Explore the AI security product landscape

TrojAI Defend blocks prompt injection, jailbreaking, sensitive-data leakage, and toxic content in real time under security policies.

WonderFence intercepts unsafe model inputs and blocks harmful outputs in production LLM and agentic workflows.

Amazon Bedrock Guardrails applies inline controls that can block prompt-injection and jailbreak-style requests before model outputs are returned.

Anthropic applies constitutional classifier safeguards to detect and block jailbreak attempts against Claude model behavior.

Arthur Shield identifies and blocks prompt-injection attempts that seek to override intended LLM behavior.

Cato AI Security runtime monitoring detects prompt-injection patterns and policy-violating AI prompt activity.

AI Agent Security helps detect prompt attacks, indirect injection, and sensitive data exposure across agent prompts, responses, and tool use.

Check Point AI Security documents runtime enforcement that includes protection against harmful outputs in AI interactions.

Lakera runtime AI security positioning includes prompt attack prevention, data leakage protection, and context-aware runtime security controls.

AI Runtime Protection provides runtime guardrails for prompt and response threat mitigation.

Aiceberg Guardian Agent provides runtime visibility and guardrail control over agentic AI decision flows.

Cranium AI platform interaction inspection is positioned to detect adversarial prompt-injection patterns in runtime workflows.

Pangea AI Guard detects and blocks prompt injection, jailbreak attempts, and malicious content insertion in AI applications.

DynamoGuard performs low-latency runtime inspection of prompts and model completions to detect and block prompt injection, jailbreaks, PII leakage, and toxic content.

Sift intercepts API traffic to foundation models to detect and block prompt injection, jailbreaking, PII leakage, and toxic outputs at the gateway boundary.

F5 AI Guardrails applies inline policy controls that can block prompt-injection payloads before requests reach downstream models and tools.

F5 AI Guardrails enforces policies to detect and prevent jailbreak-style attempts that seek to bypass model safety boundaries.

Glow continuous endpoint monitoring can detect suspicious AI interaction patterns and prompt-driven misuse across local applications and browser activity.

Model Armor scans prompts and responses for prompt injection and jailbreak content and can return a block verdict when a violation is detected.

Model Armor sanitizes MCP tool-call requests, responses, and tool execution errors and can block content that violates configured security filters.

Cygnal classifies and blocks adversarial AI inputs and unsafe outputs during production runtime.

HiddenLayer presents AI Guardrails as a use case for enforcing safe, compliant, and policy-aligned AI behavior across applications, models, and business units.

AI Runtime Security can detect, block, or redact unsafe actions and sensitive information according to policy and platform capabilities.

Lasso runtime enforcement can block prompt-injection attempts and prevent unsafe autonomous agent actions or unauthorized tool invocation.

Bifrost applies runtime model protection controls that block unsafe outputs and enforce policy on live AI traffic.

Llama Guard classifies input prompts and model responses against content-safety categories so applications can detect unsafe interactions.

Llama Prompt Guard classifies input text for prompt injection and jailbreak patterns before the text reaches the primary model.

Azure AI Content Safety and Prompt Shields identify direct and indirect prompt injection and related unsafe prompt patterns before they reach downstream model behavior.

Azure AI Content Safety applies jailbreak detection and policy filtering to reduce unsafe prompt and response interactions at runtime.

Runtime guardrails are applied to block prompt injection attack paths before harmful model behavior propagates.

Neo Security Platform is positioned to prevent agentic threats by controlling unsafe runtime action paths and over-permissioned execution conditions.

AI Agent Security uses hook-level interception and tool-call governance to constrain unsafe autonomous actions before execution.

NeMo Guardrails applies programmable dialog, topical, and safety rails to inspect and constrain input prompts and model responses in LLM applications.

OpenAI Guardrails Python runs configurable checks on application inputs and outputs so failed checks can prevent an unsafe interaction from proceeding.

The OpenAI Moderation API inspects text and image content and returns category classifications for potentially harmful content.

LLM Guard inspects, sanitizes, and evaluates LLM prompts and model completions against prompt injection, jailbreaks, and sensitive data leakage.

Portkey invokes Prisma AIRS guardrail checks before and after model requests and can enforce returned block verdicts.

Prisma AIRS applies real-time safeguards to AI prompts, responses, model interactions, agent actions, and data exposure paths.

Prompt Security runtime enforcement can block prompt-injection attempts in employee and application AI interaction paths.

Cortex AI Guardrails evaluate runtime prompts and responses to mitigate direct and indirect prompt-injection and jailbreak attempts.

TrendAI Vision One AI Security inspects and controls enterprise traffic to public and private GenAI services to reduce prompt-injection abuse and unauthorized AI use.

