Accelerated Innovation

Mitigating Sensitive Information Disclosure Risks in Your GenAI Systems

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Mitigating Sensitive Information Disclosure Risks in Your GenAI Systems

Description

This capability focuses on identifying, preventing, and remediating the risk of GenAI systems unintentionally exposing sensitive, confidential, or proprietary information. It includes implementing controls to safeguard internal knowledge, personal data, regulated content, and context-specific business inputs across GenAI inputs and outputs.

Why it's Important

Sensitive information disclosures-whether accidental or adversarial-pose serious risks to enterprises deploying GenAI. These risks can result in regulatory violations, reputational damage, loss of IP, or exposure of customer data. Since GenAI models are probabilistic and can surface memorized or inferred information, traditional access controls and data masking are often insufficient. Proactively addressing disclosure risk is essential for maintaining trust, meeting compliance standards, and enabling safe, scalable GenAI usage across functions.

Why it's Challenging @ Scale

  • Difficult to define “sensitive” consistently: What qualifies as sensitive varies across teams, regions, and use cases-making automated enforcement challenging.
  • LLMs are unpredictable by design: Even without training on restricted content, models can generate sensitive information based on inference or memorization.
  • Redaction and masking gaps: Data preprocessing often misses edge cases, especially when sensitive details are embedded in unstructured or context-rich formats.
  • Limited GenAI-specific DLP tools: Traditional data loss prevention systems are not optimized to monitor and intercept sensitive content in GenAI-generated outputs.
  • Feedback loops are immature: Without scalable review workflows, it’s hard to detect and correct disclosure risks as GenAI usage expands.

Complexity

Extremely High: Addressing disclosure risks requires deep alignment across security, legal, and technical teams, as well as new capabilities in detection, response, and GenAI-specific tooling.

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Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

