Q1 2026 compliance and remediation update

Introduction

Welcome to our Turnberry Compliance and Remediation update for Q1 2026.  Each quarter, our C&R team will present a brief rundown of the latest news and analysis of recent compliance, regulation, and security trends. 

The goal of our newsletter is to increase awareness of the challenges, risks, and opportunities we face as we help our clients navigate their Compliance and Remediation journeys.  We also hope to inform our team members of the latest trends in Compliance, including changes presented by AI, new regulations, and global events.

This quarter’s contents:

• AI Risk Focus: What Lies Beneath
• Q1 Compliance Updates

AI risk focus: what lies beneath

Every spring, people from the northern states flock to the sunshine and warm waters of the southern US.  Spring break is a time when families, students, and others seek to leave the snow and cold behind for a week in the sun.  For those who travel to the southeast or the Caribbean, spring break often involves long days spent in or near the ocean.   However, those who live near the ocean know that it’s not all white sand and lapping waves.  There

are hazards that must be acknowledged and respected.  One such hazard is the rip current (a.k.a. rip tide).  While a rip current may be hard to spot, it cannot be dismissed.  Having a strategy to mitigate or address the effects of a rip current helps ensure that swimmers and beachgoers enjoy their day in the water.  So, how does this apply to managing AI risk?

Hidden AI risks can be thought of in much the same way as a rip current.   Rip currents aren’t loud or obvious, and the water may look deceptively calm.  The same can be said for most AI failures.  There are several types of AI failures that can occur while things may look fine on the surface.  For example:

• Model drift
• Hallucinations that sound confident
• Silent data leakage
• Automation bias (“the model said so…”)
• Shadow AI tools outside governance

These risks can creep in unnoticed by those who may not be looking for them.  Recently, an AI agent at Meta (the parent company of Facebook and Instagram) quietly went rogue, exposing sensitive company and user data to employees who did not have permission to access it.  According to the incident report, a Meta employee posted on an internal forum asking for help with a technical question.  (A pretty standard action.)  However, another engineer asked an AI agent to help analyze the question, and the agent ended up posting a response without asking the engineer for permission to share it.  This led to a chain of events where the employee who originally asked the question ended up taking actions based on the AI agent’s guidance, which inadvertently made massive amounts of company and user-related data available to engineers who were not authorized to access it. Had guidance been in place to ensure that the AI response was not disseminated improperly, the internal data breach may have been avoided.

Many of us have been annoyed by lifeguards blowing whistles and interrupting our fun.  Usually, our initial response is “I know what I’m doing.  I’m a good swimmer.”  But often, those lifeguards have more local knowledge and visibility of the area than we do. The same mindset of keeping a human overwatch on the ocean is needed for AI.  Keeping a human in the loop ensures that the AI isn’t allowed to “wander off” into danger.  Human oversight can help the model stay safe by:

• Adding guardrails instead of retraining
• Constraining prompts and outputs
• Using retrieval grounding instead of bigger models
• Inserting checkpoints (human review of output, deployments, etc.)
• Splitting tasks across models (no single point of failure)

With competition in the market, some AI companies are starting to loosen their guardrails rather than tighten them. The recent high-profile split between the US government and Anthropic over military use of AI has led to some competitors (and Anthropic themselves) lowering some safety thresholds. This points to the importance of AI-adopting companies establishing a strong internal AI governance strategy.

Humans can mandate reviews for high-risk outputs (i.e. legal, financial, medical).  We can create a clear RACI for model ownership and establish model risk committees.  Compliance and Risk personnel can also make sure that technology partners adhere to best practices for mitigating failure (e.g. “Fail Safe, Don’t Fail Silent”): 

• Fail safely: Ensure guardrails are in place to ensure that small issues can’t cascade into a larger, systemic failure.
• Fail visibly: When a problem arises, ensure that communications and remediation procedures are utilized to address the issue as soon as possible.  Be open about the root causes and plans to mitigate.  AI failures are not an area to “bury the lead” and hope stakeholders don’t notice.  Treat failures as an opportunity to learn and develop stronger policies and procedures.
• Fail early: Thorough testing and quality assurance checks in DEV (or other lower environments) ensure that the most likely failures can be caught early and harmlessly.  Testers should try to “break the model” in a lower environment.   Include normal cases, edge cases, ambiguous inputs, malformed inputs, adversarial inputs, policy-sensitive inputs, and examples from real production traffic that have been sanitized.  For generative AI, include tests for prompt injection, unsafe instructions, leakage of hidden/system prompts, and harmful tool-use chains.

