Summary: AI security solutions defend AI models, apps, and agents from prompt injection, data leakage, and model theft. Best for: Radware (runtime LLM/agent protection), Palo Alto Prisma AIRS (lifecycle), Wiz (AI-SPM), HiddenLayer (model security).
What are AI Security Solutions?
AI security solutions provide specialized protection for Artificial Intelligence models, data, and infrastructure, covering threats like prompt injections, data poisoning, and model theft. Key solutions include AI-SPM (Security Posture Management), LLM firewalling, and automated adversarial testing.
Key components of AI security solutions:
- AI security posture management (AI-SPM): Tools map the AI pipeline to detect risks, such as misconfigured models or unauthorized access.
- LLM firewalls and guardrails: Solutions filter inputs/outputs to prevent prompt injection, PII leakage, and jailbreaking.
- AI red-teaming and assessment: These capabilities offer adversarial simulations to test model robustness and uncover vulnerabilities before deployment.
- Runtime protection: Securely monitoring agent behavior to detect and stop abnormal actions in real time.
In this article:
The table below summarizes the key differences between the solutions covered in this article, including the category each fits into, who it suits best, its main strengths, and the trade-offs to weigh. We explore each solution in more detail in the sections that follow.
| Category |
Solution |
Best For |
Key Strengths |
Things to Consider |
| AI Application & LLM Protection |
Radware |
Runtime protection of LLM apps and AI agents |
Prompt-level LLM firewall plus agent behavior monitoring |
Initial setup and tuning can take time for small teams |
| AI Application & LLM Protection |
Palo Alto Networks Prisma AIRS |
Securing the full AI lifecycle across build and runtime |
Model scanning, red teaming, and runtime protection in one |
Enterprise quote-based pricing, no free tier |
| AI Application & LLM Protection |
Cisco AI Defense |
Network-layer AI protection within Cisco Security Cloud |
Agentless guardrails, validation, and supply chain checks |
Best value inside the Cisco security ecosystem |
| AI Application & LLM Protection |
Check Point |
Protecting employee AI use, AI apps, and AI agents |
Shadow AI discovery, DLP, and Lakera-powered red teaming |
Capabilities split across separate modules |
| AI Application & LLM Protection |
Akamai Firewall for AI |
Filtering prompts and responses at the network edge |
Inbound and outbound LLM filtering via edge or API |
Scope is prompt and response filtering, not full AI-SPM |
| AI Security Posture Management (AI-SPM) & Discovery |
Wiz |
Agentless AI posture management across cloud environments |
AI-BOM discovery with attack path analysis on one graph |
Breadth and data depth bring a learning curve |
| AI Security Posture Management (AI-SPM) & Discovery |
Noma Security |
AI-SPM with runtime protection and agent access control |
Discovery, red teaming, and runtime defense, 80+ integrations |
Pricing is sales-gated and the vendor is newer |
| AI Security Posture Management (AI-SPM) & Discovery |
Cranium |
AI exposure management and third-party AI risk |
Attack surface mapping with a dedicated red-team arena |
Leans to governance and posture over inline blocking |
| AI Red Teaming & Model Security |
HiddenLayer |
Model scanning and runtime defense for ML assets |
Agentless model scanning, AIDR, and attack simulation |
Deployment can require ML and infra engineers |
| AI Red Teaming & Model Security |
Mindgard |
Automated red teaming across the AI attack surface |
Autonomous red teamer with runtime detection and response |
Offensive testing focus, runtime layer is newer |
| AI Red Teaming & Model Security |
Enkrypt AI |
Red teaming and guardrails for AI apps and agents |
Continuous red teaming, runtime guardrails, MCP scanning |
App and agent layer focus, not cloud posture |
| Agentic AI Security |
Zenity |
Securing enterprise AI agents and copilots |
Agent observability, posture, and step-level detection |
Coverage centers on major SaaS agent platforms |
| Agentic AI Security |
Straiker |
Runtime protection for autonomous AI agents |
Agent discovery, red teaming, and runtime guardrails |
Commercially launched in 2025, short track record |
| Agentic AI Security |
BeyondTrust |
Identity and privilege control for AI agents |
Privilege discovery and least-privilege access for agents |
Identity lens, not an inline AI content firewall |
AI Security Posture Management (AI-SPM)
AI security posture management (AI-SPM) focuses on continuously assessing and improving the security stance of an organization's AI assets. It inventories AI models, tracks their configurations, and evaluates compliance with internal policies and external regulations.
