Protecting AI Applications
Many organizations are deploying generative AI capabilities in external-facing applications - powering customer support chatbots, virtual assistants, content generation tools, and retrieval-based systems. These AI systems could expose internal logic, models, or data to external users, including customers, partners, and the public.
This shift introduces new security, privacy, and reputational risks. Organizations must ensure that external AI responses do not leak sensitive data or violate business, legal, or regulatory boundaries.
Challenges
- Public-facing AI systems may be vulnerable to prompt injection or jailbreak attacks that manipulate model behavior.
- AI systems may unintentionally expose internal or regulated content (e.g., PII, PHI, financial data) in model responses.
- Customers may prompt systems to generate disallowed or high-liability content (e.g., legal, medical, political advice). Topic restrictions and liability boundaries are often undocumented or unenforced at runtime.
- There may be no logging or policy enforcement layer between the LLM and the public interface.
Example scenarios
- A public-facing support chatbot accidentally reveals internal document content or user data when answering a query.
- A customer-facing assistant provides prohibited advice on political, legal, or medical topics in violation of business policy.
- A partner portal tool using RAG retrieves sensitive or restricted material from enterprise sources due to inadequate filtering.
How AIDR helps
AIDR enables organizations to monitor and enforce policy for AI responses exposed to external users. The same techniques used in the Employee AI Usage scenario apply, with additional focus on audience risk and topic restriction.
AIDR can help:
- Block prompt injection and jailbreak attempts that cause LLMs to behave in unsafe or unauthorized ways.
- Detect and sanitize sensitive content in model responses before delivery to external users - including PII, secrets, or internal references - using gateway sensors or application-level instrumentation.
- Apply topic-based restrictions (for example, prevent political, legal, or medical output) to reduce business and compliance risk.
- Log external AI interactions for audit, compliance, and incident investigation.
- Integrate with SIEM platforms to support alerting, response workflows, and accountability.
Deployment options
To monitor and control external AI usage, deploy AIDR in one or more of the following ways:
- Deploy a Gateway sensor (for example, Kong, F5, LiteLLM) in front of public-facing AI endpoints to inspect and control request–response traffic.
- Use application-level instrumentation via AIDR SDKs or APIs to apply policy before delivering model responses to users.
- Deploy an Agentic sensor (using the MCP Proxy) to process prompt orchestration, model responses, and autonomous agent activity that powers external-facing assistants or multi-step flows.
- Use OpenTelemetry instrumentation to forward detection logs and telemetry to AIDR from services that process AI inputs or outputs. This supports breach detection, incident response, and forensic analysis.
- Forward AIDR logs to your SIEM to support real-time alerting, investigation, and compliance reporting.
Summary
External-facing AI systems require proactive security controls to prevent data exposure, policy violations, and reputational harm. AIDR helps organizations enforce topic and content restrictions at the boundary between LLMs and external audiences - ensuring safe, compliant, and auditable use of generative AI in customer-facing environments.
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