Lasso AI Security Platform

Inspects prompts, uploads, and AI-generated responses for sensitive data across modalities, preventing exposure of regulated or proprietary information to third-party AI services.

Discovers and categorizes the organization's use of third-party AI, whether consumed as a service, installed locally, or embedded inside other applications, building a continuously updated inventory of AI usage including shadow AI.

Enforces AI usage controls through multiple local inspection points - browser, endpoint, and network - coordinated from a cloud-delivered control plane, so coverage does not depend on a single interception path.

Defines organizational AI usage policies and enforces them at the point of use - allowing, blocking, redirecting, or constraining specific AI services, models, and features per user, group, or data context.

Records prompts, completions, and metadata for all AI interactions with tamper-resistant storage, supporting compliance, forensics, and policy investigation.

Detects and blocks adversarial inputs designed to override system prompts, extract training data, or redirect model behavior. Detection approaches include pattern matching, input semantic analysis, and secondary model classification.

Enforces IAM-style policies on LLM API access, controlling which users and applications can invoke which models and data sources, with audit logging.

Evaluates model outputs against content policy, data classification rules, and format expectations before delivery to end users, blocking responses containing sensitive data or policy violations.

Intercepts prompts and completions to prevent sensitive data (PII, credentials, internal IP), from being transmitted to external LLM services or returned in model responses.

Continuously stress-tests the product's own guardrails and filters against jailbreaks, prompt-injection payloads, and data-extraction attempts, then re-tightens policies after model or prompt changes. A self-validation loop within the runtime protection layer, distinct from the standalone AI Red Teaming discipline that tests AI systems end to end.

Autonomously plans and executes multi-step adversarial campaigns against AI systems, emulating real attacker workflows across reconnaissance, exploitation, and escalation rather than running a fixed checklist of tests.

Discovers AI assets, including shadow models, agents, and inference endpoints, and maps the reachable attack surface to scope and target red-team campaigns. Offensive reconnaissance, distinct from posture inventory.

Tests LLMs and AI applications against a library of direct and indirect prompt-injection and jailbreak techniques, reporting which payloads bypass system instructions and safety controls.

Re-runs red-team campaigns continuously and at release gates in the CI/CD pipeline as models, prompts, and configurations change, catching new exploit paths before and after deployment.

Tests AI agents and their tool chains for context-poisoning, tool-misuse and indirect prompt-injection vulnerabilities.

Reports validated AI vulnerabilities with reproduction evidence, attacker context, and remediation guidance, mapped to the OWASP LLM Top 10, MITRE ATLAS, EU AI Act, and NIST AI RMF for auditable AI risk reporting.

Attacks AI agents through their tools, memory, and connected services using multi-step techniques such as tool misuse, goal hijacking, and indirect injection, surfacing exploit paths unique to autonomous agents.

Maps data lineage and provenance across AI training and inference pipelines, tracing how PII, PHI, and IP move into models and external services.

Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.

Scores deployed AI models by risk level based on data sensitivity processed, deployment scope, capability classification, and applicable regulatory requirements.

Discovers AI model and inference endpoints and flags public exposure, weak authentication, default credentials, or excessive permissions as posture misconfigurations.

Detects sensitive or regulated data in AI training, fine-tuning, or third-party LLM flows without appropriate controls, such as unencrypted PII in inputs or PHI sent to external APIs.