AI-SPM tools identify misconfigurations, risky model behaviors, and vulnerabilities in training data, ensuring that AI systems do not expose sensitive information or become targets for adversarial manipulation. AI-SPM also automates risk assessment processes and provides recommendations for remediation.
LLM Firewalls and Guardrails
LLM firewalls and guardrails are controls that protect large language models (LLMs) from prompt injection, data leakage, and misuse. These solutions monitor and filter user inputs and outputs to prevent malicious or unintended behavior, such as leaking confidential data, generating harmful content, or executing unauthorized actions.
They can block or sanitize risky prompts in real time, reducing the risk of LLM exploitation by threat actors. Guardrails extend beyond simple filtering by enforcing usage policies, ethical guidelines, and context-sensitive rules for AI interactions. They can restrict topics, limit exposure to sensitive information, and ensure that generated content meets organizational standards.
AI Red-Teaming and Assessment
AI red-teaming involves simulating adversarial attacks and probing AI systems for weaknesses, similar to penetration testing in traditional cybersecurity. This process identifies vulnerabilities in model logic, training data, and system integrations that attackers could exploit. Red-teaming exercises can uncover issues such as susceptibility to data poisoning, adversarial input manipulation, or unauthorized access to model outputs.
Assessment tools complement red-teaming by providing automated analysis and reporting on AI system security. They benchmark models against known attack techniques, evaluate resilience to common threats, and generate recommendations for strengthening defenses. Regular red-teaming and assessment help AI deployments withstand evolving attack methods.
Runtime Protection
Runtime protection for AI systems monitors models and applications in real time to detect and block threats as they occur. This includes observing model inputs and outputs for anomalies, enforcing access controls, and intercepting attempts at prompt injection or unauthorized data extraction.
Runtime protection solutions provide immediate response capabilities, reducing the window of opportunity for attackers to exploit vulnerabilities in production environments. By integrating with AI workflows and infrastructure, runtime protection tools deliver continuous defense without disrupting business operations. They can adapt to new threats as they emerge, using machine learning to improve detection accuracy over time.
1. Prompt-Level Protection
Prompt-level protection focuses on analyzing and controlling interactions between users and AI models. These capabilities inspect prompts for malicious instructions, prompt injection attempts, jailbreak techniques, and other manipulations designed to bypass model safeguards. Effective solutions can detect suspicious patterns, sanitize inputs, and enforce security policies before requests reach the model.
Advanced prompt protection also evaluates model responses before they are returned to users. This helps prevent the disclosure of sensitive information, execution of prohibited actions, or generation of unsafe content. Organizations should look for solutions that provide real-time inspection, customizable policies, and support for multiple AI models and applications.
2. Data Leakage Prevention
Data leakage prevention capabilities help ensure that sensitive information is not exposed through AI systems. These solutions inspect prompts, model outputs, training datasets, and retrieval sources for personally identifiable information (PII), intellectual property, credentials, and other confidential data. When sensitive content is detected, policies can mask, redact, block, or alert security teams.
Strong data protection features should operate across the entire AI lifecycle, including training, inference, and integrations with external data sources. Integration with existing data governance and security tools can improve visibility and help organizations maintain compliance with regulatory requirements and internal security policies.
3. Agentic AI Protection
Agentic AI systems can perform actions, interact with external tools, and make decisions with limited human intervention. Protecting these systems requires controls that monitor agent behavior, validate tool usage, and enforce operational boundaries. Security solutions should verify that agents only access authorized resources and execute approved actions.
