Employees no longer need engineering teams to build software. With AI app builders, coding assistants, and autonomous agents, business users can create internal applications, automate workflows, connect databases, and interact with enterprise systems through natural language. A marketing employee can build a reporting application, while a finance team can create an automation that processes sensitive business information.
These capabilities accelerate development but create a security problem that traditional application controls do not fully address. AI-generated applications may access sensitive data, install unverified dependencies, execute commands, or interact with external services without following established security reviews. For CISOs, the challenge is enabling AI-driven innovation without allowing employees to introduce uncontrolled risks.
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1. Pluto Security
Pluto Security is an AI Workspace Security platform designed to protect the tools employees use, the ecosystem those tools connect to, and the applications and agents they create. Its approach addresses the security challenges that emerge when AI-powered development expands beyond engineering teams into everyday enterprise workflows.
Rather than treating AI builders as isolated applications, Pluto examines the relationships between users, endpoints, AI tools, connected services, and generated artifacts. This allows security teams to understand not only which builders employees use, but also what those builders can access, what they install, and which actions they execute.
The platform focuses on four interconnected security challenges: uncontrolled AI tool adoption, risky runtime execution, AI supply chain exposure, and ungoverned AI-generated applications.
Pluto’s architecture combines discovery, contextual risk assessment, and runtime policy enforcement. Its controls are designed to allow approved AI activities while preventing specific actions that violate enterprise security requirements.
For example, a company may allow employees to build internal applications using Lovable or Claude Code while restricting access to sensitive databases, untrusted MCP servers, or unauthorized external destinations. Pluto helps security teams apply those distinctions without imposing an organization-wide ban on AI builders.
Key capabilities include:
- Explorer: Discovers AI tools, agents, applications, skills, extensions, and related activity across supported enterprise environments.
- Risk Radar: Evaluates identified risks using contextual information about tools, configurations, permissions, connections, and accessible data.
- Space Shield: Enforces granular runtime policies that can restrict risky actions without blocking legitimate AI workflows.
- AI supply chain protection: Assesses MCP servers, plugins, skills, packages, and connectors before or during enterprise adoption.
- AI-generated artifact visibility: Helps identify applications, agents, and automations created through AI-assisted workflows.
- Intent-based controls: Evaluates AI actions in context to support decisions about permitted and prohibited behavior.
2. Zenity
Zenity provides an AI agent security and governance platform designed to protect autonomous agents and AI-powered applications across enterprise SaaS, cloud, and development environments. The platform addresses the risks associated with agents that can independently interact with applications, retrieve information, and perform actions on behalf of employees.
Zenity’s approach centers on understanding an agent’s permissions, connected resources, and runtime behavior. This is particularly relevant for AI builders because a generated application may include agents that access enterprise data or execute workflows across multiple systems.
Key capabilities include:
- AI agent discovery: Identifies agents across supported SaaS, cloud, and custom development environments.
- Agent security posture management: Evaluates agent configurations, permissions, connections, and exposure.
- Runtime protection: Monitors agent actions and detects potentially harmful behavior.
- Policy enforcement: Applies security controls to agent activities and connected resources.
- AI threat detection: Identifies risks such as prompt injection and unauthorized tool usage.
- Agent relationship mapping: Provides visibility into connections between agents, users, data, and enterprise applications.
- Governance reporting: Supports investigations and reviews of agent security risks.
3. Noma Security
Noma Security provides an enterprise AI security platform that helps organizations discover, assess, test, and protect AI applications and agents across different deployment environments. Its platform supports AI systems developed by engineering teams, agents created through enterprise SaaS platforms, and AI tools operating on employee endpoints.
Noma’s approach is based on the recognition that AI agents may operate across several environments simultaneously. An employee could run a coding assistant locally, connect it to an external MCP server, and use that connection to access enterprise infrastructure.
Key capabilities include:
- AI asset discovery: Identifies agents, models, MCP servers, and tools across supported environments.
- AI security posture management: Evaluates configurations, permissions, and potential exposure.
- Agent control plane: Provides centralized policies for AI agent access and permitted actions.
- Automated AI red teaming: Tests agents and applications against adversarial interactions.
- Runtime threat detection: Monitors agent behavior for security threats and policy violations.
- MCP security: Assesses connections between AI agents and external tools.
- AI governance: Supports inventory, policy management, and ongoing security oversight.
4. Lasso Security
Lasso Security provides an AI security platform that combines AI usage monitoring, agent governance, adversarial testing, and runtime protection. The platform addresses security risks across employee AI applications, custom AI agents, and connected enterprise systems.
For AI builders, Lasso focuses on the risks introduced when agents receive access to business information and external tools. An employee-created agent may be able to retrieve data, execute API requests, or modify records according to instructions that are not always predictable.
Key capabilities include:
- AI agent discovery: Identifies agents and their relationships with connected tools and enterprise data.
- Agent risk scoring: Evaluates permissions, access paths, and potential exposure.
- Runtime security: Monitors AI interactions and agent actions for suspicious behavior.
- Intent-aware governance: Examines the purpose and context of agent activity.
- MCP and tool security: Assesses connected tools and enforces policies for agent interactions.
- Automated red teaming: Tests AI applications and agents against adversarial techniques.
- Sensitive data protection: Helps prevent unauthorized disclosure of enterprise information.
