AI Safety

Multi-Layer Guardrail System

Our comprehensive safety architecture employs a sophisticated multi-layer guardrail system that serves as the first line of defense against inappropriate, harmful, or malicious requests. Every incoming request undergoes rigorous analysis through our advanced filtering mechanisms before reaching the inference layer. This preprocessing stage evaluates multiple dimensions of safety and appropriateness to ensure that only legitimate, safe, and contextually appropriate requests proceed to model execution.

The guardrail system operates through five critical evaluation dimensions. Legal compliance assessment ensures that all requests adhere to applicable laws and regulations across jurisdictions, preventing the system from facilitating illegal activities. Request type classification helps identify the nature and intent of each query, enabling appropriate routing and response mechanisms. Business relevance evaluation determines whether requests align with intended use cases and organizational policies. Security analysis detects potential threats and vulnerabilities that could be exploited through the agent system. Safety evaluation assesses whether requests could lead to harm, whether physical, psychological, or societal.

Content Filtering and Policy Enforcement

Our content filtering mechanisms work in conjunction with inference provider policies to create multiple layers of protection against harmful content generation. When our primary guardrails identify illegal or inappropriate requests, they are immediately blocked from execution. Additionally, inference providers implement their own content policies that serve as secondary safeguards, ensuring that even if a request bypasses our initial filters, it will still be subject to provider-level safety measures.

Ethical, safety, and social responsibility standards are embedded throughout our decision-making processes. Requests that violate common ethical principles, pose safety risks, or contradict accepted social norms are systematically refused. This includes content that could promote violence, discrimination, misinformation, or other harmful behaviors. Our ethical framework is regularly updated to reflect evolving societal standards and emerging safety concerns.

Contextual Appropriateness and User Guidance

The system maintains strict boundaries regarding its capabilities and intended use cases. When users submit requests that fall outside the agent's scope of abilities or contextual appropriateness, the system provides clear explanations and offers constructive alternatives. Rather than simply refusing requests, our approach includes suggesting similar queries that align with the agent's capabilities and safety parameters. This user-friendly approach maintains safety standards while providing helpful guidance toward productive interactions.

Advanced Threat Detection

Our security infrastructure incorporates sophisticated threat detection mechanisms designed to identify and neutralize various attack vectors targeting AI systems. Advanced pattern recognition algorithms detect prompt injection attacks, where malicious users attempt to override system instructions or extract sensitive information. The system also identifies other LLM-specific attacks, including attempts to manipulate model behavior through adversarial prompts or social engineering techniques.

Traditional cybersecurity threats are also actively monitored and blocked, including SQL injection attempts, remote code execution exploits, and other common attack patterns. Our threat detection systems continuously evolve to address emerging attack methodologies, ensuring robust protection against both known and novel security threats.

Terms of Service Compliance

All interactions are continuously monitored for compliance with organizational terms of service and acceptable use policies. Automated policy enforcement mechanisms ensure that requests violating service terms are immediately identified and refused. This includes monitoring for abuse patterns, commercial misuse, intellectual property violations, and other policy infractions that could compromise service integrity or user safety.

Data Integrity and Attribution Systems

To combat hallucination and ensure information accuracy, our integrity framework implements comprehensive citation and attribution mechanisms. Every piece of internal data retrieved by the system is tagged with attribution tokens that trace back to original sources. This citation system operates transparently, allowing users to verify the provenance of information and assess its reliability.

When agents synthesize data from multiple sources, they emit attribution tokens that clearly indicate the origin of each piece of information. This granular attribution system enables users to understand exactly where conclusions are drawn from and evaluate the credibility of underlying sources. External research queries are similarly tagged with URL sources and citation metadata, ensuring full transparency in information sourcing.

Authentication and Access Control Security

Agent access to organizational data is secured through a locked-down authentication system that operates on strict principle-of-least-privilege access. Each agent instance is provided with a carefully curated and restricted subset of tools and data access capabilities that align precisely with its intended function and user context. This sandboxed environment ensures that agents cannot access data beyond their authorized scope, regardless of the complexity or sophistication of potential attack vectors.

The authentication architecture is designed to be immune to prompt injection attacks that attempt to escalate privileges or impersonate other users or organizations. No prompt injection technique can enable an agent to authenticate as a different user or gain access to unauthorized data sets. The system maintains strict identity verification and session management protocols that cannot be bypassed through conversational manipulation or adversarial prompts.

Arbitrary data queries are fundamentally impossible to execute within our security framework. The system employs query validation and access pattern analysis to ensure that all data requests conform to predefined schemas and authorized access patterns. This prevents agents from conducting exploratory data mining or accessing sensitive information through indirect query methods, even if such requests are cleverly disguised within legitimate-seeming interactions.

Privacy and Data Protection

Privacy protection is integral to our safety framework, with comprehensive measures to prevent unauthorized data access or exposure. Personal information handling follows strict privacy policies and regulatory requirements, while data minimization principles ensure that only necessary information is collected and processed.

Secure data transmission and storage protocols protect user information throughout the interaction lifecycle, and automated data purging mechanisms ensure that sensitive information is appropriately deleted according to retention policies. Access controls and audit trails provide additional layers of protection for user data and system integrity.