustments needs to be able to receive that signal, understand its business meaning and then coordinate the right responses across all the different functions legal finance, operations, vendor management, everyone.

Sam Jones:

So connecting the security action to everything else it touches.

Ori Wellington:

Yes, and doing it seamlessly, audibly and at that same machine speed. The headache is bridging that gap between the isolated security action and the fully integrated, enterprise-wide risk management response. It's about weaving those autonomous decisions into the fabric of existing business policies and controls without slowing things down.

Sam Jones:

Okay, that orchestration challenge sounds significant. Now, to help us sort of visualize how to tackle that, the 2025 IRM Navigator Viewpoint Report introduces this idea of five functional layers of autonomous IRM. You can think of this, maybe, as the architectural blueprint risk leaders need. It maps everything from the high level strategy down to the real time controls. It's designed to ensure these autonomous actions don't just happen in a vacuum.

Ori Wellington:

Exactly, it provides structure. Should we walk through them quickly?

Sam Jones:

Yeah, let's do that. Start at the top.

Ori Wellington:

Okay, layer one, strategic oversight this is the highest level. Its job is to make sure everything aligns risk appetite where the money goes, business priorities. It all needs to line up with the overall company strategy.

Sam Jones:

And this is squarely in the realm of ERM enterprise risk management.

Ori Wellington:

Correct, primarily focused on performance and resilience. Think of it as setting the strategic guardrails, the big picture rules for any autonomous systems operating below.

Sam Jones:

Got it Okay, moving down layer two.

Ori Wellington:

Layer two is business orchestration. Now, this is where that coordination piece we just talked about really happens. It's about taking those risk signals, maybe from layer three or four, and routing them across the right business function.

Sam Jones:

Ah, so making sure the right teams get notified and act together.

Ori Wellington:

Precisely Driving coordinated mitigation, making sure operational execution happens smoothly. This is operational risk management, ORM territory and again the goals are resilience and performance, Getting that synchronized business response.

Sam Jones:

Okay, makes sense.

Ori Wellington:

Layer three Layer three Threat intelligence and validation. This is fascinating here you're using AI, real-time data feeds, threat modeling basically simulating attacks and stress, testing your systems constantly.

Sam Jones:

So proactively poking and prodding to find weaknesses.

Ori Wellington:

Exactly Dynamically validating your actual exposure. This sits mainly in technology, risk management, trm, and the objectives are resilience, obviously, but also assurance, knowing your defenses are working. And here's a critical point. The source notes that CrowdStrike's Charlotte AI performs vital functions right here in this layer, providing that real-time intel and validation needed before an autonomous action is taken.

Sam Jones:

Ah, okay, so it's not just reacting, it's informing, the validation before the reaction. That clarifies layer three. So if a threat gets validated there, what happens at layer four?

Ori Wellington:

Layer four is remediation and response. This is where the economist action kicks in, based on those predefined policies and thresholds set higher up.

Sam Jones:

Okay, so what does autonomous mitigation actually look like here? What kind of actions are we talking about?

Ori Wellington:

Could be a range of things. Maybe isolating a user account that seems compromised that's identity isolation. Or perhaps automatically triggering business continuity protocols if a critical system seems under attack.

Sam Jones:

Okay.

Ori Wellington:

It could even involve escalating alerts or actions to third-party vendors if the risk originates with them. This layer involves both TRM and ORM technology and operational risk, because the actions have both tech and process implications.

Sam Jones:

And the goals are resilience and compliance, presumably.

Ori Wellington:

Yes, resilience and compliance are key and crucially. Just like layer three, charlotte AI is highlighted as performing critical functions within this layer two, executing those rapid approved responses.

Sam Jones:

Got it, executing the plan, which brings us to the final layer, layer five, verification and audit. What's the main job here, especially when machines are doing the acting?

Ori Wellington:

Layer five is all about accountability and proof. Its purpose is capturing the evidence of what happened, what the machine did, why it did it, based on what policy.

Sam Jones:

Okay, the digital paper trail.

