Pillar Platform

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.

Assesses the identities and service accounts that AI models, pipelines, and agents use, flagging over-permissioned non-human identities and access paths that violate least privilege. Reports identity risk as a posture finding, distinct from enforcing access policies at the model API at runtime.

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.

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.

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.

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.

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.

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.

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.

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.