Organizations should also look for capabilities that detect abnormal behavior, excessive autonomy, and attempts to manipulate agent workflows. Runtime monitoring, policy enforcement, and action-level approvals can reduce the risk of agents performing unintended operations or being exploited through indirect prompt injection attacks.
4. Access Control and Identity
Access control and identity management ensure that only authorized users, applications, and services can interact with AI resources. These capabilities apply authentication and authorization policies to models, datasets, APIs, and development environments. Role-based access controls help limit privileges according to job responsibilities and reduce the risk of unauthorized access.
Modern AI environments often involve multiple models, cloud services, and data repositories. Security solutions should provide centralized identity management, support for single sign-on, and integration with existing identity providers. Detailed permissions and least-privilege access models help minimize exposure if accounts are compromised.
5. Monitoring and Audit Logs
Monitoring and audit logging provide visibility into how AI systems are used and how they respond to requests. These capabilities capture information about prompts, outputs, user activity, policy violations, model changes, and system events. Continuous monitoring helps security teams detect suspicious behavior, investigate incidents, and identify emerging risks.
Comprehensive audit trails also support compliance and governance requirements. Organizations should look for solutions that offer searchable logs, long-term retention, alerting, and integration with security information and event management (SIEM) platforms. Detailed records make it easier to understand model behavior and demonstrate adherence to security policies.
How we selected these tools: We shortlisted AI security solutions based on their ability to protect AI models, applications, and agents against threats such as prompt injection, data leakage, model theft, and unsafe agent actions, spanning posture management, runtime protection, red teaming, and agentic security.
AI Application and LLM Protection
1. Radware
Best for: Runtime protection of LLM apps and AI agents
Strengths: Prompt-level LLM firewall plus agent behavior monitoring
Things to consider: Initial setup and tuning can take time for small teams
Radware approaches AI security at two layers: the prompts flowing into large language model applications and the behavior of AI agents at runtime. Its LLM Firewall inspects and filters prompts and responses, blocking attempts at prompt injection, jailbreaks, and resource abuse before they reach the model. It also applies controls for sensitive data and PII to limit data exfiltration, with support for regulatory requirements such as GDPR and HIPAA. The firewall is delivered as an add-on to Radware Cloud Application Protection Services and works across different model providers.
Key features include:
- LLM Firewall: Inspects and filters prompts and model responses to block prompt injection, jailbreak attempts, and resource abuse before requests reach the language model.
- Data leakage controls: Applies PII and sensitive data policies to reduce data exfiltration through AI applications, with support for compliance frameworks including GDPR and HIPAA.
- Agent discovery and visibility: Identifies homegrown and SaaS-based AI agents across the environment so security teams can see which agents are running and what they can access.
- Intent-based runtime security: Uses behavioral algorithms to evaluate agent actions at runtime, including multi-step and cross-agent activity, to detect deviations from expected behavior.
- Deep platform integration: Connects with Microsoft 365 Copilot, Copilot Studio, and AWS Bedrock to apply protection within widely used AI environments.
- Continuous posture mapping: Maintains a dynamic Risk Graph Map of agents and their relationships to track exposure as the environment changes.
Limitations (as reported by users on G2):
- Initial configuration effort: Initial setup and fine-tuning can require time and planning, which smaller teams may find demanding at first.
- Advanced customization: Some granular customization options can require support involvement to configure as intended.
- Reporting workflow: Reporting and analytics can involve switching between views, and some users would prefer a more streamlined experience.
2. Palo Alto Networks Prisma AIRS
Best for: Securing the full AI lifecycle across build and runtime
Strengths: Model scanning, red teaming, and runtime protection in one
Things to consider: Enterprise quote-based pricing, no free tier
Prisma AIRS is Palo Alto Networks' AI security platform, organized around three activities: discover, assess, and protect. It aims to cover AI applications, models, and agents across their lifecycle rather than at a single point. The platform discovers AI usage across cloud, SaaS, and endpoints, including shadow AI, then assesses models and applications for weaknesses before they reach production. At runtime it inspects traffic to and from models to catch a range of threats.