Votal blocks prompt-injection patterns and PII policy violations through layered runtime guardrail controls.

Zscaler AI Security runtime protection blocks prompt injection, data poisoning, and malicious URLs in AI interactions.

TrojAI Defend for MCP discovers MCP servers and tools and enforces policy on agent-to-model-to-server communications.

Cato AI Security governance controls constrain unsafe autonomous actions and high-risk tool usage for AI agents and applications.

AI Agent Security governs which tools and MCP servers agents can use, and evaluates agent actions in context to help block unsafe or unauthorized behavior at runtime.

Aiceberg Guardian Agent provides runtime visibility and guardrail control over agentic AI decision flows.

Cranium AI platform is positioned with runtime policy controls to govern unsafe autonomous agent behavior and over-permissioned execution paths.

Pangea provides proxy-based MCP integration to apply guardrails and authentication or authorization controls to agent-to-tool and agent-to-agent communications.

F5 AI Guardrails applies agent-focused runtime controls to constrain unsafe autonomous actions and high-risk tool execution paths.

Glow autonomous endpoint enforcement can block unsafe agentic tool execution and unauthorized actions initiated from user devices.

Through Agent Gateway, Model Armor screens and can block policy-violating traffic between agents and clients, external systems, MCP servers, third-party AI agents, and other AI agents.

Wiz AI-SPM identifies and classifies tools that agents can access to show the actions those agents can perform.

HiddenLayer presents Agentic and MCP Security as a use case for protecting autonomous agents and MCP-based systems from prompt injection, unsafe tool use, and harmful autonomous actions.

AI Runtime Security can detect, block, or redact unsafe actions and sensitive information according to policy and platform capabilities.

Lasso runtime enforcement can block prompt-injection attempts and prevent unsafe autonomous agent actions or unauthorized tool invocation.

Microsoft Agent 365 provides continuous agent threat detection and configurable real-time protection policies that block unsafe behaviors and malicious activity.

Runtime response workflows support containment and remediation for unsafe agent behavior and unauthorized action chains.

Neo Security Platform is positioned to prevent agentic threats by controlling unsafe runtime action paths and over-permissioned execution conditions.

AI Agent Security uses hook-level interception and tool-call governance to constrain unsafe autonomous actions before execution.

Koi AES enforces endpoint guardrails that approve, flag, or block autonomous tools.

Prisma AIRS verifies agent identity and enforces real-time security controls for agent actions.

Prisma AIRS stops agent-specific tool misuse, memory manipulation, adversarial instructions, and prompt injection before threats propagate across an agent swarm.

Prompt Security agent-focused governance restricts risky autonomous tool use and unsafe execution decisions.

Cortex AI Gateway records both agent identity and human delegator context for task-scoped attribution and governance workflows.

Snowflake reported remediation of an indirect prompt-injection path in Cortex Code workflows following external security research disclosures.

Straiker Defend AI detects and blocks identity abuse, memory poisoning, data exfiltration, and resource exploitation targeting AI agents at runtime.

Zenity runtime boundaries can block unsafe agent actions, including risky tool usage and unauthorized task execution.

Zscaler AI Security runtime protection blocks prompt injection, data poisoning, and malicious URLs in AI interactions.

WonderFence intercepts unsafe model inputs and blocks harmful outputs in production LLM and agentic workflows.

SageMaker Clarify and monitoring workflows provide ongoing model and data behavior analysis for drift, quality, and governance evidence.

DeepKeep monitors and controls AI agent gateway usage, identifies active MCP server dependencies, and supports allow or block policy enforcement.

DynamoGuard performs low-latency runtime inspection of prompts and model completions to detect and block prompt injection, jailbreaks, PII leakage, and toxic content.

Sift intercepts API traffic to foundation models to detect and block prompt injection, jailbreaking, PII leakage, and toxic outputs at the gateway boundary.

Security Command Center detects AI-specific threats across the AI stack.

Security Command Center provides response workflows for AI-specific threats across the AI stack.

Wiz AI Runtime Protection detects prompt injection, rogue agents, and malicious behavior targeting AI systems.

Cygnal classifies and blocks adversarial AI inputs and unsafe outputs during production runtime.

AI Runtime Security detects and investigates prompt injection, unsafe agent behavior, sensitive data exposure, malicious tool use, and other runtime AI threats.

Lasso AI detection and response workflows identify and triage suspicious AI behavior using contextual attack telemetry.

Microsoft Agent 365 provides continuous agent threat detection and configurable real-time protection policies that block unsafe behaviors and malicious activity.

Microsoft Copilot for Security supports analyst investigation and response workflows to accelerate triage and containment actions.

