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Applies context-aware rules specific to GenAI workflows rather than generic DLP patterns.","intents":["I want to ensure confidential data never reaches external AI providers like OpenAI or Anthropic","I need to prevent employees from accidentally leaking PII or proprietary information through LLM queries","I want to redact sensitive data from model responses before they reach end users"],"best_for":["Regulated enterprises (finance, healthcare, legal)","Organizations with strict data residency requirements","Companies handling customer PII or trade secrets"],"limitations":["Cannot detect all forms of obfuscated or encoded sensitive data","May require custom rules for industry-specific data types","Performance impact on high-volume LLM inference"],"requires":["Integration with LLM API gateways","Configured data classification rules","Visibility into both request and response payloads","Policy definitions for acceptable data types"],"input_types":["text prompts","API payloads","model responses","user queries"],"output_types":["redacted content","block decisions","data exposure alerts","audit logs"],"categories":["security","compliance","data-protection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aim-security__cap_10","uri":"capability://management.multi.model.provider.management","name":"multi-model-provider-management","description":"Provides centralized management and monitoring across multiple LLM providers (OpenAI, Anthropic, Google, etc.) with unified policies, controls, and visibility. 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Enables fine-grained governance of who can use which models.","intents":["I want to restrict LLM access to specific teams or departments","I need to control which applications can use which models","I want to enforce different policies for different user roles"],"best_for":["Large enterprises with complex organizational structures","Organizations with sensitive use cases requiring strict access control","Companies needing role-based governance"],"limitations":["Requires integration with identity management systems","May create friction if access is too restricted","Needs regular maintenance as teams and roles change"],"requires":["Identity and access management integration","Role and permission definitions","Application registration and management","Access control enforcement at API level"],"input_types":["user identity","application identity","role definitions","access requests"],"output_types":["access decisions","access logs","permission reports","access violation alerts"],"categories":["access-control","governance","security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aim-security__cap_12","uri":"capability://analytics.cost.and.usage.analytics","name":"cost-and-usage-analytics","description":"Tracks and analyzes LLM usage patterns and associated costs across the organization, providing visibility into spending by team, application, and model. Helps optimize resource allocation and identify cost anomalies.","intents":["I want to understand how much we're spending on LLM APIs","I need to allocate costs to different teams or projects","I want to identify opportunities to optimize spending"],"best_for":["Finance and operations teams","Organizations scaling LLM usage","Companies with cost optimization initiatives"],"limitations":["Accuracy depends on provider billing data","May not capture all indirect costs","Pricing models vary significantly between providers"],"requires":["Integration with LLM provider billing APIs","Usage tracking and metering","Cost allocation rules","Analytics and reporting infrastructure"],"input_types":["usage metrics","billing data","user/application metadata","model information"],"output_types":["cost reports","usage analytics","cost anomaly alerts","optimization recommendations"],"categories":["analytics","cost-management","reporting"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aim-security__cap_2","uri":"capability://security.jailbreak.attempt.detection","name":"jailbreak-attempt-detection","description":"Identifies and blocks known and novel jailbreak techniques that attempt to circumvent model safety guidelines or restrictions. Detects patterns like role-playing exploits, hypothetical scenarios, and instruction override attempts.","intents":["I want to prevent users from bypassing safety guidelines through creative prompting","I need to detect when someone is trying to get the model to produce harmful content","I want to maintain consistent model behavior across all users and use cases"],"best_for":["Organizations with public-facing GenAI applications","Companies concerned about misuse of internal AI tools","Enterprises needing consistent safety posture"],"limitations":["New jailbreak techniques emerge faster than detection rules can be updated","May block legitimate advanced use cases","Requires continuous model updates to stay effective"],"requires":["Real-time prompt analysis","Updated jailbreak pattern database","Integration with LLM inference pipeline","Feedback loop for new attack patterns"],"input_types":["text prompts","conversation history","user queries"],"output_types":["jailbreak risk score","block/allow decision","technique classification","security alerts"],"categories":["security","compliance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aim-security__cap_3","uri":"capability://compliance.llm.usage.audit.logging","name":"llm-usage-audit-logging","description":"Captures and logs all LLM interactions including prompts, responses, user identity, timestamps, and model metadata. Provides comprehensive audit trails for compliance and forensic analysis.","intents":["I need to maintain audit logs for regulatory compliance (SOC 2, HIPAA, GDPR)","I want to track who used which AI models and for what purposes","I need to investigate security incidents or policy violations involving LLMs"],"best_for":["Regulated industries (finance, healthcare, legal)","Organizations with compliance requirements","Enterprises needing forensic capabilities"],"limitations":["High volume of logs can create storage and performance overhead","Sensitive data in logs requires careful handling and encryption","Log retention policies may conflict with data minimization requirements"],"requires":["Integration with all LLM API endpoints","Secure log storage infrastructure","Log retention and archival policies","Timestamp synchronization across systems"],"input_types":["LLM API requests","LLM API responses","user metadata","system events"],"output_types":["audit logs","compliance reports","usage analytics","forensic data"],"categories":["compliance","security","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aim-security__cap_4","uri":"capability://security.api.gateway.zero.trust.enforcement","name":"api-gateway-zero-trust-enforcement","description":"Enforces zero-trust security policies at the API gateway level, controlling which LLM providers can be accessed, validating all requests, and preventing unauthorized data flows to external AI services. Implements