Regulators, customers, and major financial institutions continue to publicly raise the bar on what “secure by design” really means, it’s clear that compliance checklists and static controls no longer cut it.  

As someone responsible for both innovation and risk at Shield, I believe agility—when structured correctly—isn’t a security tradeoff. It’s a multiplier. In a world where systemic risk often flows through the supply chain, security should not be an overlay, but a design principle embedded in culture, code, processes, and cloud infrastructure. 

Companies shouldn’t secure data because the industry says they should—but because trust is the cornerstone of every relationship. 

Agile by intent, secure by design 

At every stage of my career, I’ve made security the foundation. 

It is not just a department, it’s a culture. It should be woven into the fabric of everything we do—every team we have. Our agile structure lets teams move fast and stay current—an essential edge as threats evolve daily. 

With a holistic view on security in all aspects, I’ve maintained a security design signoff prerequisite step of any feature design or development. By going further, security is a core discipline within R&D, establishing a baseline where developers write secure code by instinct, not instruction. That’s the outcome we care about. 

When we build, we build with ownership. Every engineering decision reflects an understanding of impact, risk, and trust. This security-native thinking enables the kind of execution precision larger firms often struggle to achieve. 

Third-party proof, not just internal confidence 

Anyone can say they’re secure. I believe you have to prove it. 

From internal testing to external validation, security isn’t just a posture, it’s doing the work day in and day out to maintain it.  

Our technology is tested by the world’s most respected cybersecurity firms, including Deloitte. These continuous penetration tests go beyond surface scans and dig deep into real-world attack scenarios. At the same time, our internal operations are audited to SOC 2 Type II standards, validating that our security practices are not only in place but actually work—consistently. Our Secure Software Development Lifecycle (SSDLC) goes beyond checklists. It starts before the first line of code is written and extends across the entire lifecycle. 

This dual validation—from technology to team—is our way of saying: Don’t take our word for it. 

Security by architecture, not just policy 

In 2025, the organizations earning the most trust aren’t the ones with the biggest infrastructure—they’re the ones that treat security, compliance, and scale as first principles, not afterthoughts. Legacy thinking says archives belong in the basement. Modern resilience means architecting for visibility, speed, and control from day one. 

I’ve made sure we threat-model every feature: We instrument every pipeline. We use active tooling that halts unsafe code and gives developers real-time feedback. It’s not just DevSecOps—it’s continuous, contextual, and deeply integrated. 

We: 

• Monitor how code is structured 
• Analyze cloud infrastructure definitions 
• Validate identity and authorization flows 
• Embed live feedback loops from runtime behavior 

Data is sacred—and guarded accordingly 

In today’s digital economy, customer data is both the crown jewel and the crown risk. Treating it as sacred isn’t just a compliance statement—it’s a cultural one. The organizations leading the charge are the ones who recognize that true data stewardship requires more than perimeter defense; it demands continuous, internal accountability as well. 

Our access policies are governed by a “just-in-time, least-privilege” approach. This means only the right people, at the right time, and only for the exact task required—no more, no less. Every access is logged, audited, and automatically expired unless revalidated. No exceptions. Most importantly, customers remain in control: Every safeguard we implement supports transparency, accountability, and compliance with the highest regulatory standards. Data isn’t just protected—it’s governed with precision. 

Where trust is built 

Crises happen. Systems fail. Threats evolve. Resilience isn’t built in the moment—it’s rehearsed long before. In a world where disruption is inevitable, the leaders separating preparation from performative compliance are the ones who treat crisis playbooks as living systems, not shelfware. Many vendors concentrate on protection and often overlook crisis planning.  

Shield, however, maintains a living business continuity and crisis management playbook. These aren’t just documents for compliance—they’re action plans tested regularly to ensure our teams are ready when it counts. From cyberattacks to system outages, we’ve mapped, rehearsed, and prepared for scenarios our customers may experience. 

The SaaS landscape is changing fast, and so are the threats. Supply chain risk, identity sprawl, and AI-powered attacks are no longer emerging—they’re here. But so is the opportunity to lead. 

I believe trust isn’t won by scale. It’s earned through consistency, transparency, and execution. And as the industry raises its expectations, we’re proud to be among the companies leading that shift. 

Because in a world where software runs everything, trust should run the software. 

Learn more about our security here.

The post Trust is the new architecture appeared first on Shield: Digital communication governance and archiving solutions.