AI Runtime Threat Detection and Response monitors production AI execution to detect suspicious outputs and attack behavior.

Data Detection and Response performs real-time scanning with automated quarantine, notification, and remediation workflows for risky content flows.

Zenity behavioral runtime telemetry supports detection and investigation of unsafe or manipulative AI outcomes.

0DIN publishes threat-intelligence updates and validated vulnerability patterns for enterprise AI security teams.

Amazon Bedrock AgentCore gives agents distinct scoped identities and uses Cedar policy to authorize every tool call and data access independently of model reasoning.

Claude Code requires permission for state-changing file, command, and network actions before those actions execute.

BeyondTrust provides MCP server access controls and Secrets Insights integration to reduce unsafe secret exposure.

Cyera Agent Guardian traces and governs agentic data usage across autonomous agent delegation chains, enforcing automated data access perimeters and non-human identity controls.

DeepKeep monitors and controls AI agent gateway usage, identifies active MCP server dependencies, and supports allow or block policy enforcement.

AgentWarden analyzes reachable agent tools, external endpoints, and data sources during build time, evaluating tool combinations that present lethal trifecta risks (data access, external egress, and execution authority).

AgentWarden enforces security and authorization policies at runtime agent action boundaries across prompts, tool calls, tool responses, and final actions to prevent unauthorized execution and goal hijacking.

Model Armor sanitizes MCP tool-call requests, responses, and tool execution errors and can block content that violates configured security filters.

Wiz AI-SPM identifies and classifies tools that agents can access to show the actions those agents can perform.

Bifrost centralizes MCP tool connections and enforces authentication, access-control, and governance policies for agent tool usage.

Microsoft Foundry provisions distinct Entra agent identities and uses scoped access tokens and RBAC to authorize agent calls to MCP servers and downstream tools.

OpenShell runs autonomous agents in isolated sandboxes and enforces out-of-process filesystem, network, process, inference, credential, and skill policies at binary, destination, method, and path level, with developer-approved policy updates and an audit trail of allow and deny decisions.

Okta Agent Gateway intercepts agent-to-tool and agent-to-API calls at runtime without requiring upstream application code changes.

OpenAI Frontier integrates automated security testing and red teaming to evaluate AI coworkers for prompt injections, jailbreaks, data leaks, tool misuse, and out-of-policy behaviors.

Promptfoo simulates multi-turn adaptive attacks, autonomous agent goal hijacking, tool execution vulnerabilities, and unauthorized function calls in pre-deployment CI/CD environments.

Promptfoo MCP Proxy intercepts Model Context Protocol traffic between clients and tools to evaluate tool-call authorization, parameter tampering, and excessive agency.

Idira Secure AI Agents acts as a dynamic agent identity broker that grants an agent access only for the duration of a specific task and automatically revokes permissions once the task completes.

Koi AES enforces endpoint guardrails that approve, flag, or block autonomous tools.

Portkey MCP Gateway centralizes authentication, fine-grained server and tool access, observability, and runtime policy and stops unauthorized tool invocations before execution.

Prisma AIRS verifies agent identity and enforces real-time security controls for agent actions.

Prisma AIRS governs agent tool calls, model access, and external connections through centralized policy and assigns each agent a governed identity with precise permissions and traceability.

Ping Agent Gateway intercepts agent invocations across multi-cloud proxies and MCP servers to evaluate runtime authorization policies dynamically before executing tool calls.

Saviynt Agent Access Gateway intercepts agent-to-tool and agent-to-application API calls at runtime and evaluates them against task intent before permitting execution.

Teleport proxies Model Context Protocol (MCP) server traffic and enforces granular role-based access control (RBAC) to allow or block specific tool calls based on string, regex, or glob patterns.

Verno Labs provides runtime enforcement controls to constrain unsafe or unauthorized AI agent actions.

Votal inspects model requests and tool calls in real time, gates tool permissions by tenant and role, and enforces policy decisions at runtime.

Zscaler AI Broker secures agentic communications through MCP and A2A brokers and enforces fine-grained access policies across enterprise AI agents.

Cyera Agent Guardian traces and governs agentic data usage across autonomous agent delegation chains, enforcing automated data access perimeters and non-human identity controls.

Mirror Security keeps prompts, context, and responses encrypted through inference workflows while enforcing decision-time policies.

Prisma AIRS stops agent-specific tool misuse, memory manipulation, adversarial instructions, and prompt injection before threats propagate across an agent swarm.

Straiker Defend AI detects and blocks identity abuse, memory poisoning, data exfiltration, and resource exploitation targeting AI agents at runtime.

