Where Autonomous IRM Begins—And Where It Must Go Next
The Quiet Rise of Autonomous IRM—From the Middle Out
Autonomous IRM is no longer theoretical. AI-powered platforms are starting to deliver tangible value: agentic systems that simulate attacker behavior, validate control effectiveness, and recommend mitigation actions—often autonomously.
The June 5 announcement from Tuskira, integrating directly with ServiceNow’s Vulnerability Response and SecOps modules, is a prime example. By embedding simulation-backed scoring and posture-aware mitigation into operational workflows, Tuskira is delivering intelligence in real time.
But there’s something missing: the announcement doesn’t mention Integrated Risk Management (IRM) at all.
That silence is a signal. Tuskira operates in what Wheelhouse Advisors defines as Layer 3: Intelligence & Validation—the middle of the risk architecture. And while this layer is where automation is gaining traction, it’s also where many organizations are managing in isolation, without input from either end of the enterprise risk stack.
Autonomous IRM is emerging from the middle. But to be successful, it must ingest strategic inputs from above and provide validated outputs for assurance below.
The Five Functional Layers of Autonomous IRM
As part of the IRM Navigator™ Model, Wheelhouse Advisors defines five interconnected layers through which risk flows. These are not maturity levels or operational silos—they are decision layers that align to the IRM objectives of Performance, Resilience, Assurance, and Compliance (PRAC) and the risk domains – ERM, ORM, TRM and GRC.
The dominant objective is listed first for each layer. Compliance is not an independent or dominant objective—it is an outcome of the other three. The goal is not more compliance activity, but smarter, evidence-backed assurance.
ERM (Layer 1): Provides the strategic lens—risk appetite, risk-capacity thresholds, and alignment with enterprise goals. It informs what should be prioritized in simulation and response.
ORM (Layers 2 & 4): Acts as the connective tissue—normalizing data from operational units (Layer 2) and coordinating corrective actions (Layer 4). ORM also integrates residual risk tracking into assurance activities.
TRM (Layers 3 & 4): Anchors the real-time, telemetry-driven portion of the architecture. It uses simulations, adversary modeling, and automated scoring to power Layer 3, while also triggering responses in Layer 4.
GRC (Layer 5): Often misunderstood as IRM itself, GRC remains essential—but it’s best applied at the tail end of the process. It documents what happened, proves it, and satisfies regulatory requirements, but does not guide real-time decisions.
Managing in the Middle—But Starving from the Edges
Most current platforms operate in Layer 3—validating known risks and guiding technical response. But that intelligence is often starved of context from the outer layers:
Strategic priorities (Layer 1) are not being fed downward, so simulation engines prioritize technical severity rather than business relevance.
Verification and audit feedback (Layer 5) is fed back into validation logic, so known control failures are not simulated again—or worse, ignored entirely.
This is why many security teams end up automating noise. Without guidance from above and confirmation from below, Layer 3 becomes a dead end.
True Autonomous IRM is not just faster simulation. It is connected decision-making—fed by strategy and confirmed by assurance.
The Two-Way Street: How IRM Must Really Work
While platforms like Tuskira manage validated threats inside operational workflows, few can ingest the signals that should shape those simulations. That is the core flaw in the current model.
To correct it, organizations must embrace a two-way architecture:
Strategy must flow downward from Layer 1, so simulation and response focus on the right assets and exposures.
Validated intelligence must flow outward from Layer 3, informing dashboards, decision rights, and board-level oversight.
Audit must close the loop, verifying that remediation was effective—and aligned to enterprise priorities.
Today, many firms have automation at the core but no input from the outer layers. That’s not integration—it’s isolation at scale.
Case Study: When Strategy Didn’t Flow Down
In 2022, Toyota publicly acknowledged that a subcontractor had exposed API credentials for its T-Connect telematics platform on GitHub—leaving nearly 300,000 customer records vulnerable for over five years. The breach was not technical in nature—it was strategic.
Mapping the breakdown:
Layer 1 (Strategy) failed to classify telematics as Tier 1 data—even though it tied directly to customer trust and digital services.
Layer 3 (Simulation) did not detect stale, embedded credentials—because they weren’t prioritized in the simulation logic.
Layer 4 (Response) focused on patch velocity and endpoint management—not long-lived authentication risks.
Layer 5 (Audit) failed to flag the issue until it became public.
Had strategic business value flowed downward—and assurance data fed back in—the API exposure might have been addressed years earlier.
Source: Wheelhouse Advisors
PRAC in Practice: What the Layers Deliver
Each IRM objective aligns with specific layers:
Performance originates in Layer 1, where strategic priorities are set.
Resilience is built in Layers 2 through 4, where orchestration, validation, and action occur.
Assurance comes from Layer 3 (validation) and Layer 5 (verification).
Compliance is achieved as an outcome—when the system is working, not when checklists are filled.
Without connectivity, organizations end up optimizing for the wrong outcome—or worse, institutionalizing misalignment through automation.
What Risk Leaders Should Do Now
To build a true Autonomous IRM model:
Map your existing systems to the five-layer structure. Identify layers where automation exists but integration is missing.
Push strategic metadata into your simulation logic. Flag growth initiatives, high-trust assets, and emerging exposures.
Feed assurance data back into Layer 3. Let audit results refine future simulations—not just post-mortem reviews.
Shift performance metrics from velocity (e.g., time to close tickets) to relevance (e.g., value-at-risk reduction).
Follow the right sequence: Simplify. Automate. Integrate. Automation is only as effective as the model it amplifies.
Final Word: Autonomy Without Alignment Is a Risk Multiplier
Autonomous IRM is not about replacing people—it’s about giving them better context, clearer priorities, and validated outcomes. That’s only possible when the middle layer—where simulation occurs—receives inputs from both ends of the architecture.
We are not waiting for Autonomous IRM to arrive. It’s already here—in parts. The next step is to connect those parts into a living, learning system that reflects what the business values—and verifies that those values are protected.
When that happens, IRM moves from compliance to consequence. And risk management finally becomes strategy in action.
References
Tuskira, Tuskira Announces Integration with ServiceNow to Deliver Validated, Risk-Based Security Response, BusinessWire, June 5, 2025
GitGuardian, Toyota Exposed a Secret Key Publicly on GitHub for Five Years, October 2022
Corsha, What We Can Learn from Toyota’s API Security Breach, December 2022
ISC², Cybersecurity Workforce Study 2024, October 2024
Wheelhouse Advisors, IRM Navigator™ Annual Viewpoint Report, 2025 Edition
Institute of Internal Auditors, The Three Lines Model, July 2020