This bulletin turns a spotlight on what many in legal and compliance functions have long known: Information barriers are not a sufficient control. You may say two people shouldn’t talk about x, but information barriers cannot sufficiently stop them from talking about x. In an environment where material non-public information (MNPI) can leak across voice, chat, or email in seconds, firms need to rethink how information control frameworks actually work. 

The stakes have changed—and so must the approach. 

The FCA’s red flags: What firms should take away 

The FCA’s bulletin doesn’t just revisit UK MAR expectations. It raises questions about the operational integrity of how firms handle sensitive deal information today. In particular, the regulator is concerned about: 

• Strategic or negligent disclosures that appear to originate from inside deal teams 
• Weak enforcement around “need-to-know” protocols across advisory and issuer networks 
• A lack of auditable oversight mechanisms to detect, prevent, and respond to information seepage 
• Culture and governance gaps that enable sensitive data to circulate too freely—and too informally 

When the FCA starts linking M&A leak patterns with enforcement risk, it’s not a time for incremental fixes. 

Rising risks in the numbers 

The FCA’s 2024 Suspicious Transaction and Order Report (STOR) figures reinforce the urgency behind Market Bulletin 54.  In 2024, 87% of all STORs submitted related to insider dealing—the majority linked to trading ahead of earnings announcements and M&A activity. Equities dominated the reports, while commodities, fixed income, and FX markets showed significantly lower volumes, raising concerns about under-surveillance and potential blind spots. 

The takeaway is clear: While equity surveillance appears relatively mature, non-equity markets like commodities, FX, and fixed income lag behind, suggesting blind spots in detection and reporting. In less-monitored asset classes, gaps can be even wider. Without comprehensive monitoring and active information barriers, firms risk missing critical threats—and exposing themselves to growing regulatory scrutiny. 

Why legacy information barriers aren’t enough anymore 

The way firms used to manage inside information—with restricted lists, firewalled teams, and manual compliance checks—still leaves the space for leaks wide. Even with surveillance ad hoc reviews based on lexicons does not provide preventative or adequate control. Digital collaboration and hybrid working models have blurred boundaries and made static controls feel increasingly performative. 

The numbers from the 2024 STOR report show that insider risks are rising even in highly surveilled markets. As trading patterns become more complex and corporate activity increases, static information barriers leave firms exposed to faster-moving, harder-to-detect leaks. 

In practice, many firms struggle with: 

• Visibility into how restricted lists actually translate across communication platforms 
• Differentiating between permissible internal collaboration and boundary-crossing disclosures 
• Retrospective reviews that surface issues too late to mitigate reputational or legal damage 

The result is an ever-widening gap between policy and practice—one that the FCA, and other regulators, are now pointing to explicitly. 

Now what? 

Getting ahead of this risk isn’t just about tightening controls—it’s about making them dynamic, contextual, and enforceable. Firms serious about preventing unlawful disclosures during M&A activity (and similar high-risk events) should focus on two core shifts: 

• Automate the linkage between restricted entities and communications surveillance—including voice, email, chat, and collaboration platforms. 
• Monitor both proactively and retroactively, surfacing misuse or unauthorized access for the entire time an individual or team remains on a “need-to-know” list. 

It’s this kind of dynamic enforcement that moves a firm from “we had a policy” to “we saw the breach, and we stopped it.” 

What good looks like: Bridging compliance lists and communications 

At Shield, we’ve seen the benefits of this firsthand. Our  Information Barriers model within Shield Surveillance was designed to operationalize the surveillance of sensitive information across eComms and voice. It connects compliance lists—watch, restricted, deal, research—to real-time alerts and review workflows. 

It doesn’t just enforce policy. It closes the loop. 

By scanning for risk across the lifecycle of a deal, the platform gives compliance teams the ability to detect and respond to potential leaks while individuals are still within the “need-to-know” window. Whether used proactively to prevent misuse, or retroactively to investigate, it provides the accountability regulators are demanding. 

Shield’s broader platform was also recently recognized by Gartner as a Visionary in the Digital Communications Governance and Archiving (DGCA) Magic Quadrant and a number 1 vendor in AI critical capabilities. One of the reasons cited: Our ability to operationalize AI for modern surveillance—and translate policy into actual protection. 

Rethinking risk 

Information barriers aren’t just a compliance concern. They’re a trust signal. They protect firm reputation, deal value, and client confidence. As the FCA sharpens its focus on information control, firms have an opportunity to get ahead—not just to avoid fines, but to build smarter risk cultures. 

In a world where leaks are no longer tolerated as inevitable, enforcement is no longer about the breach. It’s about the response. 