Much like rip currents, AI output can appear calm and trustworthy on the surface but should still be approached with a healthy dose of skepticism before acting on it.  As AI end-users, we all have a responsibility to monitor outputs and ensure the technology stays under control.  The actions described above can ensure that companies can still make progress on their AI rollout plans without incurring undue risk.  Like planning a safe day at the beach, AI risk management isn’t about stopping the ocean: It’s about knowing where, when, and how to swim.

WIIFM

Turnberry can help companies adopt AI with confidence by putting the right guardrails in place so they can innovate faster without losing control. Our partners get stronger oversight of model performance, fewer confident-but-wrong outputs, better protection against hidden data leakage, less risk that employees will overtrust AI recommendations, and a clearer way to identify and govern shadow AI tools. The result is AI that is safer, more accountable, and easier to manage – so companies can capture the value of automation and insight without creating unnecessary compliance, operational, or reputational risk.

Q1 2026 regulatory radar

Q1 2026 has been defined less by sweeping new mandates and more by targeted regulatory changes that require companies to update controls, systems, and compliance processes now. Across sectors, the common theme is operational readiness: several rules are already in effect, while others are proposed but important enough that compliance teams should begin planning against them immediately.

In healthcare and pharma, the biggest change to take effect is the FDA’s Quality Management System Regulation (QMSR) for medical devices. This change became effective on February 2 and aligns FDA quality requirements more closely with ISO 13485 and raises the bar for procedure updates, training, supplier oversight, and inspection readiness. The FDA also finalized the shift to a 12-digit National Drug Code format, which starts the clock on a major cross-functional remediation effort affecting labeling, ERP, barcoding, claims, and trading-partner systems. In parallel, CMS’s proposed 2027 Notice of Benefit and Payment Parameters signals tighter oversight of Exchange integrity, subsidy administration, and broker conduct. For many healthcare companies, the immediate priority is to confirm QMSR readiness, launch NDC transition planning, and review payer and broker controls against CMS’s proposed direction.

In finance, the Federal Reserve’s February 23 proposal to remove “reputation risk” from its supervisory framework could reshape how banks document risk and respond to examiners. The SEC’s Form N-PORT proposal would ease portfolio reporting burden by extending filing timing and restoring quarterly, rather than monthly, public disclosure. Meanwhile, finalized FDIC and OCC actions updated official signage and advertising requirements and removed a legacy fair housing home-loan data reporting obligation. Compliance teams should review governance materials that still reference reputation risk, monitor possible N-PORT reporting-calendar changes, and refresh disclosure inventories and control libraries to reflect the FDIC and OCC updates.

For retail, the clearest nationwide change is the FTC’s 2026 Hart-Scott-Rodino threshold update, effective February 17, 2026, which affects whether acquisitions and investments trigger premerger notification. At the same time, New York’s Attorney General has already signaled active enforcement of the state’s algorithmic pricing disclosure law, making clear that companies using personal data to drive pricing must provide compliant disclosures and be able to prove they did so. To ensure compliance, retailers should update transaction-screening thresholds, inventory any pricing or personalization tools that rely on customer data, and retain logs, screenshots, and governance documentation supporting disclosure compliance.

Across all four sectors, Q1 2026 shows regulators continuing to push compliance closer to the systems, data, and decision logic that run the business. Organizations that move early to align controls, documentation, and ownership with these developments will be in a stronger position for the rest of 2026.

WIIFM

Turnberry Solutions can help organizations translate these regulatory changes into practical, enterprise-ready action by aligning compliance requirements to business processes, systems, and measurable outcomes. Our teams partner with clients across industries to assess current-state readiness, identify control and process gaps, and build actionable roadmaps that prioritize both immediate remediation and longer-term operating model improvements. Turnberry brings the cross-functional delivery expertise needed to coordinate compliance, technology, operations, and change management. The result is a more resilient organization that not only responds to regulatory change efficiently but also improves transparency, reduces risk, and strengthens operational performance.