Amazon Bedrock AgentCore applies identity and authorization controls to agents using MCP and A2A communications with external tools and other agents.

Through Agent Gateway, Model Armor screens and can block policy-violating traffic between agents and clients, external systems, MCP servers, third-party AI agents, and other AI agents.

Microsoft Foundry uses agent identities and audience-scoped tokens to authenticate Agent-to-Agent endpoints and allow downstream services to grant or deny access through RBAC.

Zscaler AI Broker secures agentic communications through MCP and A2A brokers and enforces fine-grained access policies across enterprise AI agents.

Claude Code constrains command execution with operating-system sandbox boundaries that limit filesystem and network access.

BeyondTrust Endpoint Privilege Management places least-privilege guardrails on local AI agents.

NemoClaw deploys supported autonomous agents from a digest-verified hardened blueprint with restrictive filesystem and process defaults, credential filtering, routed inference, state integrity checks, and operator approval for blocked network endpoints.

OpenShell runs autonomous agents in isolated sandboxes and enforces out-of-process filesystem, network, process, inference, credential, and skill policies at binary, destination, method, and path level, with developer-approved policy updates and an audit trail of allow and deny decisions.

Okta Agent Gateway intercepts agent-to-tool and agent-to-API calls at runtime without requiring upstream application code changes.

Ping Agent Gateway intercepts agent invocations across multi-cloud proxies and MCP servers to evaluate runtime authorization policies dynamically before executing tool calls.

Saviynt Agent Access Gateway intercepts agent-to-tool and agent-to-application API calls at runtime and evaluates them against task intent before permitting execution.

Teleport Beams provisions ephemeral Firecracker microVMs with file system and network isolation to execute infrastructure AI agents without static credentials.

AgentWarden enforces security and authorization policies at runtime agent action boundaries across prompts, tool calls, tool responses, and final actions to prevent unauthorized execution and goal hijacking.

Prisma AIRS stops agent-specific tool misuse, memory manipulation, adversarial instructions, and prompt injection before threats propagate across an agent swarm.

SailPoint mandates human owner assignment at onboarding and triggers automated succession workflows to prevent orphaned AI agents.

Verno Labs provides runtime enforcement controls to constrain unsafe or unauthorized AI agent actions.

AWS supports human approval hooks for high-consequence agent actions and behavioral monitoring for actions outside an agent's authorized scope.

Claude Code requires permission for state-changing file, command, and network actions before those actions execute.

NemoClaw deploys supported autonomous agents from a digest-verified hardened blueprint with restrictive filesystem and process defaults, credential filtering, routed inference, state integrity checks, and operator approval for blocked network endpoints.

Okta registers AI agents as first-class identities in Universal Directory with mandatory human owner binding and lifecycle governance.

Idira Secure AI Agents logs agent actions and communications, recording which agent performed an action and on behalf of which user, to support governance and compliance review.

PingOne Agent IAM Core registers AI agents as managed non-human identities with unique IDs, human ownership attribution, and dynamic scoping.

SailPoint mandates human owner assignment at onboarding and triggers automated succession workflows to prevent orphaned AI agents.

Saviynt binds every AI agent to a human owner sponsor for lifecycle governance and accountable execution.

Cisco AI Defense includes AI access and policy controls as part of the platform's documented control surface.

Bifrost centralizes MCP tool connections and enforces authentication, access-control, and governance policies for agent tool usage.

AI Access Security classifies GenAI applications by sanction status and can revoke access or control upload and download actions based on risk and privilege.

Portkey MCP Gateway centralizes authentication, fine-grained server and tool access, observability, and runtime policy and stops unauthorized tool invocations before execution.

Teleport proxies Model Context Protocol (MCP) server traffic and enforces granular role-based access control (RBAC) to allow or block specific tool calls based on string, regex, or glob patterns.

TrendAI Vision One AI Security inspects and controls enterprise traffic to public and private GenAI services to reduce prompt-injection abuse and unauthorized AI use.

Votal inspects model requests and tool calls in real time, gates tool permissions by tenant and role, and enforces policy decisions at runtime.

Zscaler AI Security can warn, block, or isolate user access to AI applications under acceptable-use and data-protection policies.

Harmonic Security discovers and catalogs enterprise generative AI application usage (Shadow AI), categorizing application risk posture and data compliance across cloud and endpoint environments.

Microsoft Purview for AI provides classification and compliance tracking artifacts that support governance review of AI information handling.

Data Detection and Response performs real-time scanning with automated quarantine, notification, and remediation workflows for risky content flows.

AI Access Security discovers and categorizes GenAI applications, agents, marketplace plugins, usage, and users.

Zscaler AI Security can warn, block, or isolate user access to AI applications under acceptable-use and data-protection policies.

