Pillar Platform

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Discovers and enforces least-privilege access for non-human and AI-agent identities across systems and data.

Monitors AI-agent behavior at runtime to detect anomalous or malicious actions and policy violations.

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.

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

Assesses and scores the risk of discovered AI services and embedded AI features (data handling, training-use terms, hosting, vendor posture) to drive sanction/block decisions.

Detects anomalous AI usage patterns - unusual volumes, off-policy services, atypical data flows to AI endpoints - and alerts on potential misuse or exfiltration through AI channels.

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.