Key features include:
- AI runtime security: Inspects live model traffic to detect prompt injection, malicious code, toxic content, data leaks, model denial of service, and hallucinated output.
- Automated AI red teaming: Runs multi-turn and multi-agent adversarial tests against AI applications to surface weaknesses before deployment.
- AI model security: Scans third-party and open-source models for tampering, malicious scripts, unsafe deserialization, and neural backdoors.
- Agent security: Verifies the identity of AI agents and scans agent artifacts and connected Model Context Protocol servers.
- AI posture management: Assesses configuration and risk across AI assets to identify exposure in the environment.
- Shadow AI discovery: Detects unsanctioned AI usage across cloud, SaaS, and endpoints to bring it under management.
Limitations (based on publicly available sources):
- Inline latency: Runtime inspection adds processing time to model requests, which teams need to account for in latency-sensitive applications.
- Red teaming coverage: Automated red teaming provides partial coverage of possible attacks and is intended to complement, not replace, human testing.
- Enterprise pricing: Pricing is quote-based with no free tier, which can complicate evaluation for smaller teams.
- Deployment constraints: Traffic is processed regionally and capacity is governed by per-resource limits that require planning at scale.
3. Cisco AI Defense
Best for: Network-layer AI protection within Cisco Security Cloud
Strengths: Agentless guardrails, validation, and supply chain checks
Things to consider: Best value inside the Cisco security ecosystem
Cisco AI Defense secures AI applications and agents from within the Cisco Security Cloud, applying protection at the network layer without requiring agents on endpoints. It builds an inventory of models, applications, agents, and Model Context Protocol servers, then validates and protects them across development and runtime. Because it operates as part of Cisco's broader security platform, it shares threat intelligence from Cisco Talos and aligns its controls to recognized frameworks.
Key features include:
- AI cloud visibility: Builds an inventory of models, applications, agents, and Model Context Protocol servers in use across the environment.
- Model and application validation: Runs algorithmic red teaming that can be embedded in CI/CD pipelines to test AI applications before release.
- AI runtime protection: Applies network-embedded guardrails for prompt injection, model denial of service, code detection, off-topic prompts, and data leakage.
- AI supply chain risk management: Scans model files, repositories, and MCP servers to identify risk in the AI supply chain.
- AI access controls: Governs employee use of third-party AI applications to limit unsanctioned access.
- Threat intelligence and framework mapping: Draws on Cisco Talos intelligence and maps controls to NIST, MITRE ATLAS, and the OWASP LLM Top 10.
Limitations (based on publicly available sources):
- Ecosystem fit: The product delivers the most value to organizations already using the Cisco Security Cloud.
- On-premises hardware: On-premises deployment runs on Cisco UCS hardware, which adds an infrastructure requirement.
- Enterprise pricing: Pricing is based on applications and usage and is quote-based at the enterprise level.
- Red teaming scope: Algorithmic red teaming complements rather than replaces human-led testing.
4. Check Point
Best for: Protecting employee AI use, AI apps, and AI agents
Strengths: Shadow AI discovery, DLP, and Lakera-powered red teaming
Things to consider: Capabilities split across separate modules
Check Point approaches AI security across three audiences: the workforce using AI tools, the applications an organization builds, and the agents it deploys. For the workforce, it discovers shadow AI use, detects sensitive data pasted into AI tools, and applies data loss prevention across browsers, SaaS applications, and copilots. For applications, it protects model inputs and outputs at the application edge. For agents, it controls tool calls, file access, and runtime behavior.
Key features include:
- Workforce AI protection: Discovers shadow AI use, detects sensitive data pasted into AI tools, and applies DLP across browsers, SaaS apps, and copilots.
- Application protection: Inspects and protects model inputs and outputs at the application edge to limit misuse of deployed AI applications.