Ori Wellington:

Exactly Aligning those actions back to specific controls and providing real-time assurance, not just for internal managers, but potentially for auditors, regulators, other external stakeholders too. This is the GRC governance, risk and compliance domain.

Sam Jones:

And the objectives are assurance and compliance Makes sense.

Ori Wellington:

Assurance and compliance, yes, making sure everything is documented and verifiable.

Sam Jones:

So if we kind of tie a bow on these five layers, the whole point of autonomous IRM structured this way is to make sure these super fast machine executed decisions aren't just happening randomly.

Ori Wellington:

Right. They need to be authorized based on strategy.

Sam Jones:

Absorbed into the whole risk picture.

Ori Wellington:

Scored for impact, escalated if needed.

Sam Jones:

Documented for audit and, importantly, used to learn and improve the system over time. Because without that coordinated system across all five layers, these powerful autonomous actions like from Charlotte AI could just end up being isolated incidents.

Ori Wellington:

Exactly. They'd be unmanaged events, potentially causing new risks, which totally defeats the purpose of integrated risk management. You need the whole structure.

Sam Jones:

Okay, that framework is clear, but now we hit this almost paradoxical point you mentioned earlier. The technology, like Charlotte AI, seems ready. It's capable of operating in those crucial layers three and four.

Ori Wellington:

Yeah, the tech is moving incredibly fast.

Sam Jones:

But organizational readiness, that's often a completely different story, isn't it? What's the biggest disconnect? You see there?

Ori Wellington:

That really is the crux of the matter now. The tech capability is leaping ahead, but organizations are struggling to keep up internally.

Sam Jones:

And the source mentions the IRM navigator maturity curve as a way to sort of diagnose this.

Ori Wellington:

It's a useful lens. Think of it as stages moving from basic reactive risk management on one end towards fully integrated predictive autonomous systems on the other. And the key finding, or maybe the warning from the source, is that while the technology, like Charlotte AI, represents a catalyst pushing towards those higher stages, operating layers three and four, most IRM programs today are actually and this is the quote stalled between coordinated and embedded stages on that maturity curve.

Sam Jones:

Stalled. Okay For our listeners. What does that stall really mean in practical terms? Is it just a slight lag or is it a major problem preventing them from actually using these new tools effectively? What do those stages, coordinated and embedded, even look like?

Ori Wellington:

It's a critical choke point, I'd say. Being stalled there means maybe organizations are still just trying to get their different risk and compliance functions to talk to each other consistently. That's the coordinated stage.

Sam Jones:

Silos are still a problem.

Ori Wellington:

Big time. They might have some automated tools in pockets, maybe in security, maybe in compliance, but they lack that overarching orchestration framework, the embedded stage to connect a machine speed event in one area to the necessary business impact analysis and coordinated response across the whole enterprise.

Sam Jones:

So they can't translate the signal properly.

Ori Wellington:

Exactly. It prevents them from moving beyond reacting in fragments, often at human speed, to operating as a truly integrated, resilient and increasingly autonomous organization. The stall means they can't fully leverage the power of tools like Charlotte AI.

Sam Jones:

So if the tech's ready, why the stall? Is it just about needing bigger budgets for new IRM platforms, or is it something deeper?

Ori Wellington:

Budget is always a factor, of course, but the source makes it clear. It's often much deeper than just technology or money. We're talking about significant structural barriers Like org charts, yes, and cultural barriers too how people think about risk and leadership buy-in or lack thereof. In many places, risk management is still treated primarily as an audit function.

Sam Jones:

A check-the-box exercise after the fact.

Ori Wellington:

Pretty much A compliance thing done periodically. It's not seen or run as a dynamic operational system that needs to be woven into the fabric of everyday business decisions. And now machine speed actions.

Sam Jones:

So it's a fundamental mindset shift that's needed.

Ori Wellington:

Absolutely From the very top of the organization down.

Sam Jones:

Which means to really get to autonomous IRM, you have to shift focus. It's less about just documenting risks after they happen.