Amazon Bedrock AgentCore gives agents distinct scoped identities and uses Cedar policy to authorize every tool call and data access independently of model reasoning.

BeyondTrust provides MCP server access controls and Secrets Insights integration to reduce unsafe secret exposure.

Cyera AI-SPM maps AI assets to identities and sensitive data to identify over-permissioned agents, misconfigurations, and unauthorized data paths.

Wiz AI-SPM identifies posture gaps involving risky agent permissions and connects identities and access paths to AI-system exposure.

Microsoft Foundry provisions distinct Entra agent identities and uses scoped access tokens and RBAC to authorize agent calls to MCP servers and downstream tools.

Okta registers AI agents as first-class identities in Universal Directory with mandatory human owner binding and lifecycle governance.

Idira Secure AI Agents acts as a dynamic agent identity broker that grants an agent access only for the duration of a specific task and automatically revokes permissions once the task completes.

Prisma AIRS governs agent tool calls, model access, and external connections through centralized policy and assigns each agent a governed identity with precise permissions and traceability.

PingOne Agent IAM Core registers AI agents as managed non-human identities with unique IDs, human ownership attribution, and dynamic scoping.

SailPoint aggregates AI agent objects, permission scopes, credentials, and ownership metadata across cloud platforms.

SailPoint Machine Identity Security governs the lifecycle of service accounts, API keys, OAuth tokens, and RPA bots through automated access certifications and cryptographic verification.

Saviynt binds every AI agent to a human owner sponsor for lifecycle governance and accountable execution.

Cortex AI Gateway centralizes access policy, authentication, and tool-permission controls across first-party and third-party MCP-connected agent workflows.

Straiker Defend AI detects and blocks identity abuse, memory poisoning, data exfiltration, and resource exploitation targeting AI agents at runtime.

Tenable One AI Exposure surfaces identity and entitlement risk for AI workloads, including over-permissioned access paths that can increase autonomous abuse risk.

Zenity posture workflows evaluate configuration and permission risk for agents before and during deployment.

Zenity identity-aware runtime boundaries can prevent sensitive data access and exposure by over-permissioned agents.

Zscaler AI Access Graph provides real-time visibility into how AI agents use data and identities, identifies unnecessary access, and tracks data lineage across channels.

Zscaler AI Broker secures agentic communications through MCP and A2A brokers and enforces fine-grained access policies across enterprise AI agents.

Amazon Bedrock Guardrails includes sensitive-information policy controls to reduce protected-data leakage in prompt and response flows.

Arthur Shield applies runtime checks intended to prevent sensitive data leakage through LLM interactions.

Cato AI Security applies inline controls to block sensitive data exfiltration to unauthorized AI services and channels.

AI Agent Security helps detect prompt attacks, indirect injection, and sensitive data exposure across agent prompts, responses, and tool use.

AI Runtime Protection provides runtime guardrails for prompt and response threat mitigation.

Cranium AI platform data controls are positioned to prevent sensitive information disclosure across model and agent workflows.

Pangea Redact detects confidential information and PII in AI workflows and can block, mask, hash, or encrypt the data under configured rules.

Cyera AI Guardian evaluates data-exposure risk and enforces policies to reduce sensitive-data exposure to AI systems.

DynamoGuard performs low-latency runtime inspection of prompts and model completions to detect and block prompt injection, jailbreaks, PII leakage, and toxic content.

Sift intercepts API traffic to foundation models to detect and block prompt injection, jailbreaking, PII leakage, and toxic outputs at the gateway boundary.

F5 AI Guardrails inspects prompts and responses to redact or block sensitive data and reduce leakage to external AI systems.

Model Armor can inspect, transform, tokenize, and redact sensitive elements in AI prompts and responses through Sensitive Data Protection integration.

Sensitive Data Protection classifies and de-identifies sensitive content used for model training, tuning, and generative AI prompts and responses.

Harmonic Security inspects user prompts in real time to detect sensitive enterprise information (PII, source code, financial data) and enforces redaction or blocking policies prior to transmission to external LLMs.

Microsoft Purview for AI applies DLP and sensitivity controls to reduce unauthorized sensitive data transfer in AI interactions.

Mirror Security keeps prompts, context, and responses encrypted through inference workflows while enforcing decision-time policies.

AI Application DLP applies pre-submission filtering and policy controls to block sensitive prompt, upload, and clipboard data before transfer to AI providers.

AI Access Security classifies sensitive content inline and blocks sensitive text and file transfers to GenAI applications.

LLM Guard inspects, sanitizes, and evaluates LLM prompts and model completions against prompt injection, jailbreaks, and sensitive data leakage.