- Agent controls: Governs agent tool calls, file access, and runtime behavior to constrain what deployed agents can do.
- AI red teaming: Runs adversarial tests mapped to OWASP guidance to surface weaknesses in AI applications.
- Lakera-powered runtime defense: Applies AI-native runtime protection built on Lakera technology acquired by Check Point.
- Infinity integration: Integrates AI security into the Check Point Infinity platform, beginning with CloudGuard WAF.
Limitations (based on publicly available sources):
- Integration in progress: The Lakera-based capabilities are still being integrated across the Check Point portfolio.
- Platform dependency: The capabilities deliver the most value within the Check Point Infinity ecosystem.
- Modular structure: Functionality is divided across separate workforce, applications, agents, and red teaming modules, which requires assembling the relevant pieces.
5. Akamai Firewall for AI
Best for: Filtering prompts and responses at the network edge
Strengths: Inbound and outbound LLM filtering via edge or API
Things to consider: Scope is prompt and response filtering, not full AI-SPM
Akamai Firewall for AI sits in front of large language model applications and filters traffic in both directions. On the inbound path it detects and blocks prompt injection, jailbreak attempts, and adversarial inputs before they reach the model. On the outbound path it filters responses for data leaks, toxic content, hallucinations, and compliance issues. It also applies policy controls for sensitive data and PII, backed by multiple layers of guardrails.
Key features include:
- Inbound prompt filtering: Detects and blocks prompt injection, jailbreaks, and adversarial inputs before they reach the language model.
- Output filtering: Inspects model responses for data leaks, toxic content, hallucinations, and compliance issues before they return to users.
- Sensitive data controls: Applies policy controls for sensitive data and PII, supported by multiple layers of guardrails.
- Flexible deployment: Runs through the Akamai edge network or via a REST API so it can be added to existing AI applications.
- Real-time monitoring: Monitors AI traffic in real time to give visibility into prompts and responses.
- Threat intelligence updates: Adapts its detection using Akamai's threat intelligence.
Limitations (based on publicly available sources):
- Defined scope: The product covers inbound and outbound prompt and response filtering rather than full lifecycle AI-SPM or red teaming.
- Complementary product: It is designed to work alongside Akamai App and API Protector rather than as a standalone suite.
- Maturity: It is a newer offering with limited third-party reviews available.
AI Security Posture Management (AI-SPM) and Discovery
6. Wiz
Best for: Agentless AI posture management across cloud environments
Strengths: AI-BOM discovery with attack path analysis on one graph
Things to consider: Breadth and data depth bring a learning curve
Wiz, which coined the term AI-SPM and built the first CNAPP, embeds AI security posture management into its cloud security platform. It discovers AI assets agentlessly, building an AI bill of materials that covers models, services, SDKs, agents, and Model Context Protocol usage. It then detects misconfigurations, scans infrastructure as code, and applies data security posture management to identify sensitive training data. These findings are connected on the Wiz Security Graph.
Key features include:
- Agentless AI-BOM discovery: Builds an AI bill of materials covering models, services, SDKs, agents, and Model Context Protocol usage without deploying agents.
- Misconfiguration and IaC scanning: Detects misconfigurations in AI services and scans infrastructure as code to catch issues before deployment.
- DSPM for AI: Applies data security posture management to locate sensitive training data and connect it to AI usage.
- Attack path analysis: Uses the Wiz Security Graph to trace how exposed AI assets link to identities and data, prioritizing risk by impact.
- Runtime protection: Detects runtime threats such as prompt injection and rogue agent behavior.
- AI investigation agents: Lets analysts query the Security Graph in natural language to investigate AI risk.
Limitations (as reported by users on G2):
- Alert noise: Some users report false positives and alert volume, such as findings on unused libraries, that require tuning.
- Learning curve: The breadth of the platform and the depth of its data can be overwhelming for new users.
- AI policy maturity: Some users would like faster development and more out-of-the-box AI policies.
