Govern AI Agents and LLMs Across Your Digital Properties

AI Agent Governance is policy-driven, entity-level control of how LLMs, AI agents, and automation interact with your properties. It addresses a structural problem: not all automation is bad, but binary “block bots” approaches cannot distinguish legitimate agents from adversarial ones.

Rather than a single allow-or-block decision, AI Agent Governance enables you to:

• Identify what entity is interacting (human, approved agent, unknown automation, adversary)
• Authenticate whether signals are credible
• Interpret intent and behavior patterns
• Apply proportional controls (allow, allow-with-limits, challenge, suppress, block) based on risk

Traditional bot blocking is typically binary and treats all non-human activity as a threat. AI Agent Governance treats automation contextually.

CHEQ evaluates three correlated signal streams, each targeting a different dimension of entity behavior, to classify interactions:

• Traffic Intelligence: Automation indicators (headless patterns, framework signatures), device and browser spoofing, network anomalies, behavioral deviation from human norms
• Trust Intelligence: Script execution behavior, consent enforcement state, data leakage signals, unapproved vendor risks
• Identity Intelligence: Identity resolution and consistency, synthetic identity indicators, alignment with known trusted patterns and workflows

Correlating these layers allows CHEQ to determine what is interacting, whether signals are authentic, and what it is attempting to do—without relying on single-signal detection or blocking all agents equally.

Yes. CHEQ enables differentiated policy, not binary controls. You can:

• Allow approved agents with full or limited access based on use case
• Allow public crawlers and search bots with rate and route limits
• Suppress data extraction while preserving content discoverability
• Block spoofed agents and unauthorized automation attempting sensitive actions
• Challenge high-risk interactions with step-up before permitting access

Policies vary by journey stage, entity type, action, and risk level. This approach reduces scraping while enabling valuable automation.

CHEQ identifies and mitigates spoofed agents through multiple layers of validation that work together to expose impersonation attempts:

• Environment integrity checks: Detect device spoofing, masked origins, credential inconsistencies, and mismatched behavioral signals
• Synthetic identity detection: Flag indicators of AI-generated or recycled identity elements during registration and high-risk actions
• Cross-session consistency: Assess identity alignment across sessions to detect coordinated fraud and misrepresentation
• Intent alignment: Evaluate whether claimed agent behavior aligns with expected patterns and declared workflows

This prevents spoofed agents from impersonating legitimate integrations or triggering sensitive actions without proper authentication.

CHEQ supports journey-specific governance with proportional enforcement tailored to each workflow’s risk profile:

• Authentication and Login: Detect credential abuse, enumeration, and automation; apply step-up for high-risk attempts
• Registration and Onboarding: Validate identity integrity; flag synthetic identity risk; require verification for suspicious submissions
• Data Access and Queries: Govern API rate limits and endpoint access; suppress sensitive data exposure for low-trust sessions; route high-risk queries for review
• Account Changes and Transactions: Require higher trust thresholds; apply step-up or block for high-risk modifications; enable legitimate agent-assisted workflows with scoped permissions

Controls are configurable by action type, entity classification, and trust level.

CHEQ’s Trust Intelligence layer controls execution and data exposure:

• Script and vendor governance: Evaluate vendor behavior; suppress unapproved scripts for low-trust sessions; enforce consent-aligned execution
• Data leakage detection: Identify and flag attempts to extract PII, pricing, inventory, or proprietary information
• Session-based suppression: Reduce tool and feature exposure for unknown automation while maintaining full functionality for verified users
• Consent routing: Route sessions based on privacy preferences and regulatory requirements

This reduces unauthorized data exposure without blocking legitimate traffic.

CHEQ is designed to minimize friction by:

• Reducing false positives through correlated signals instead of single detection rules
• Applying step-up only when risk warrants it, not blanket challenges
• Enabling selective enforcement rather than universal rules
• Preserving legitimate user experience through entity-level context and trust-based decisions
• Supporting legitimate agent workflows with scoped permissions and approval paths

Friction is applied proportionally to risk. Legitimate users and approved agents experience minimal friction while malicious actors face stronger controls.

CHEQ operates as a central trust provider with multiple integration modes:

• Real-time: API decisioning for edge, WAF, CDN, and immediate enforcement
• Identity/Authentication: Step-up orchestration, MFA triggers, and risk-based routing
• Analytics/CDP: Batch exports and event streams for clean segmentation and personalization
• Security Tools: Verdict streaming to SIEM, SOAR, and threat hunting platforms
• Data Platforms: Event streams and batch APIs for downstream activation and analysis

Both synchronous and asynchronous integration patterns are supported.

CHEQ supports continuous calibration and refinement:

• Explainable verdicts: Every decision includes signals and reasoning, enabling investigation and confidence in adjustments
• Real-time dashboards: Monitor verdict outcomes, false positive rates, and policy impact without code changes
• Event streaming: Feed verdict data into SIEM and data platforms for pattern analysis
• Flexible reconfiguration: Adjust thresholds, rules, and enforcement actions as threats and behaviors evolve
• Business logic calibration: Iteratively refine policies using customer truth data and feedback loops

Policies adapt to new agent techniques and business changes without requiring platform re-architecture.

CHEQ enables experience tailoring based on entity type and trust level, so each visitor class receives an appropriate experience:

• Humans: Deliver full personalization, targeted offers, and premium features
• Approved agents: Offer agent-specific flows and tool access; apply gentle rate limits where appropriate
• Unknown automation: Suppress sensitive tools and features; limit data exposure; require authentication for risky actions
• Adversarial actors: Block or heavily restrict

Route CHEQ trust signals to your analytics, CDP, and activation stack so audiences, recommendations, and campaigns are built on verified engagement—not automated noise. This improves data quality and campaign effectiveness.

You can allow controlled LLM access while protecting your interests:

• Approved models and partnerships: Route authorized training requests through dedicated APIs with logging
• Licensed content: Enforce licensing terms and attribution requirements through policy
• Public vs. proprietary: Distinguish between public-web content and premium or authenticated resources; apply different governance to each
• Rate and scope limits: Allow research models or content crawlers with controlled rates and endpoint restrictions
• Extraction limits: Permit structured API access while blocking bulk scraping and distillation

This allows beneficial partnerships while preventing unauthorized training and data theft.