Aim Security

Analyzes user inputs and LLM prompts to identify and block prompt injection attacks that attempt to manipulate model behavior or bypass safety guidelines. Uses pattern recognition and behavioral analysis to detect malicious prompt crafting techniques.

Monitors and prevents sensitive data (PII, trade secrets, credentials) from being sent to external LLM providers or exposed in model outputs. Applies context-aware rules specific to GenAI workflows rather than generic DLP patterns.

Provides centralized management and monitoring across multiple LLM providers (OpenAI, Anthropic, Google, etc.) with unified policies, controls, and visibility. Enables organizations to use multiple models while maintaining consistent security and governance.

Manages granular access control for LLM usage at the user and application level, including role-based access, team-based restrictions, and per-application model permissions. Enables fine-grained governance of who can use which models.

Tracks and analyzes LLM usage patterns and associated costs across the organization, providing visibility into spending by team, application, and model. Helps optimize resource allocation and identify cost anomalies.

Identifies and blocks known and novel jailbreak techniques that attempt to circumvent model safety guidelines or restrictions. Detects patterns like role-playing exploits, hypothetical scenarios, and instruction override attempts.

Captures and logs all LLM interactions including prompts, responses, user identity, timestamps, and model metadata. Provides comprehensive audit trails for compliance and forensic analysis.

Enforces zero-trust security policies at the API gateway level, controlling which LLM providers can be accessed, validating all requests, and preventing unauthorized data flows to external AI services. Implements identity-based access control for LLM integrations.

Automatically generates compliance documentation and audit reports for regulated industries by aggregating LLM usage data, security controls, and policy adherence. Streamlines evidence collection for compliance audits and certifications.

Continuously monitors LLM outputs for unexpected behavior changes, hallucinations, or deviations from expected patterns. Detects model drift, poisoning attempts, or quality degradation in real-time.

Defines, enforces, and manages organization-wide policies for GenAI usage including acceptable use policies, data handling rules, and model selection guidelines. Provides centralized governance for AI tool adoption.

Automatically identifies and classifies sensitive data types (PII, PHI, trade secrets, credentials) within prompts and responses, then applies appropriate handling rules. Uses pattern recognition and contextual analysis to tag data sensitivity levels.

Generates real-time alerts for detected security threats, policy violations, and compliance issues related to LLM usage. Provides immediate notification to security teams for rapid incident response.