And that’s something every firm should be ready to show. 

The post Strengthening information barriers: Why it matters now  appeared first on Shield: Digital communication governance and archiving solutions.

Embedded throughout the AmplifAI suite, Shield’s AI agents are now in use across leading financial institutions, enhancing how firms investigate, detect, and govern complex communications. Delivering real-world results, teams have reduced investigation times, improved risk detection clarity, and slashed manual workloads by automating critical compliance workflows. 

“These agents don’t just answer questions — they think through tasks, orchestrate actions, learn from outcomes, and evolve,” said Mr. Ofir Shabtai, Co-Founder and CTO at Shield. “Shield’s AI agents don’t just help with compliance, they strengthen it. With multi-layered safeguards and human-in-the-loop (HITL) design they critically validate and self-reflect to ensure firms are more thoroughly defended from risk.” 

Live in AmplifAI today: How agentic AI delivers impact 

Shield’s AI agents are now live and integrated into all core compliance workflows within Shield AmplifAI — and are designed specifically for the challenges of financial compliance. They operate within the AmplifAI toolkit to reduce manual workloads through automation of burdensome tasks, increase investigation speed through natural language queries, and enhance decision-making across complex datasets through simplified and concise rationale. 

AmplifAI your Analysis: Shiela – Your agentic assistant for compliance search and analysis 
Natural language in, prioritized answers out. Shiela now leverages multiple AI agents to understand natural language queries, search and assess customer’s data, reason through requests, critically prioritize most relevant communications, and reason through to provide a clear explanation to the user around the results. 

AmplifAI your Surveillance: now leveraging multi-agent orchestration for tailorable nuanced coverage and noise reduction: 

• Fortified Surveillance – Agentic intelligence for risk detection 
  With the introduction of agentic AI, Fortified Surveillance provides an autonomous second round of risk review according to your compliance needs, whether ensuring no nuance is missed in non-alerted comms, to critically assess alerts and further reduce false positives or other configurations according to goals.  

• Risk Reasoning – Explainable AI that thinks before you act 
  AI agents deliver clear, human-readable rationale for alerts, streamlining and accelerating triage across large volumes of alerts and long communications.   

Customers already leveraging these capabilities report significant improvements in user time-to-impact (TTI) and risk clarity. For a leading global bank, Risk Reasoning accelerated the alert review process enabling users make decisions in minutes in place of hours. It also provided intelligent qualification for false positives, allowing analysts to quickly and confidently categorize them, reducing their manual workload without sacrificing oversight.  

Trust, transparency, and governance by design 

Shield AmplifAI leverages agentic AI with strict architectural safeguards to ensure full alignment with compliance expectations. Every agent operates under identity-verified security controls and delivers outputs that are fully traceable, auditable, and explainable. 

AmplifAI’s agentic AI also maintains a human-in-the-loop approach, ensuring that autonomy never comes at the expense of control. Compliance teams retain full oversight of each decision, with every step guided, validated, and aligned to internal policy and regulatory standards. 

In an AI-driven world, your insights are only as strong as your data, so Shield’s agents operate across unified, audit-ready datasets—built on a platform designed to defend your data, defend your decisions, and defend your compliance. No siloes. No blind spots. Just trusted, explainable intelligence. 

The result is a system that accelerates compliance outcomes while fully aligning and global regulatory requirements and compliance frameworks including GDPR, the EU AI Act, HIPAA, FCA, and FINRA. 

Compliance that moves at the speed of risk 

With this advancement, Shield reinforces its position as a pioneer in AI-driven compliance, delivering not just innovation, but impact—making every compliance program smarter, faster, and more resilient against the complexities of tomorrow.  

Shield is already developing the next wave of agents to accelerate risk detection, dynamically enforce policies, and reinvent compliance investigations. 

Stay tuned: The future of compliance isn’t reactive—it’s agentic. 

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The discussion was moderated by Jess Jones, Surveillance SME for Thought Networks, and the panel featured Dr. Shlomit Labin, Shield’s VP of Data Science, Kay Firth-Butterfield, CEO of Good Tech Advisory, and Erin Stanton, the AI & Data Lead at Virtu Financial.

While you may have missed the webinar, we captured it for you below (and if you want to tune in, click here)

Transparency and self-governance in AI

ChatGPT supports 300 million weekly active users—and it was only launched 2 years ago. While this level of adoption presents countless opportunities, it also brings complex ethical challenges. Unlike traditional rule-based systems, today’s machine-learning models are notoriously difficult to govern.

