On November 12, 2025, a major financial technology vendor, SitusAMC, known for providing core services to over 1,500 banks, mortgage lenders, state agencies, and pension funds, suffered a targeted cyberattack. Hackers exfiltrated huge volumes of customer data, including critical accounting records and legal agreements for top-tier institutions like JPMorgan Chase, Citigroup, and Morgan Stanley.  

While the attack remains under FBI investigation, it exposes the new reality we’re living in today: traditional security architectures are structurally incapable of protecting the hyper-connected, AI-powered data environments that define modern financial services. 

The SitusAMC breach isn’t just another breach. It’s signaling that hard conversations need to be had now about the security blind spots that exist in data across the industry and that are at risk because of the introduction of AI.  

The Blind Spots of Legacy Data Security 

In the SitusAMC breach, attackers bypassed existing controls not by deploying ransomware or destructive malware, but by silently siphoning data from core, existing, and fully functional systems. This is a profoundly important point.  

Financial data environments have dramatically changed in the last 10 years, especially in how information moves between banks, providers, SaaS platforms, and compliance systems at a blistering pace. According to the 2025 Verizon Data Breach Investigations Report, 30% of all breaches now involve third-party vendors. That’s double the rate from just one year ago. The financial sector, despite spending billions annually on cybersecurity, remains acutely vulnerable to breaches that exploit the seams between organizations rather than their perimeters. 

Information flows continuously between banks, vendors, SaaS platforms, compliance systems, and increasingly, AI-powered automation tools, often at speeds that outpace human oversight. Legacy DLP tools and perimeter defenses were not designed to detect and control these nuanced, multi-hop data movements, let alone the AI-driven automation increasingly present in modern workflows. 

Why Contextual, Entity-Aware Security Matters 

Our perspective is clear: Without visibility into which entities (customers, partners, human users, and AI agents) are accessing or transforming sensitive records, financial institutions and their vendors are flying blind. Incidents like the SitusAMC breach often expose not just regulated information, but entire webs of third-party relationships, downstream systems, and hidden data flows. 

Where legacy security fails: 

  • System-centric rather than entity-centric: Traditional tools focus on protecting infrastructure, not understanding the business context or real identities behind data access. The IBM 2025 Cost of a Data Breach Report found that 97% of organizations that experienced AI-related breaches lacked proper access controls, exposing the fundamental disconnect between security tooling and actual data governance. 
  • Alert fatigue and enforcement paralysis: Security teams are overwhelmed. According to IBM, organizations using extensive AI and automation in security operations save $1.9 million per breach and reduce detection time by 80 days, but only 17% of companies have implemented technical controls that can actually block unauthorized data uploads to AI platforms. The rest rely on training, policies, and "warning emails" that provide no actual protection. 
  • No AI governance guardrails: As financial organizations adopt platforms like Microsoft Copilot and custom LLMs for operations and compliance automation, enterprise security teams are concerned about AI tools potentially exposing sensitive information. Shadow AI, employees using AI tools without IT approval, now accounts for 20% of all breaches and adds an average of $670,000 to breach costs. 

AI Data Security for Financial Services 

This incident demonstrates the importance of implementing data security programs and processes that were built with AI in mind, particularly because protecting unstructured data, across email, SaaS platforms, collaboration tools, and especially AI systems, requires intelligent, real-time, and entity-aware controls. 

What is needed?  

  • Unified, multi-channel visibility: Prevents blind spots by monitoring data in motion, at rest, and in use, regardless of the application or cloud. 
  • Contextual, high-precision detection: Distinguishes between generic and customer-specific content, reducing noise and preventing false positives. 
  • AI-ready governance: Provides explainable, granular controls for both AI consumption and AI development, including Copilot, custom LLMs, and third-party SaaS integrations. 
  • Rapid deployment, minimal friction: Integrates quickly into existing stacks, giving financial organizations value in days, not months. 

For institutions handling sensitive financial, legal, and customer data, a single weak link, such as an under-protected vendor or an unsanctioned AI workflow, can trigger industry-wide exposure. Modern adaptive content security is no longer optional; it’s the foundation for resilience, regulatory compliance, and customer trust. 

TL;DR 

The proliferation of sophisticated breaches shows that multi-channel, context-aware, and AI-aligned data security is now a baseline requirement, not a future aspiration. Bonfy.AI is ready to help financial organizations move beyond reactive, fragmented controls and into a future defined by proactive protection, operational agility, and sustainable innovation. 

For financial institutions rethinking their data protection strategy after the latest breach, the time to act is now. The alarm bell has already sounded.  

 

Learn how Bonfy provides the unified, precise, and adaptive security needed for today’s digital finance environment. Request a demo today.