- Cost at scale: The platform centers on public cloud and premium pricing can rise as deployments grow.
7. Noma Security
Best for: AI-SPM with runtime protection and agent access control
Strengths: Discovery, red teaming, and runtime defense, 80+ integrations
Things to consider: Pricing is sales-gated and the vendor is newer
Noma Security is an independent AI security platform that spans posture management, testing, and runtime defense. It discovers and inventories AI assets, including models, agents, Model Context Protocol servers, and data sources, to give teams a current view of what is running. From there it runs automated, continuous red teaming driven by its own agents to find weaknesses across the AI estate.
Key features include:
- AI-SPM discovery: Inventories models, agents, Model Context Protocol servers, and data sources to map the AI estate.
- Automated red teaming: Runs continuous, agent-driven red teaming to surface weaknesses across AI assets.
- Runtime protection: Blocks malicious prompts, rogue outputs, and unauthorized agent actions during operation.
- Agent access control: Constrains what AI agents are permitted to do at runtime.
- Supply chain scanning: Scans the AI supply chain to identify risk in components and dependencies.
- Broad integrations: Connects through more than 80 integrations, including Copilot Studio, Salesforce AgentForce, ServiceNow, LangChain, and CrewAI.
Limitations (based on publicly available sources):
- Sales-gated pricing: Pricing is not published and requires contacting the vendor.
- Procurement effort: Enterprise procurement can involve a longer evaluation cycle.
- Vendor maturity: Noma is a newer vendor in the AI security market.
8. Cranium
Best for: AI exposure management and third-party AI risk
Strengths: Attack surface mapping with a dedicated red-team arena
Things to consider: Leans to governance and posture over inline blocking
Cranium, which originated in KPMG Studio, centers on AI exposure management across both internal and third-party AI. Its exposure management capability characterizes the AI attack surface, assesses vulnerabilities, incorporates threat intelligence, and runs an OODA-loop approach to red teaming. The goal is to give organizations a structured view of where their AI is exposed and how that exposure could be used against them.
Key features include:
- Exposure management: Characterizes the AI attack surface, assesses vulnerabilities, and incorporates threat intelligence into a continuous loop.
- Cranium Arena: Provides a dedicated environment for red teaming AI systems.
- AI Card: Documents AI systems to support visibility and governance.
- Detect AI: Identifies AI usage across the organization.
- Third-party AI visibility: Extends visibility to AI introduced through external vendors and partners.
- Governance and compliance support: Supports governance and compliance activities for AI systems.
Limitations (based on publicly available sources):
- Posture orientation: The platform leans toward governance, posture, and third-party risk rather than inline blocking.
- Emerging category: AI exposure management is an emerging category that is still maturing.
- Limited reviews: Limited public reviews are available for the product.
AI Red Teaming and Model Security
9. HiddenLayer
Best for: Model scanning and runtime defense for ML assets
Strengths: Agentless model scanning, AIDR, and attack simulation
Things to consider: Deployment can require ML and infra engineers
HiddenLayer's AISec Platform secures machine learning models and the systems around them. It is model-agnostic and agentless, and it does not require access to model weights or training data to operate. The platform discovers AI assets, including shadow AI, then secures the AI supply chain by scanning models for malware, backdoors, and vulnerable dependencies through its Model Scanner.
Key features include:
- AI discovery: Identifies AI assets across the environment, including shadow AI.
- Model scanning: Scans models for malware, backdoors, and vulnerable dependencies through the Model Scanner.
- AI runtime security (AIDR): Detects prompt injection, model extraction, and unauthorized tool use at runtime.
- Attack simulation: Runs red teaming aligned to the MITRE ATLAS framework.
- AIBOM and model genealogy: Produces an AI bill of materials and tracks model lineage.
- Pipeline and SOC integration: Integrates with CI/CD and MLOps pipelines as well as SIEM and SOAR tools.