Growing fast but with regulation still playing catch-up, all panelists agreed that firms need to protect themselves by updating their internal govenance strategies. Labin specifically urges users to put guardrails in place to address declining AI explainability.

GenAI results should be validated against external knowledge, or with more traditional technologies such as Google search. Compliance teams can reestablish governing power by prompting LLMs to explain chains of reasoning and comparing answers from multiple models.

Another concern raised is security. While there are no easy answer, the simple action of being transparent about data inputs and outputs can boost public perception. AI model-builders like Stanton are setting the stage for a more open-source industry by leading with transparency and publishing the data they use, but more importantly, the data they wish they had. She explains that calling out shortfalls encourages data sharing and helps to bridge gaps in datasets.

Firth-Butterfield notes that being aware of shortfalls extends beyond just the data provided and into the sustainability of the technology period. “LLMs are extremely thirsty for energy, consuming ¼ litres of water every time you ask a question”.

Being open about inefficiencies in technology leaves space for you to actively teach users how to navigate those areas – in this case, raising awareness of AI’s resource consumption reduces waste because users may opt to use a more resource efficient option instead.

AI’s role in Financial Services

AI’s ‘black box problem’ poses issues for compliance teams, especially in high-stakes industries like finance. Compliance teams must understand how decisions are made, and Stanton’s team at Virtu aids this by documenting every dataset inclusion and exclusion, as well as every algorithm used. They then share this information in layman’s terms to ensure everyone can understand and challenge the model’s logic. “Our compliance team loves that we’ve built this into every step of our process.”

Stanton explains that developers have an ethical and social responsibility to place guardrails inside of their models, as LLMs will learn bias from plain data if left unchecked. She makes a great point that model builders ultimately have responsibility over deploying models “even if I’ve spent a year building this model, if I don’t love how it works then I just won’t deploy it.”

For example, if a dataset lacks strong representation from a region like Asia, developers can block outputs for that geography to avoid unreliable predictions. 

The bias problem: Inclusion in AI

The panel didn’t shy away from discussing AI’s diversity gap. LLMs are trained on internet data, data that overwhelmingly reflects the perspectives of white males from the Global North. “Just the fact that this is an all-female panel,” said Firth-Butterfield, “helps to diversify the data pool.”

For people of color the rate of representation is even worse, as ⅓ of the global population isn’t connected to the internet, and therefore none of their data is represented.

And the challenge is getting worse. With increasing reliance on AI-generated data, we’re witnessing a phenomenon called ‘model cannibalism’ where AI models are being trained on their own outputs, compounding bias over time. It’s estimated that as early as the middle of 2026, there’ll be more AI than human-created data! The EU AI Act and other international AI policies aim to reduce risks stemming from these biases; for example, creating a risk profile around a person is now prohibited because vendors can’t ensure that their AI models will be free of bias.

Shield’s role in the future of AI

The panel agreed that while AI is revolutionizing compliance, the challenge lies in how we as a community govern its use. But with the right voices at the table, we can work towards a future where AI is inclusive, accountable, and free of bias.

We’re committed to creating space for important conversations to happen, because the future of AI isn’t just about models and data—it’s about people.

Watch the full webinar on-demand here.

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Regulators back to business(ish) 

Considering the recent change in administration, a predictable chorus of “back to our traditional mission” pervaded—I mean, the actual words were repeated. Regulators emphasized charging bad actors while also signaling a shift back toward guidance-first leadership. (Read: The creative thinking that birthed Panuwat need not apply.) 
 
Mary Jo White dusted off her cross-examination skills, pressing a panel of regulators to confirm whether penalties would decrease under the new regime. None affirmed — how could they? — but a collective pause suggested she might be onto something. 
 
Meanwhile, in the hallways and bars, a different wind blew. Several regulatory panelists insisted that deeper stances would only emerge once other executive branches were fully staffed. Boilerplate evasion or the first signs of more structural changes? No one could say for sure. 

Tales from the crypto

Crypto got a more welcome, albeit still vague, reception. Regulators gave nods to its potential while reaffirming that enforcement remains grounded in time-tested violations — think Rule 2110 and its ilk. 
 
The SEC’s Emerging Technologies unit continues to focus on AI-washing cases, many of which conveniently intersect with digital asset promotions. But broader, consistent guidance around crypto regulation remains a work in progress. 

AI: On the other hand…

AI was presented as both promise and risk — with a strong preference for the present-tense, acceptable uses over speculative ones. One striking reminder: If AI speeds up legal workflows, the billable hour can’t pretend otherwise. 
 
