Companies that trust BrownPipe
Artificial Intelligence
Protect your organization from emerging AI risks, from prompt injection to training data leakage.
Companies that trust BrownPipe
Context
Integrating AI models into products and services boosts efficiency and reach, but introduces risks that did not exist in traditional applications.
Technologies that operate with greater autonomy behave in unexpected ways and create specific attack surfaces:
Attackers manipulate the model into performing unauthorized actions or disclosing sensitive information.
Models may expose confidential data used during training or embedded in system instructions.
Manipulation of training data to compromise the model's behavior.
Outputs generated without adequate auditability or explainability.
Models reproduce and scale biases present in training data.
These risks require a specific security approach, different from what applies to traditional systems.
Services
We offer two complementary services for organizations that use or develop AI-powered solutions:
We analyze the integration flows between your services and products and AI models. We identify vulnerabilities specific to this context and test the security of these integrations.
AI-specific attack surface mapping
Prompt injection and context manipulation testing
Data leakage assessment via model
Access control and permissions analysis
Decision traceability and logging verification
Custom threat modeling for each integration
Threat model specific to your AI solution
Vulnerability report with proof-of-concept demonstrations
Prioritized mitigation recommendations
Comprehensive view of risks and defense strategies
We assess personal data processing throughout the entire lifecycle of your AI solution, from model training to production use.
Personal data of many categories is processed in AI solutions. When data protection requirements are not addressed during the planning and development phases, there is a risk of losing investments already made. If personal data is used without a proper legal basis for training, the outcome may be compromised and violate the LGPD.
Mapping of personal data processing in model training and usage
Analysis of applicable legal bases for processing
Assessment of existing data protection policies
Contract and service provider relationship analysis (AI providers)
Identification of risks to which personal data is exposed
Verification of transparency and explainability requirements
AI-specific LGPD compliance assessment
Gap and risk mapping
Remediation plan with prioritized measures
Recommendations based on industry best practices
Scenarios
You are integrating or developing solutions with AI models (LLMs, ML, etc.)
You use third-party APIs for AI capabilities (OpenAI, Anthropic, Google, etc.)
You train proprietary models with customer or user data
You need to demonstrate regulatory compliance for AI solutions
You want to understand specific risks before deploying a solution to production
You experienced an incident or suspect a vulnerability in an AI integration
Results
Identification and treatment of AI-specific threats before they are exploited.
Alignment with LGPD and preparation for future AI regulations.
Avoid rework and future costs due to regulatory non-compliance.
Leverage the benefits of AI with confidence and control.
Common questions
It is an attack technique where the user manipulates inputs to make the AI model perform unintended actions or disclose information it should protect, such as system instructions, other users' data, or confidential training information.
Yes. The provider's API security does not guarantee the security of your integration. The way you send data, build prompts, handle responses, and control access creates attack surfaces specific to your context.
The LGPD applies to personal data processing in any context, including AI. This covers data used for model training, data processed during use, and data generated as output. A valid legal basis is required, data subjects' rights must be ensured, and adequate security measures must be implemented.
In addition to violating the LGPD and being subject to sanctions, you may have to discard the trained model or remediate in costly ways. It is cheaper and safer to assess compliance before training.
We assess integrations with LLMs (ChatGPT, Claude, Gemini, etc.), machine learning models, recommendation systems, chatbots, virtual assistants, and other applications that use AI. The scope is defined according to your needs.
It depends on the complexity of the solution. Assessments focused on a specific integration take 1 to 2 weeks. Comprehensive analyses of multiple integrations and full LGPD compliance can take 3 to 4 weeks.
No, it complements it. AI pentesting focuses on vulnerabilities specific to models and integrations. If your application has web components, APIs, or infrastructure beyond AI, those elements should be assessed with traditional pentesting.
Artificial Intelligence brings opportunities, but also specific risks that require a specialized approach. Assess your integrations before vulnerabilities are exploited or regulatory gaps lead to avoidable costs.
Get in touch