Limitations (as reported by users on Gartner Peer Insights):
- Deployment complexity: Deployment can require machine learning and infrastructure engineering resources.
- Advanced documentation: Documentation for advanced configuration can be sparse.
- Learning curve: Some users report a learning curve when getting started.
- Pricing transparency: Pricing transparency can be limited for smaller teams.
10. Mindgard
Best for: Automated red teaming across the AI attack surface
Strengths: Autonomous red teamer with runtime detection and response
Things to consider: Offensive testing focus, runtime layer is newer
Mindgard, a spinout from Lancaster University, provides an autonomous red teaming platform built around a four-stage process: discover, recon, attack, and defend. It performs AI reconnaissance and discovery across models, agents, MCP and agent-to-agent servers, tools, and shadow AI. From there it can run agentic red teaming with a single click and chain attacks together to test how weaknesses combine.
Key features include:
- AI recon and discovery: Identifies models, agents, MCP and agent-to-agent servers, tools, and shadow AI across the environment.
- Single-click agentic red teaming: Runs autonomous red teaming against AI systems with minimal setup.
- Attack chaining: Chains attacks together to test how individual weaknesses combine.
- Runtime detection and response: Provides context guardrails and system-prompt hardening at runtime.
- Framework mapping: Maps testing and controls to the MITRE ATLAS and OWASP frameworks.
- CI/CD and tool integration: Integrates into CI/CD pipelines and works with tools including Burp Suite, with multimodal support.
Limitations (based on publicly available sources):
- Offensive orientation: The platform centers on offensive testing, and its runtime layer is newer.
- Limited public detail: Public detail on attack internals is limited, and demos are typically required to learn more.
- Vendor stage: Mindgard is a venture-stage company.
11. Enkrypt AI
Best for: Red teaming and guardrails for AI apps and agents
Strengths: Continuous red teaming, runtime guardrails, MCP scanning
Things to consider: App and agent layer focus, not cloud posture
Enkrypt AI secures AI applications and agents through a combination of testing and runtime controls. Its Agent Red Teaming runs automated, continuous, multimodal tests, while Agent Guardrails apply low-latency input and output controls during operation. The platform includes an MCP Gateway and MCP Scanner to govern and inspect Model Context Protocol usage, and an AI Data Risk Audit that examines data, fine-tunes, and embeddings.
Key features include:
- Agent red teaming: Runs automated, continuous, multimodal red teaming against AI agents and applications.
- Agent guardrails: Applies low-latency input and output controls at runtime.
- MCP gateway and scanner: Governs and inspects Model Context Protocol usage through a gateway and scanner.
- AI data risk audit: Examines data, fine-tunes, and embeddings for risk.
- Agent policy engine and asset scanner: Defines agent policies and scans AI assets across the environment.
- Compliance mapping: Maps controls to the EU AI Act, NIST AI RMF, and OWASP, with a taxonomy of six risk categories and around 300 subtypes.
Limitations (based on publicly available sources):
- Layer focus: The platform covers the application, agent, and model layers rather than cloud or infrastructure posture.
- Funding scale: The company has raised a relatively small amount of funding.
- Limited reviews: Limited public reviews are available for the product.
Agentic AI Security
12. Zenity
Best for: Securing enterprise AI agents and copilots
Strengths: Agent observability, posture, and step-level detection
Things to consider: Coverage centers on major SaaS agent platforms
Zenity focuses on the agent layer, covering both build time and runtime. Its AI Observability capability discovers and inventories agents, maps ownership and permissions, and surfaces shadow agents. Its AI-SPM capability evaluates configuration and permission risk before an agent runs, so issues can be addressed ahead of deployment. Together these give security teams a view of which agents exist and how exposed they are.
Key features include:
- AI observability: Discovers and inventories agents, maps ownership and permissions, and surfaces shadow agents.
- AI-SPM: Evaluates configuration and permission risk before an agent runs.
- AI detection and response: Inspects step-level execution for direct and indirect prompt injection, memory poisoning, tool misuse, and data exfiltration.