Portkey automatically redacts sensitive data from requests before sending them to an LLM.

Prisma AIRS applies real-time safeguards to AI prompts, responses, model interactions, agent actions, and data exposure paths.

Prompt Security prompt and content controls redact sensitive enterprise data before it is sent to external models.

TrendAI combines AI Scanner pre-deployment testing with AI Guard runtime controls to detect vulnerabilities and reduce sensitive data leakage in AI applications.

Votal blocks prompt-injection patterns and PII policy violations through layered runtime guardrail controls.

Zscaler AI Security applies data security and content policies to prevent risky AI outputs and govern response safety.

Zscaler Data Security inspects AI usage and prompts and can block risky access or enforce prompt DLP to prevent data loss.

Sensitive Data Protection continuously discovers and classifies sensitive data across organizational data assets.

Sensitive Data Protection classifies and de-identifies sensitive content used for model training, tuning, and generative AI prompts and responses.

Wiz DSPM for AI detects sensitive training data, identifies leakage risk, and exposes attack paths to that data.

Harmonic Security inspects user prompts in real time to detect sensitive enterprise information (PII, source code, financial data) and enforces redaction or blocking policies prior to transmission to external LLMs.

Microsoft Purview for AI applies DLP and sensitivity controls to reduce unauthorized sensitive data transfer in AI interactions.

AI Application DLP applies pre-submission filtering and policy controls to block sensitive prompt, upload, and clipboard data before transfer to AI providers.

Zscaler Data Security discovers and classifies sensitive data and uses contextual classification to identify data risk across GenAI and other channels.

WonderCheck runs scheduled production evaluations to detect model drift, regressions, and emerging AI vulnerabilities over time.

AWS AI Security Framework and Security Hub posture workflows support layered AI control oversight and detection of risky configuration conditions.

Cyera AI-SPM maps AI assets to identities and sensitive data to identify over-permissioned agents, misconfigurations, and unauthorized data paths.

Security Command Center assesses interconnected AI risks and prioritizes high-risk issues using posture analysis and virtual red teaming.

Wiz AI-SPM applies built-in configuration rules to detect misconfigured AI services and unsafe deployments.

Harmonic Security discovers and catalogs enterprise generative AI application usage (Shadow AI), categorizing application risk posture and data compliance across cloud and endpoint environments.

AI Discovery scans cloud accounts, repositories, endpoints, and pipelines to detect AI models and associated agents, and associates assets with ownership, sensitivity, and risk metadata.

Lasso AI Security Posture Management evaluates misconfigurations and policy gaps, including supply-chain-oriented risk indicators before production rollout.

Microsoft Agent 365 powers a centralized inventory and posture view of Microsoft and supported non-Microsoft agents, including identities, tools, MCP servers, risk indicators, alerts, and recommendations.

Defender for Cloud AI posture capabilities discover and inventory AI resources and workloads for security posture visibility.

Neo Security Platform analyzes security gaps across agentic and non-agentic software to surface exposure conditions that require remediation.

Prisma AIRS provides posture visibility and policy management for AI assets, training and inference data, application integrity, and model access.

PingOne Agent Governance discovers and catalogs AI agents across cloud platforms and MCP servers to eliminate shadow agent sprawl.

SailPoint generates an AI Bill of Materials that details tools, data access boundaries, model dependencies, and authority limits for each deployed agent.

Saviynt discovers and catalogs non-human identities, service accounts, and AI agents across multi-cloud and SaaS environments to eliminate shadow AI.

Cortex AI Gateway provides centralized token and API-consumption visibility with spend governance controls to reduce runaway enterprise AI usage.

Straiker Discover AI maps AI agents, MCP servers, and agentic workflows and provides posture monitoring and misconfiguration detection.

Tenable One AI Exposure analyzes AI posture and misconfiguration conditions to prioritize exposure reduction for AI workloads.

TrendAI AI-SPM discovers AI-related cloud assets and identifies security misconfigurations and exposure paths in LLM deployments.

Zenity posture workflows evaluate configuration and permission risk for agents before and during deployment.

Zscaler Data Security provides data posture controls for GenAI and adjacent channels by combining contextual classification with risk identification.

Amazon Bedrock AgentCore Registry catalogs agents and MCP servers for centralized governance and observability.

AWS AI Security Framework and Security Hub posture workflows support layered AI control oversight and detection of risky configuration conditions.

BeyondTrust automatically discovers AI agents and analyzes cross-domain privilege paths.

Cato AI Security governs sanctioned and unsanctioned enterprise AI usage through interaction-level visibility and policy enforcement.

AI Agent Security discovers agents and assesses their tool and MCP access surface for security risk.