identity-based access control for LLM integrations.","intents":["I want to ensure only approved LLM providers can be used in my organization","I need to prevent shadow AI usage and unauthorized integrations","I want to control which teams or applications can access which LLM models"],"best_for":["Enterprise security teams","Organizations with strict vendor management policies","Companies concerned about shadow AI and unauthorized SaaS usage"],"limitations":["Requires integration at API gateway level, may need infrastructure changes","Cannot prevent local model usage or direct API calls outside gateway","May impact developer velocity if policies are too restrictive"],"requires":["API gateway infrastructure","Identity and access management system","Approved vendor/model whitelist","Network-level enforcement capability"],"input_types":["API requests","user identity","destination endpoints","request metadata"],"output_types":["allow/deny decisions","access logs","policy violation alerts","usage reports"],"categories":["security","access-control","compliance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aim-security__cap_5","uri":"capability://compliance.compliance.documentation.automation","name":"compliance-documentation-automation","description":"Automatically generates compliance documentation and audit reports for regulated industries by aggregating LLM usage data, security controls, and policy adherence. Streamlines evidence collection for compliance audits and certifications.","intents":["I need to prepare documentation for SOC 2, ISO 27001, or other compliance audits","I want to demonstrate that our AI usage meets regulatory requirements","I need to generate evidence of security controls for compliance reviews"],"best_for":["Regulated enterprises (finance, healthcare, legal)","Organizations undergoing compliance audits","Companies with multiple compliance frameworks to manage"],"limitations":["Cannot replace human compliance review and sign-off","Requires accurate configuration of compliance rules and policies","May not cover all industry-specific compliance requirements"],"requires":["Audit log data from LLM usage","Configured compliance frameworks and policies","Security control implementation data","Policy documentation"],"input_types":["audit logs","security control data","policy configurations","usage metrics"],"output_types":["compliance reports","audit documentation","evidence packages","control attestations"],"categories":["compliance","reporting"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aim-security__cap_6","uri":"capability://monitoring.model.behavior.monitoring","name":"model-behavior-monitoring","description":"Continuously monitors LLM outputs for unexpected behavior changes, hallucinations, or deviations from expected patterns. Detects model drift, poisoning attempts, or quality degradation in real-time.","intents":["I want to detect when a model starts producing incorrect or harmful outputs","I need to identify if a model has been compromised or poisoned","I want to monitor for quality degradation in model responses over time"],"best_for":["Organizations with mission-critical LLM deployments","Companies concerned about model reliability","Enterprises needing production monitoring"],"limitations":["Requires baseline data to detect anomalies","May have high false positive rates for legitimate output variation","Cannot predict all types of model failures"],"requires":["Historical baseline data of normal model behavior","Real-time output analysis capability","Anomaly detection algorithms","Integration with model inference pipeline"],"input_types":["model outputs","user feedback","quality metrics","historical data"],"output_types":["anomaly alerts","behavior change reports","quality metrics","drift indicators"],"categories":["monitoring","quality-assurance","security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aim-security__cap_7","uri":"capability://governance.policy.enforcement.and.governance","name":"policy-enforcement-and-governance","description":"Defines, enforces, and manages organization-wide policies for GenAI usage including acceptable use policies, data handling rules, and model selection guidelines. Provides centralized governance for AI tool adoption.","intents":["I want to establish clear policies for how employees can use GenAI tools","I need to enforce data handling standards across all LLM integrations","I want to control which models and providers are approved for use"],"best_for":["Enterprise governance teams","Organizations scaling GenAI adoption","Companies needing centralized AI governance"],"limitations":["Policies must be regularly updated as threats and use cases evolve","Enforcement depends on technical integration and user compliance","May create friction if policies are too restrictive"],"requires":["Policy definition framework","Integration with LLM endpoints and gateways","User communication and training","Monitoring and enforcement mechanisms"],"input_types":["policy definitions","business rules","compliance requirements","user actions"],"output_types":["policy enforcement decisions","violation alerts","policy reports","governance dashboards"],"categories":["governance","compliance","security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aim-security__cap_8","uri":"capability://data.protection.sensitive.data.classification.and.tagging","name":"sensitive-data-classification-and-tagging","description":"Automatically identifies and classifies sensitive data types (PII, PHI, trade secrets, credentials) within prompts and responses, then applies appropriate handling rules. Uses pattern recognition and contextual analysis to tag data sensitivity levels.","intents":["I want to automatically identify when sensitive data is being sent to LLMs","I need to classify data by sensitivity level to apply appropriate controls","I want to ensure consistent data classification across all LLM interactions"],"best_for":["Organizations handling regulated data (healthcare, finance, legal)","Companies with complex data classification needs","Enterprises needing automated data governance"],"limitations":["Cannot detect all types of sensitive data, especially obfuscated or encoded","Requires custom rules for industry-specific data types","May have false positives or false negatives"],"requires":["Data classification rules and patterns","Integration with LLM request/response pipeline","Configurable sensitivity levels","Pattern matching and NLP capabilities"],"input_types":["text prompts","model responses","user queries","structured data"],"output_types":["data classification tags","sensitivity scores","classification reports","handling recommendations"],"categories":["data-protection","compliance","security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aim-security__cap_9","uri":"capability://security.real.time.threat.alerting","name":"real-time-threat-alerting","description":"Generates real-time alerts for detected security threats, policy violations, and compliance issues related to LLM usage. 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