- Correlation agent: Works to determine the intent behind agent activity.
- Broad agent coverage: Covers SaaS-managed agents such as M365 Copilot and Salesforce Agentforce, homegrown agents on AWS Bedrock and Vertex AI, and device-based agents.
- Framework alignment: Aligns controls with OWASP, MITRE ATLAS, and NIST.
Limitations (based on publicly available sources):
- Platform coverage: Purpose-built coverage centers on platforms such as Microsoft, Salesforce, ServiceNow, and ChatGPT, with less depth for code-framework agents like LangChain and Databricks.
- Scope: The platform centers on detection, prevention, and governance rather than offensive red teaming.
- Limited reviews: Limited public reviews are available for the product.
13. Straiker
Best for: Runtime protection for autonomous AI agents
Strengths: Agent discovery, red teaming, and runtime guardrails
Things to consider: Commercially launched in 2025, short track record
Straiker is an agentic-first AI security company that launched commercially in 2025. Its Discover AI capability finds agents, monitors the tools they use, and provides visibility into Model Context Protocol activity, backed by a catalog of more than 17,000 MCP entries. Ascend AI acts as an autonomous red teamer that runs continuous adversarial testing against agents to find weaknesses.
Key features include:
- Discover AI: Finds agents, monitors tool usage, and provides MCP visibility backed by a catalog of more than 17,000 MCP entries.
- Ascend AI: Acts as an autonomous red teamer running continuous adversarial testing against agents.
- Defend AI: Applies runtime guardrails against prompt injection, data leakage, and tool manipulation, with runtime action tracing.
- MCP and tool-chain risk: Addresses risk arising from MCP and agent tool chains.
- Flexible deployment: Deploys through API monitoring or as an inline gateway, with multimodal support.
- Agent coverage: Targets coding, productivity, and custom-built agents.
Limitations (based on publicly available sources):
- Maturity: The company launched commercially in 2025 and has a short track record.
- Scope: Coverage centers on the agent layer rather than broader AI posture.
- Limited reviews: Limited public reviews are available for the product.
14. BeyondTrust
Best for: Identity and privilege control for AI agents
Strengths: Privilege discovery and least-privilege access for agents
Things to consider: Identity lens, not an inline AI content firewall
BeyondTrust approaches AI security through an identity and privilege lens, applying its Pathfinder platform and Identity Security Insights to AI agents. It discovers AI agents that hold excess privilege across environments such as OpenAI, Vertex AI, Salesforce Agentforce, ServiceNow, and AWS Bedrock, and surfaces shadow AI. Its True Privilege Graph maps escalation paths so teams can see how an over-privileged agent could be misused.
Key features include:
- AI agent privilege discovery: Identifies AI agents with excess privilege across OpenAI, Vertex AI, Salesforce Agentforce, ServiceNow, and AWS Bedrock, and surfaces shadow AI.
- True Privilege Graph: Maps escalation paths to show how over-privileged agents could be misused.
- Least-privilege controls: Applies zero standing privileges and just-in-time access for AI agents.
- Credential vaulting: Uses Password Safe to vault and rotate credentials used by agents.
- Endpoint privilege management: Governs desktop AI clients such as Claude and ChatGPT.
- Pathfinder MCP server: Extends privilege controls to Model Context Protocol environments.
Limitations (as reported by users on Gartner Peer Insights):
- Training availability: Some users report limited or infrequent training and a learning curve.
- Support experience: Some users cite support responsiveness and account management as areas for improvement.
- Large-environment tuning: Account discovery in large environments can require tuning, and active-passive setups need paired appliances.
Conclusion
AI security solutions address the unique risks introduced by AI systems, including model manipulation, data exposure, and autonomous decision-making. By combining posture management, runtime protection, and adversarial testing, these solutions provide visibility and control across the AI lifecycle. As organizations expand their use of AI, implementing dedicated security layers becomes essential to ensure safe, reliable, and compliant operation of AI-driven applications.