Lasso AI Security Platform

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.

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

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.

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.

Discovers MCP servers and AI agent integrations in use, routes agent tool calls through a governed gateway or proxy, and enforces access and data policies on agent-to-tool traffic, extending AI usage control from human prompts to autonomous agent workflows.

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.

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

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.

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

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

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.

Secures AI coding assistants and their Model Context Protocol connections against unsafe actions, data exposure and supply-chain risks.

Baselines how a deployed AI agent normally reasons, calls tools and chains actions, then flags or blocks behavioral anomalies such as agent drift, intent manipulation and tool-abuse sequences, at the low latency and low false-positive rate that inline agent traffic tolerates.

Discovers, governs and allowlists the Model Context Protocol servers and tools that AI agents are permitted to invoke, and enforces the connection controls the protocol does not provide by default: transport security, OAuth-based agent authentication, scoped short-lived tokens, and gateway or proxy mediation of external MCP traffic.

Discovers the AI models, agents, MCP servers and AI-enabled applications in use across the organization, including those deployed without IT authorization, and keeps them in an inventory that records type, ownership and criticality. Discovery runs through traffic inspection and code repository scanning at minimum; some products add cloud provider, application server or AI engineering tool integrations. The inventory anchors exposure management and risk scoring.

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

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

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.

Scans the configuration of AI model and inference endpoints, and of the MCP servers and tool endpoints that agents call, and flags public exposure, weak or missing authentication, default credentials and excessive permissions as posture findings. Findings come from configuration and passive scanning rather than inline traffic inspection, which belongs to runtime defense. Products differ in the endpoint types covered and in whether findings carry a severity score.

Maps AI posture findings and the controls in place to AI security and regulatory frameworks such as the EU AI Act, NIST AI RMF and ISO/IEC 42001, and reports coverage and open gaps per framework for audits, regulators and leadership. The reporting draws on the AI inventory and its findings; approval workflows, attestations and evidence management belong to AI governance platforms.

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.

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 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.

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.

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.

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.

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

Attacks deployed guardrails, system prompts and content filters to measure how reliably they block adversarial inputs, quantifying bypass rates rather than assuming the controls work, and feeds the results back so runtime guardrails can be tuned to the exposures the assessment found.