A panel of surveillance leads highlighted compelling AI use cases: 
– Alert closure and QA of those decisions 
– A centralized U4 repository 
– Sentiment surfacing in written communications 
 
One panelist noted issues with emoji and multilingual coverage — both areas where AI has a clear use case. (FWIW, Shield offers native emoji and language coverage OOTB). 

What are off-channel comms again? 

SIFMA’s straight-faced panel laid out the full arc of off-channel comms enforcement. They traced the fines, the SEC’s dissents, and the more retooling of consequences — lighter monetary penalties, no ICCs, and no heightened supervision. 
 
The takeaway? The regulatory posture is shifting, but that doesn’t mean the pressure is off. The expectation is clear: Firms must proactively define and defend their communication boundaries. 

Bottom Line: 

The themes are familiar — enforcement, guidance, crypto, AI, off-channel comms. But beneath them is a regulatory community recalibrating. Not pulling back. Not pushing forward. Just shifting gears. 

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Shield is eliminating these obstacles with an expanded API suite that sits on Shield API Hub, giving enterprises full control over their compliance data. By integrating compliance related data directly into their existing infrastructure and systems, organizations can streamline risk monitoring, strengthen oversight, elevate insights, and reduce operational friction. 

Removing barriers to compliance data access 

Shield’s API Hub is built for organizations that need direct, structured access to their compliance data, without workarounds or delays. These latest updates build on the existing API suite, covering additional APIs such as legal hold, case management, and focus on ensuring organizations have direct, structured access to their compliance data, without workarounds or delays, including:  

• Complete access to compliance data – Extract everything from alerts and policy violations to audit logs and digital comms metadata. 

• Direct system integrations – Align compliance data with existing BI and analytics, and case management systems for faster investigations and better oversight. 

• Enterprise-grade security & control – A two-layer architecture ensures high-performance data processing while enforcing strict access control through JWT authentication. 

• Advanced analytical insights – Automate reporting and investigations by integrating compliance data directly into analysis and BI systems to allow more visualization tools. 

Building a scalable compliance ecosystem 

This API expansion introduces new capabilities to extract alert and hit data, with audit log and eComms metadata APIs coming soon, reinforcing Shield’s commitment to providing compliance teams with the tools to extract value from their data, not just store it. 

“Data access isn’t an afterthought, it’s a necessity for modern compliance,” says Tamar Sharir Beiser, Chief Product Officer at Shield. “Organizations shouldn’t have to fight to use their own compliance data. With Shield’s API Hub, compliance teams move beyond data storage to full operational control, allowing them to manage risk on their own terms.” 

“We’re seeing growing demand from enterprises looking to move past manual exports and rigid reporting tools,” adds Sharir Beiser. “This is about giving compliance and IT teams the ability to structure and automate their workflows, reducing complexity while maintaining complete oversight. 

Command your compliance data 

With Shield’s API Hub, enterprises gain direct control over their compliance ecosystem. By eliminating bottlenecks and ensuring full data accessibility, Shield enables organizations to act faster, uncover hidden risks, and align compliance operations with business objectives. 

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More importantly, how well does a GenAI vendor understand the need to offer context behind a model’s output and accordingly tailor its development practices? 

Communication is complex and firms need models that can distinguish between casual conversation and potential red flags, understand the subtle differences in communication across various financial markets, and adapt to the ever-evolving language of finance. 

Context is everything when it comes to AI-driven communications monitoring and surveillance. Technology is reshaping the landscape of financial compliance and risk detection with a layered approach to building context-aware AI models. One thing is becoming very clear—the future of surfacing risk with GenAI lies in understanding data better and offering firms the flexibility to understand context of their outputs. 

The significance of context in GenAI for financial institutions 

Context is essential to understanding any form of communication—financial or not. Often, seemingly common phrases can have very different implications depending on the context. 

For example, the phrase “I really need a favor” might be innocuous in most situations, but in the context of a cross-border deal or in a market where the exchange of favors is less common than regulation might permit, it could be a significant red flag. 

Specialized jargon and market-specific language make a model’s task more challenging. For instance, in equities markets, sharing MNPI is strictly forbidden, while in energy markets, discussions about utilities or potential delivery delays are more common and not necessarily indicative of wrongdoing. 

Firm-based differences add another layer of complexity. For example, the way traders at one bank communicate might differ subtly from their counterparts in your bank. GenAI models need to be sophisticated enough to recognize these nuances without overemphasizing them or creating false positives based on regional or firm-based speech patterns. 

