AI Data Leakage Prevention
Stop Sensitive Data From
Leaking Through Your AI
Every AI response is a potential data leak. FirewaLLM scans LLM outputs in real time to detect and redact PII, secrets, proprietary information, and regulated data before it ever reaches the end user.
THE CHALLENGE
Your AI Is a
Data Leakage Vector
Large language models do not understand confidentiality. They will surface training data, retrieved documents, system configuration, and user information in responses whenever the context allows it. Without output-level inspection, every AI application becomes an uncontrolled data exfiltration channel.
PII Exposure in Model Responses
LLMs trained on or given access to personal data can reproduce names, emails, phone numbers, medical records, and financial details in responses. A single unredacted output can violate GDPR, HIPAA, or CCPA and expose your organization to regulatory enforcement and reputational damage.
Secret and Credential Leakage
API keys, database passwords, authentication tokens, and private keys embedded in system prompts, RAG documents, or tool configurations can be extracted by adversarial users or accidentally surfaced in model explanations. Leaked credentials enable lateral attacks across your infrastructure.
Proprietary Knowledge Disclosure
AI applications connected to internal knowledge bases, codebases, or business documents can reveal trade secrets, unreleased product details, internal strategies, and confidential communications when users ask the right questions -- or the wrong ones.
THE SOLUTION
Intelligent Output Scanning
By FirewaLLM
FirewaLLM inspects every AI response at the output layer using a combination of pattern recognition, entity classification, and semantic analysis. Sensitive data is automatically redacted, masked, or blocked according to your policies -- with full audit trails for compliance.
Real-Time PII Detection
Identifies 40+ categories of personally identifiable information across all locales and formats in both user inputs and model outputs, including names, addresses, identification numbers, financial data, and healthcare information.
Secret and Credential Scanning
Detects API keys, tokens, passwords, private keys, connection strings, and other authentication secrets using pattern libraries covering 200+ secret formats from major cloud providers, databases, and SaaS platforms.
Custom Entity Definitions
Define organization-specific sensitive data patterns using regex, keyword dictionaries, or natural-language descriptions. Protect proprietary terms, internal identifiers, and business-specific confidential categories alongside built-in detection.
RAG Output Sanitization
Inspects retrieval-augmented generation outputs to ensure retrieved documents containing restricted content do not leak through model responses, even when the model paraphrases, summarizes, or indirectly references the sensitive material.
Compliance Audit Logging
Every detection event is logged with full context including the data category, redaction action taken, source application, and timestamp. Export audit reports formatted for GDPR, HIPAA, SOC 2, and PCI DSS compliance reviews.
Adaptive Redaction Policies
Configure redaction behavior per data type, user role, and application context. Choose between full redaction, partial masking, replacement with synthetic data, or blocking the entire response based on sensitivity level and use case.
WHY FIREWALLM
Built for real-world AI security.
Automatically redact PII from AI responses before they reach users
Detect and block API keys, passwords, and credentials in LLM outputs
Protect proprietary knowledge from unauthorized disclosure through AI
Sanitize RAG pipeline outputs to prevent document-level data leaks
Meet GDPR, HIPAA, SOC 2, and PCI DSS requirements for AI data handling
Define custom sensitive data categories specific to your organization
Maintain complete audit logs of every detection and redaction event
Deploy in minutes with zero changes to your existing AI infrastructure
AI Data Leakage Prevention FAQ
How do AI applications leak sensitive data?+
AI applications leak data through multiple vectors: LLMs may reproduce training data fragments containing PII or proprietary information; RAG systems may retrieve and surface confidential documents in responses; chatbots may be manipulated into disclosing system prompts, API keys, or internal configuration; and AI agents may inadvertently pass sensitive data to external tools or APIs during task execution.
What types of sensitive data does FirewaLLM detect?+
FirewaLLM detects over 40 categories of sensitive data including personal identifiers (names, emails, phone numbers, SSNs, passport numbers), financial information (credit card numbers, bank accounts, tax IDs), authentication secrets (API keys, tokens, passwords, private keys), healthcare data (medical record numbers, diagnoses), and custom patterns you define for business-specific confidential information.
How is AI DLP different from traditional data loss prevention?+
Traditional DLP scans structured data flows like email and file transfers using static rules. AI DLP must handle unstructured natural-language outputs where sensitive data can appear in unpredictable formats, be paraphrased rather than copied verbatim, or be revealed through inference rather than direct disclosure. FirewaLLM uses semantic analysis alongside pattern matching to catch both explicit and contextual data leakage.
Can FirewaLLM prevent data leaks in RAG applications?+
Yes. FirewaLLM inspects both the retrieved context and the final model output in RAG pipelines. It identifies when retrieved documents contain sensitive data that should not be exposed to the current user, and it sanitizes model responses that attempt to surface restricted information -- even when the model paraphrases or summarizes the sensitive content rather than quoting it directly.
Does FirewaLLM support compliance with GDPR and HIPAA?+
FirewaLLM helps organizations meet data protection requirements under GDPR, HIPAA, SOC 2, PCI DSS, and other frameworks by preventing unauthorized disclosure of regulated data through AI channels. It provides configurable redaction policies, complete audit logs of all detected and blocked data, and exportable compliance reports that document your AI data protection controls.
Can I define custom sensitive data patterns for my organization?+
Absolutely. FirewaLLM supports custom entity definitions using regex patterns, keyword lists, and semantic descriptions. You can define organization-specific sensitive data categories such as internal project codenames, proprietary formulas, unreleased product details, or customer account identifiers, and FirewaLLM will detect and redact them alongside built-in data types.
Seal Every Data Leak
In Your AI Pipeline
Sensitive data should never leave your AI without your permission. Deploy FirewaLLM to scan, redact, and audit every LLM response in real time.