AI Cloud Visibility provides discovery and inventory context for AI workloads, models, data, and users.

Cranium discovers agents across enterprise environments, surfaces shadow AI, and records ownership and usage context in a living system of record.

Cyera AI-SPM discovers and inventories AI models, applications, agents, knowledge bases, and web applications in enterprise environments.

Glow continuous endpoint monitoring can detect suspicious AI interaction patterns and prompt-driven misuse across local applications and browser activity.

Security Command Center discovers and inventories AI assets across agents, data, models, applications, platforms, and infrastructure.

Wiz AI-SPM discovers and catalogs AI models, agents, services, technologies, SDKs, pipelines, and related cloud infrastructure without agents.

Harmonic Security discovers and catalogs enterprise generative AI application usage (Shadow AI), categorizing application risk posture and data compliance across cloud and endpoint environments.

AI Discovery scans cloud accounts, repositories, endpoints, and pipelines to detect AI models and associated agents, and associates assets with ownership, sensitivity, and risk metadata.

Lasso Discovery and AI-BOM inventories agents, tools, models, prompts, and guardrails with change tracking across environments.

Microsoft Agent 365 powers a centralized inventory and posture view of Microsoft and supported non-Microsoft agents, including identities, tools, MCP servers, risk indicators, alerts, and recommendations.

Defender for Cloud AI posture capabilities discover and inventory AI resources and workloads for security posture visibility.

AI Discovery and Recon identifies models, agents, MCP servers, tools, and shadow AI to map AI attack surface and exposure.

Neo Security Platform provides visibility into risky misconfigurations and permission issues across enterprise software environments.

Idira Secure AI Agents scans SaaS, cloud, and developer environments to discover active AI agents and enriches each one with ownership, purpose, status, and permission-level context in a centralized registry.

Koi AES discovers and catalogs autonomous software agents, AI agents, browser extensions, software, and AI models on enterprise endpoints.

Prisma AIRS maps enterprise, endpoint, and browser agents and surfaces MCP servers, plugins, tool interactions, shadow AI, and unsanctioned agents.

PingOne Agent Governance discovers and catalogs AI agents across cloud platforms and MCP servers to eliminate shadow agent sprawl.

SailPoint aggregates AI agent objects, permission scopes, credentials, and ownership metadata across cloud platforms.

Saviynt discovers and catalogs non-human identities, service accounts, and AI agents across multi-cloud and SaaS environments to eliminate shadow AI.

Prompt Security provides enterprise visibility over GenAI usage patterns and interaction channels.

Straiker Discover AI maps AI agents, MCP servers, and agentic workflows and provides posture monitoring and misconfiguration detection.

Tenable One AI Exposure provides continuous discovery of AI assets and attack-surface context across software and infrastructure environments.

TrendAI AI-SPM discovers AI-related cloud assets and identifies security misconfigurations and exposure paths in LLM deployments.

Verno Labs discovers AI agents and maintains lifecycle-oriented visibility over deployed agent surfaces.

Zenity AI Observability builds live inventory of agents, owners, permissions, and touched data across SaaS, cloud, and endpoint environments.

Zscaler AI Security discovers and maps AI applications, models, MCP servers, development tools, data pipelines, and related risks.

AWS posture and governance workflows help identify control gaps around autonomous and agentic execution paths for prioritized remediation.

Security Command Center assesses interconnected AI risks and prioritizes high-risk issues using posture analysis and virtual red teaming.

Wiz AI-SPM connects infrastructure, identity, models, data, and applications to uncover and prioritize exploitable AI attack paths.

Defender for Cloud AI posture capabilities provide risk prioritization signals to focus remediation for high-impact AI exposure paths.

Mindgard platform workflows include risk analysis and remediation guidance to prioritize treatment of AI security findings.

Tenable One AI Exposure analyzes AI posture and misconfiguration conditions to prioritize exposure reduction for AI workloads.

Chainguard states that library artifacts are built from source with full provenance and signed SBOMs.

Wiz AI-BOM continuously inventories AI models, datasets, frameworks, software dependencies, identities, access paths, and infrastructure.

AI Supply Chain Security analyzes model architectures, layers, weights, and artifacts for tampering or anomalies and tracks model lineage, origin, and licensing.

Lasso Discovery and AI-BOM inventories agents, tools, models, prompts, and guardrails with change tracking across environments.

Zscaler AI Security discovers and maps AI applications, models, MCP servers, development tools, data pipelines, and related risks.

Chainguard Factory applies SHA-pinned source inputs, isolated SLSA L3 build controls, cryptographic signing, and reproducibility checks when producing artifacts.

