Reg SCI, Market Infrastructure, and the AI Risk Management Question

Reg SCI, Market Infrastructure, and the AI Risk Management Question

Regulation Systems Compliance and Integrity has been around since November 2014, when the SEC adopted it as Rule 242.1000 through 242.1007. It replaced the old Automated Review Policy (ARP) framework, which was voluntary and, frankly, had no teeth. Reg SCI was the SEC's response to a string of high-profile technology failures: the May 2010 Flash Crash, the BATS IPO debacle in 2012, the NASDAQ freeze during the Facebook IPO. The message was clear. If you operate critical market infrastructure, your systems need to work, and you need to prove it.

The rule applies to a specific set of entities: national securities exchanges, registered clearing agencies, FINRA, the MSRB, plan processors, and certain alternative trading systems that meet volume thresholds (those exceeding 5% of average daily NMS volume in four of the last six months). These are "SCI entities," and their obligations are substantial. They must establish written policies and procedures to ensure the capacity, integrity, resiliency, availability, and security of their SCI systems. They must conduct annual reviews. They must notify the SEC of "SCI events" and "systems intrusions," sometimes within 24 hours.

Where AI Enters the Picture

Here is what makes this interesting right now. SCI entities are increasingly deploying AI and machine learning tools across their operations. Surveillance systems use ML models to detect manipulative trading patterns. Clearing agencies use predictive analytics for margin calculations and risk management. Exchanges use AI for capacity planning and anomaly detection. These are not peripheral tools; they sit inside or directly adjacent to SCI systems.

Reg SCI defines "SCI systems" broadly. They include systems that "directly support" trading, clearance and settlement, order routing, market data, market regulation, or market surveillance. An ML model that feeds into a surveillance engine or adjusts margin parameters is, by any reasonable reading, part of an SCI system or a system that directly supports one. The SEC has not issued specific guidance on AI within Reg SCI, but the text of the rule does not need to mention AI explicitly to cover it. The obligations attach to the systems themselves, regardless of whether the underlying logic is rule-based or learned.

The Specific Risk Treatment Problem

Standard software development has well-understood testing, change management, and rollback procedures. You write code, you test it, you deploy it, and if something breaks, you can trace the failure to a specific change. AI models introduce a different kind of risk. A model retrained on new data can behave differently without any code change. Drift in input data distributions can degrade model performance silently. Adversarial inputs, whether intentional or accidental, can produce outputs that no one anticipated during validation.

Reg SCI requires SCI entities to conduct "SCI reviews," which are comprehensive examinations of their SCI systems. These reviews must include assessments of capacity, security, and operational risks. The rule also requires business continuity and disaster recovery testing, including mandatory industry-wide tests. For traditional systems, these requirements map onto established practices. For AI systems, the mapping is less obvious.

Consider a few concrete scenarios:

• Model retraining as a change event. Under Reg SCI, material changes to SCI systems require advance notification to the SEC. If a surveillance model is retrained and its detection thresholds shift meaningfully, does that constitute a material system change? The rule does not say. But if the retrained model fails to catch a pattern it previously flagged, and that failure leads to a market disruption, the SCI entity is on the hook for an SCI event report and potentially an enforcement action.
• Explainability and root cause analysis. When an SCI event occurs, entities must provide the SEC with a detailed description, including root cause analysis. For a deterministic system, root cause analysis is hard but tractable. For a neural network that produced an anomalous output, explaining why is a fundamentally different challenge. The SEC's Division of Examinations has flagged model explainability as an area of interest in its 2023 and 2024 examination priorities.
• Capacity and stress testing. Reg SCI requires entities to maintain sufficient capacity, including the ability to handle volume spikes. AI inference workloads have different scaling characteristics than traditional order-matching engines. GPU availability, model serving latency, and batch processing bottlenecks introduce failure modes that conventional capacity planning may not address.

What the SEC Has Signaled

The SEC has not amended Reg SCI to address AI specifically, but the signals are there. In March 2023, the SEC proposed amendments to Reg SCI that would expand the definition of "SCI systems" to include certain indirect support systems and cloud infrastructure. The proposal also would broaden the scope of entities covered. While the final rule has not been adopted as of mid-2025, the direction is toward more coverage, not less.

Separately, the SEC's 2024 examination priorities explicitly mentioned AI and predictive data analytics as focus areas for broker-dealers and investment advisers. While those are not SCI entities per se, the regulatory posture is consistent: if you are using AI in a way that touches market integrity, expect scrutiny.

Commissioner Hester Peirce has noted the tension between encouraging innovation and imposing prescriptive technology requirements. But even from a deregulatory perspective, the core Reg SCI obligation remains. Your systems need to work. If they do not, you need to know immediately and tell the SEC. The mechanism by which they fail, whether a bad code deploy or a drifting ML model, is irrelevant to the obligation.

Practical Implications for SCI Entities

If you are an SCI entity deploying AI in or near SCI systems, a few things follow:

• Treat model updates like code releases. Establish change management procedures for model retraining that mirror your existing SCI system change protocols. Document the delta between model versions. Set thresholds for what constitutes a "material" change.
• Build monitoring for model behavior, not just system uptime. Traditional SCI monitoring focuses on latency, throughput, and availability. AI systems need additional monitoring for output distribution shifts, prediction confidence degradation, and input data anomalies.
• Prepare for explainability demands in SCI event reports. If an AI component contributes to an SCI event, you will need to explain what happened in terms the SEC can evaluate. Invest in interpretability tooling now, before you need it under pressure during a 24-hour reporting window.
• Include AI failure modes in your BCP/DR testing. Your annual Reg SCI testing should include scenarios where AI components fail, produce degraded outputs, or behave unexpectedly under stress. The SEC's 2014 adopting release specifically noted that testing should cover "reasonably foreseeable" disruptions. AI failure modes are foreseeable now.

The Broader Point

Reg SCI was written to be technology-neutral, and that is both its strength and its gap. The obligations are clear: your systems must be resilient, secure, and well-governed. But the compliance practices that developed around those obligations assumed deterministic software. AI breaks that assumption in specific, identifiable ways. SCI entities that do not adapt their compliance programs to account for those differences are carrying risk they may not fully appreciate, risk that crystallizes the moment a model misbehaves during a volatile trading session.

The SEC does not need to write a new rule to enforce Reg SCI against AI-related failures. The existing rule is sufficient. The question is whether your internal policies and procedures are sufficient to meet it.

How FirmAdapt Addresses This

FirmAdapt's architecture treats AI model governance as a first-class compliance function, not an afterthought bolted onto existing workflows. For organizations operating under Reg SCI or similar infrastructure-level regulatory frameworks, FirmAdapt provides continuous model monitoring, automated change documentation, and audit-ready reporting that maps directly to the policy and procedure requirements in Rule 242.1001. Model retraining events are logged, versioned, and assessed against predefined materiality thresholds.

FirmAdapt also supports the explainability requirements that Reg SCI event reporting effectively demands. When an AI component behaves unexpectedly, the platform captures the context needed for root cause analysis, including input data snapshots, model version metadata, and output deviation metrics. The goal is straightforward: make it possible to meet your existing Reg SCI obligations even as the technology underlying your SCI systems evolves.