Furthermore, what one institution considers a potentially risky communication might be viewed differently by another, depending on its specific risk appetite and regulatory obligations. This is especially true with conduct-related issues where, at a glance, workplace complaints can take on more significance depending on a firm’s history with culture issues.  

And if these challenges weren’t enough, GenAI vendors must also account for multilingual  

communication when developing models. The model must not only accurately translate the content but also understand idioms, cultural references, and context-specific meanings that may not have direct equivalents in other languages. 

To address these concerns, GenAI vendors must offer firms the flexibility to define their own risk boundaries in output and fine-tune their models. 

One benefit of imposing more context on outputs is the dramatic reduction in false positives and noise. By distinguishing between genuinely suspicious activity and normal business operations, context-aware AI allows compliance teams to focus their efforts on real risks rather than wading through a sea of irrelevant alerts. 

Hand in hand with noise reduction comes an improvement in the relevance of generated alerts.  

When an AI system flags a communication, you can have greater confidence that it truly warrants attention. This improved precision stems from the model’s ability to understand the context of conversations, including market-specific jargon, regional language differences, and the subtle cues that might indicate potential risks. 

Perhaps most importantly, the ability to fine-tune model sensitivity allows you to strike the right balance between comprehensive coverage and operational efficiency. This customization ensures that the monitoring system aligns perfectly with your risk taxonomy and regulatory obligations. 

Having said all that, what does context-aware model development look like? 

A layered approach to building context into models 

A best practice for developing context-aware AI models in financial communications monitoring involves a 3-layered approach. This method provides a comprehensive framework for understanding and contextualizing communications, offering a more nuanced and accurate risk detection system. 

1. The first layer involves ingesting messages, classifying, and tagging them. This layer doesn’t generate alerts; it simply tags and classifies the incoming data by looking at contextual information. 

2. The second layer aggregates the information tagged in the first layer against specific risks. For example, it might consider whether secrecy language appears alongside specific trade talk or bragging (for instance). 

3. The third layer uses GenAI to perform a comprehensive analysis. It can identify potential issues that the more targeted approaches of the first two layers might have missed. 

This 3-layered approach offers a level of flexibility and customization that’s crucial for financial firms. Unlike a one-size-fits-all model, the layered approach allows for more precise risk detection and reduced noise in alerts. 

One key benefit is the ability to provide rich context around why something is flagged as a potential risk. This detailed context helps compliance teams understand not just that a risk was detected, but why it was flagged, enabling more informed decision-making. 

The layered approach also allows for more nimble adaptation to different markets and communication styles. For instance, a model can differentiate how language is used in various contexts, such as the implications of “asking for a favor” in different markets. 

The 3-layered approach also helps firms tailor model outputs to their risk appetites. This customization allows you to adjust the thresholds for risk detection based on your unique requirements. An added benefit is that you can adapt to different regulatory environments and internal risk taxonomies without retraining models. 

Of course, much depends on the quality and relevance of the training data used. Specialized data helps the model understand context-specific risks that might not be apparent in general language models. However, for some models, such as ones focused on detecting secrecy, using open-source datasets with finance-specific prompts is the right approach. 

This balanced approach allows for the development of robust models without the need for extensive, hard-to-obtain financial datasets for every aspect of the system. It also enables the model to understand general language patterns while still being attuned to finance-specific nuances. Contextualizing risk is an ongoing process that requires continuous refinement. It requires a partnership between you and your GenAI providers to continually optimize your models based on real-world performance and evolving risk landscapes. 

Context is key to surfacing risk 

The importance of context in AI-driven communications monitoring is paramount, particularly in an industry where a single misinterpreted message could have significant regulatory or financial repercussions. 

Shield’s approach to this challenge exemplifies best practices in the field: 

• Our 3-layered approach, combining initial classification, risk-specific aggregation, and comprehensive GenAI analysis, offers a robust solution to the intricacies of financial communication monitoring. 

• Model flexibility that helps you define risk thresholds. 

• Customization to your specific needs and maintaining an ongoing refinement process. 

When paired with model flexibility and a commitment to transparency, defining context to aid model output reduces false positives and lifts your surveillance program to new heights. 

Learn how AmplifAI—Shield’s GenAI toolkit—surfaces risks and offers unmatched context in model outputs. 

The post From Noise to Knowledge: Contextualizing risk in financial communications with GenAI  appeared first on Shield: Digital communication governance and archiving solutions.