Ori Wellington:

And much more about orchestrating responses in real time.

Sam Jones:

And moving from looking at compliance as snapshots in time.

Ori Wellington:

To ensuring compliance is built into the real time execution. It's a completely different way of operating, really Essential to keep pace.

Sam Jones:

And this isn't some far offoff future scenario, is it?

Ori Wellington:

No, not at all. The source is really clear on this. It says and I think this is worth quoting again this is live production-level activity initiated by AI, executed within security platforms and demanding immediate reconciliation across policy continuity, third-party and assurance domains.

Sam Jones:

It's happening now.

Ori Wellington:

It's happening now. The autonomous actions are real and the need to integrate them into the broader risk picture is immediate. Organizations have to adapt their IRM programs to handle this reality today.

Sam Jones:

Okay, so let's get practical. Then. For an IRM program wanting to operate at this new machine speed, what are the concrete steps they need to take? The source lays them out right.

Ori Wellington:

It does. First, they absolutely have to be able to ingest this new kind of data, the agentic telemetry, coming from systems like Charlotte AI.

Sam Jones:

Okay, get the data in Step one.

Ori Wellington:

Step two translate that raw signal into meaningful risk context. What does this alert mean in terms of our risk thresholds? Which controls are relevant? Which business units or user personas are impacted?

Sam Jones:

Add the business, meaning Makes sense.

Ori Wellington:

Third, based on that context, trigger the right real-time workflows and, crucially, these workflows need to cut across different IRM domains security, it risk, operational risk, compliance and potentially across different software platforms too.

Sam Jones:

Okay, coordinate the action.

Ori Wellington:

Fourth, capture the evidence. Those machine-driven actions need to be logged automatically as formal evidence for audit trails and compliance reporting. Can't lose track of what the machine did.

Sam Jones:

The verification piece we talked about in Layer 5.

Ori Wellington:

Exactly. And finally, fifth, learn and adapt. Use the outcomes of these autonomous actions, the feedback, to continuously adjust the policies, the rich models, the thresholds. It's a closed-loop system.

Sam Jones:

Ingest translate trigger capture adjust Sounds like a cycle.

Ori Wellington:

It has to be. That's how you build a resilient, adaptive system.

Sam Jones:

And tying this back to the bigger picture, the organizations that can actually build these capabilities across those five functional layers we discussed.

Ori Wellington:

And managed to climb up that IRM maturity curve, getting past that stall point into stage five, the truly autonomous stage.

Sam Jones:

They're the ones who are going to have a serious advantage.

Ori Wellington:

A decisive advantage, I'd say, not just in handling today's crazy fast threats, but really in designing genuine resilience for whatever comes next. They'll be architecting for the future.

Sam Jones:

Okay, so let's try and bring this all together. Crowdstrike with tech like Charlotte, ai has effectively built the nervous system you called it.

Ori Wellington:

Yeah, providing that incredibly fast signal and execution capability, the detection and the immediate response.

Sam Jones:

But that's not the whole story. The enterprise, the organization itself, now has the job of building the what did the source call it? The musculature and memory.

Ori Wellington:

Exactly that's the critical next step Building the architecture, the processes the connective tissue that takes those rapid nerve signals and turns them into coordinated, effective strength in action, embedding that capability deep within the organization's operations.

Sam Jones:

So autonomous IRM it's not really just about plugging in a new piece of AI tech.

Ori Wellington:

Not at all. It's fundamentally about building that connective tissue, making sure risk intelligence doesn't just sit in one place but actually moves, drives action and helps the entire enterprise learn and improve constantly.

Sam Jones:

And the five functional layers give you the blueprint for that structure.

Ori Wellington:

And the IRM Navigator maturity curve kind of gives you the map showing you the journey you need to take.

Sam Jones:

So what's missing for most organizations right now?

Ori Wellington:

Well, as the source puts it so bluntly, what's missing is execution, orchestration, integration. And that execution Is no longer optional, just isn't Because the risk environment. It's definitely not waiting for anyone to catch up.