Chainguard Libraries provides a malware-resistant dependency catalog intended to replace direct reliance on public package registries.

ModelScan statically analyzes serialized ML model files (PyTorch, Pickle, SavedModel, Keras) to detect embedded code execution exploits and malicious payload injections.

Chainguard Containers is positioned as a built-from-source container catalog designed to minimize known vulnerabilities in development and production pipelines.

Chainguard AI Security documents software supply chain coverage across SDLC phases, including trusted dependencies, CI/CD integrity, and hardened runtime foundations.

Cisco AI Supply Chain Risk Management provides governance and security over AI models and files used by AI applications and agents.

AI Supply Chain Security analyzes model architectures, layers, weights, and artifacts for tampering or anomalies and tracks model lineage, origin, and licensing.

Koi AES evaluates code differences and behavioral shifts in real time to identify software supply-chain risk.

Prisma AIRS scans third-party models for tampering, malicious scripts, and deserialization risks.

Prisma AIRS scans agent code, MCP servers, and skills for unsafe permissions, hidden vulnerabilities, and indirect injection paths.

TrojAI Detect runs continuous agent-led red teaming against AI models and agents to identify unsafe behavior before deployment.

WonderBuild performs pre-deployment adversarial testing and red teaming across AI models, applications, and agents.

SageMaker Clarify and monitoring workflows provide ongoing model and data behavior analysis for drift, quality, and governance evidence.

Arthur Platform performs pre-production and runtime evaluations to monitor AI behavior and policy outcomes.

AI Red Teaming runs broad and targeted adversarial campaigns to assess AI applications and agents for prompt injection, jailbreak, data leakage, unauthorized actions, and regressions.

Lakera Red Teaming is positioned as risk-based AI testing with direct and indirect attack simulations and remediation-focused workflows.

AI Model and Application Validation performs algorithmic red teaming and model vulnerability validation for AI applications and models.

DeepKeep performs AI red teaming and model security scanning to discover vulnerabilities before production impact.

DynamoEval executes automated adversarial attacks, security benchmark evaluations, and regulatory compliance stress-tests on models prior to production release.

Enkrypt Red Team conducts automated adversarial evaluations against model endpoints across known prompt injection and compliance vulnerability categories.

F5 AI Guardrails enforces policies to detect and prevent jailbreak-style attempts that seek to bypass model safety boundaries.

Shade runs adversarial red teaming against deployed models, agent workflows, and guardrail configurations to surface exploitable weaknesses.

AI Attack Simulation tests AI systems for jailbreaks, prompt injection, data leakage, and unsafe agent tool use through automated adversarial simulation.

Lasso automated AI red teaming runs adversarial testing against agentic workflows to identify exploitable weaknesses.

AI Red Teaming and Assessment runs adversarial workflows to surface jailbreak and guardrail-bypass weaknesses for pre-deployment remediation.

Mirror DiscoveR continuously runs automated red teaming to uncover AI vulnerabilities and validate security controls.

garak probes language-model endpoints across attack classes including jailbreaks, prompt injection, and data leakage to identify model security weaknesses.

OpenAI Frontier integrates automated security testing and red teaming to evaluate AI coworkers for prompt injections, jailbreaks, data leaks, tool misuse, and out-of-policy behaviors.

Promptfoo executes automated adversarial test fixtures and benchmark assertions against LLM endpoints to evaluate resistance to prompt injection, toxicity, and system prompt extraction.

Promptfoo simulates multi-turn adaptive attacks, autonomous agent goal hijacking, tool execution vulnerabilities, and unauthorized function calls in pre-deployment CI/CD environments.

ModelScan statically analyzes serialized ML model files (PyTorch, Pickle, SavedModel, Keras) to detect embedded code execution exploits and malicious payload injections.

Prisma AIRS simulates real-world attacks against single-agent and multi-agent AI systems to identify weaknesses before production.

Straiker Ascend AI runs automated adversarial testing for prompt injection, MCP tool misuse, agentic exploits, and data exfiltration risks.

TrendAI combines AI Scanner pre-deployment testing with AI Guard runtime controls to detect vulnerabilities and reduce sensitive data leakage in AI applications.

Verno Labs performs automated adversarial testing to evaluate AI agent security weaknesses before production impact.

Votal continuously probes deployed AI guardrails with adversarial testing to identify gaps and improve policy coverage.

0DIN AI Security Scanner evaluates LLM and GenAI application behavior against exploit and jailbreak test suites.

0DIN crowdsources and validates exploit reports targeting chatbots, LLM applications, and autonomous AI agents.

Zscaler AI Security runs configurable attack simulations to identify AI vulnerabilities and provide remediation guidance before and during deployment.