Select your group’s current maturity, based on your assessment results, and act today.
  • Explore Key Concepts & Best Practices: Complete the Securing Your GenAI Solution workshop (2 hrs.) to understand foundational key concepts and explore applied best practices.
  • Introducing GenAI Threat Models and Security Posture
  • Understanding Attack Surfaces in GenAI Workflows
  • Establishing Basic Security Principles for LLMs
  • Identifying Security Stakeholders and Roles
  • Aligning Security with Compliance Requirements
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy.
  • Align on your Current State and define your Target State
  • Create an actionable enablement plan
  • Define target timeline and measures of success
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame.
  • Disclosure Testing for Internal GenAI Tools: Identify 1-2 high-usage GenAI workflows and test for unintentional sensitive information exposure.
  • Launch Prompt Filtering Checkpoints: Build lightweight filters or guidelines to prevent high-risk prompts from triggering sensitive outputs.
  • Create a Disclosure Risk Reference Sheet: Publish a short guide listing types of sensitive information commonly mishandled by GenAI tools.
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including:
  • A Deep Dive into GenAi Solution Threat Modeling
  • A Deep Dive into Enterprise Access Control for GenAI Solutions
  • A Deep Dive into Preventing Prompt Injection Attacks
  • A Deep Dive into Preventing Insecure Output Handling
  • A Deep Dive into Preventing Data Poisoning
  • A Deep Dive into Preventing Denial of Service
  • A Deep Dive into Preventing GenAI Supply Chain Risks
  • A Deep Dive into Preventing Sensitive Information Disclosure
  • A Deep Dive into Preventing Insecure GenAI Solution Plugins
  • A Deep Dive into Preventing Excessive LLM Agency
  • A Deep Dive into Preventing LLM Overreliance
  • A Deep Dive into Preventing GenAI Model Theft
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale
  • Assess Your Proposed Solution or Process: Review where and how sensitive information has surfaced in prior GenAI testing, and validate whether mitigations have been applied.
  • Define in-scope Processes and Guardrails: Outline specific GenAI workflows that require disclosure controls and define thresholds for allowable outputs.
  • Close any Data or Measurement Gaps: Build metrics to track disclosure frequency, prompt sensitivity, and response suppression rates.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units
  • Define Your Phased Implementation Plan: Prioritize GenAI use cases with high disclosure risk and define rollout stages based on business criticality.
  • Build Awareness and Finalize Enablers: Share red flag examples, pattern libraries, and mitigation strategies with developers and business stakeholders.
  • Operationalize Your Comms Plan: Create clear documentation and internal messaging around disclosure policies, escalation paths, and risk thresholds.
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Publish Disclosure Handling Guidelines: Codify tested practices for preventing sensitive information exposure across prompts, responses, and post-processing.
  • Build a Disclosure Detection Checklist: Create a repeatable checklist teams can use to evaluate risk before GenAI tools are released or updated.
  • Integrate Disclosure Audits into QA: Ensure model outputs are systematically reviewed for sensitive content as part of quality assurance cycles.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Expand Risk Controls Across Journeys: Extend disclosure protection measures to customer-facing flows, internal tools, and partner applications.
  • Equip Teams with Prompt Hygiene Tools: Offer templates, classifiers, or pre-prompts that flag or reshape sensitive input before it reaches the model.
  • Run Cross-Functional Risk Workshops: Facilitate sessions to align engineering, legal, and compliance teams on high-risk scenarios and thresholds.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Spotlight Risk Mitigation Success Stories: Showcase examples where GenAI tools avoided sensitive disclosures due to implemented safeguards.
  • Share Before-and-After Case Studies: Compare risky model behavior before and after disclosure protections were applied.
  • Recognize Contributors to Risk Reduction: Highlight teams and individuals who played a key role in enhancing safety and compliance.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Embed Disclosure Scanning into Authoring Tools: Integrate real-time alerting or tagging for potential sensitive content as GenAI prompts and outputs are created.
  • Provide In-Flow Disclosure Feedback: Surface risk scores, suppression triggers, or escalation guidance directly in GenAI user interfaces.
  • Harmonize Policies Across Channels: Ensure consistent enforcement of disclosure risk management in voice, chat, email, and document-based GenAI deployments.
  • Leverage Automation: Use GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Automate Sensitive Output Detection: Use classifiers and audit scripts to automatically flag risky language, entity references, or inferred data.
  • Suggest Edits Based on Disclosure Patterns: Enable tools that revise or annotate outputs when they resemble known sensitive formats.
  • Train Models on Disclosure-Aware Prompts: Fine-tune model behavior to avoid or deflect prompts likely to elicit confidential or regulated information.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Refresh Guidelines Based on Incident Trends: Regularly update disclosure controls based on newly observed risks and organizational learning.
  • Extend Coverage to Multimodal GenAI Tools: Apply disclosure safeguards across visual, audio, and hybrid GenAI outputs.
  • Benchmark Disclosure Risk Management: Measure your organization’s handling of sensitive information against peers, standards, and industry benchmarks.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Treating all data equally: Failing to define “sensitive” clearly can lead to under-blocking (real risks slip through) or over-blocking (value is lost).
  • Over-relying on manual review: Disclosure risks scale faster than human review capacity-automation is essential.
  • Assuming anonymization is enough: Masked data may still be re-identified by GenAI systems trained to infer context.
  • Neglecting downstream disclosures: Even safe prompts can generate unsafe outputs depending on model behavior and post-processing.
  • Leaving legal and compliance teams out: Disclosure definitions and thresholds must align with evolving legal and regulatory requirements.

Targeted Benefits

While Mitigating Sensitive Information Disclosure Risks in Your GenAI Systems can be challenging, its benefits are clear and compelling, including:

  • Reduced regulatory and reputational risk: Stronger safeguards lower the chance of privacy violations or compliance breaches.
  • More trusted GenAI outputs: Users and stakeholders are more likely to adopt systems they believe are secure by design.
  • Faster scaling with confidence: Teams can expand GenAI use knowing disclosure risks are actively monitored and managed.
  • Improved cross-functional alignment: Aligning business, legal, and technical teams around risk priorities builds shared ownership.
  • Clearer audit trails and accountability: Proactive disclosure controls make it easier to document safeguards and prove due diligence.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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