5. Prompt Security
Prompt Security provides enterprise security controls for generative AI tools, coding assistants, and agents used by employees. Its platform focuses on discovering AI activity, identifying sensitive information exposure, and enforcing security policies where employees interact with AI applications.
Prompt Security uses browser-based and endpoint-oriented controls to monitor AI usage across supported tools. This is relevant for AI builders because employees frequently use browser applications and local coding assistants to create workflows without centralized security approval.
Key capabilities include:
- Shadow AI discovery: Identifies AI applications and tools used across supported employee environments.
- Browser-level protection: Monitors interactions with web-based AI applications.
- Endpoint AI security: Extends controls to local AI assistants and agentic tools.
- Sensitive data detection: Identifies confidential information shared through AI interactions.
- Data redaction: Helps remove protected information before it reaches external AI services.
- Runtime policy enforcement: Blocks or restricts interactions that violate defined security requirements.
- Employee guidance: Provides contextual warnings and policy information during AI use.
6. WitnessAI
WitnessAI provides AI security and governance capabilities for organizations managing employee AI usage, enterprise applications, and autonomous agents. The platform combines visibility, policy controls, and runtime protection to help security teams understand how AI systems interact with enterprise data and connected services.
WitnessAI uses network-level integrations and AI-specific analysis to identify AI activity across supported applications and agents. Its approach allows organizations to establish centralized controls without requiring every AI application to implement a separate security policy.
Key capabilities include:
- AI activity discovery: Identifies employee and agent use of supported AI tools.
- Network-level visibility: Monitors AI interactions through supported enterprise network integrations.
- Agentic Control: Provides visibility into AI agents, MCP servers, and connected tools.
- Intent-based policy enforcement: Evaluates AI interactions according to context and organizational requirements.
- Sensitive data protection: Supports controls that prevent confidential information from being disclosed.
- Runtime threat prevention: Detects and blocks selected prompt injection and other AI-specific threats.
- Unified governance: Centralizes monitoring and policies across employee AI usage and agent activity.
7. Aim Security
Aim Security provides an enterprise AI security platform designed to help organizations identify AI usage, assess application risks, and apply security protections across AI-powered environments. Its capabilities address employee use of generative AI, internally developed AI applications, and AI agents interacting with enterprise data.
Aim Security combines visibility into AI adoption with protection for applications developed or deployed by enterprise teams. This includes evaluating risks associated with data access, model interactions, and potentially harmful AI-generated behavior.
Key capabilities include:
- AI discovery: Identifies AI tools and applications used across supported enterprise environments.
- AI usage governance: Applies policies to employee interactions with generative AI services.
- Sensitive data protection: Detects and helps prevent confidential information exposure.
- AI application security: Supports protection for internally developed AI-powered applications.
- AI agent security: Addresses selected risks associated with autonomous AI workflows.
- AI-specific threat detection: Identifies suspicious prompts and model interactions.
- Security policy management: Provides centralized controls for enterprise AI adoption.
AI Builder Security Platforms Compared
The seven platforms address overlapping risks but differ in their primary control points. Some focus on the employee workspace, while others concentrate on AI agent behavior, application security, or enterprise-wide AI interactions.
| Platform | Primary security approach | Main capabilities | Distinguishing focus |
| Pluto Security | AI workspace and endpoint security | Tool discovery, supply chain assessment, artifact visibility, runtime controls | Securing what employees build with AI across tools, connections, and generated artifacts |
| Zenity | AI agent security and governance | Agent discovery, posture management, runtime policies | Governance of agents created across SaaS and cloud environments |
| Noma Security | Unified AI application and agent security | Discovery, posture management, red teaming, runtime detection | Security across custom, SaaS, and endpoint agents |
| Lasso Security | Agentic AI security | Agent discovery, intent-aware monitoring, MCP governance | Protection of agent actions and connected tools |
| Prompt Security | Employee AI usage security | Browser controls, endpoint monitoring, data protection | Governance of employee interactions with AI tools |
| WitnessAI | AI security and governance | Network visibility, agent controls, runtime protection | Centralized governance across human and agent activity |
| Aim Security | Enterprise AI security | AI discovery, data protection, application security | AI adoption controls and protection of developed applications |
How to Build an Enterprise AI Builder Security Policy
An effective AI builder security policy should define what employees can create, which systems they can connect to, and what happens when an application requires elevated permissions.
Instead of writing a single rule permitting or prohibiting AI builders, organizations can establish requirements according to the sensitivity of the information and actions involved.
A practical policy framework includes:
- Approved tools: Identify AI builders, coding assistants, and agent platforms employees may use.
- Permitted data: Define which categories of company information can be processed through each tool.
- Approved connections: Maintain requirements for MCP servers, plugins, external APIs, and other integrations.
- Application ownership: Require employees or teams to identify who is responsible for the applications they generate.
- Sensitive operations: Establish additional controls for financial transactions, privileged system access, and confidential information.
- Review and monitoring: Determine which applications need security approval and how their runtime behavior will be monitored.
The policy should also consider changes after initial approval. An application may be created for a limited purpose and later connected to additional systems, increasing its potential impact. Continuous visibility and runtime controls help organizations respond to these changes without requiring every low-risk application to undergo a full traditional software review.
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Measuring the Effectiveness of AI Builder Security
Counting blocked AI applications is not an adequate measure of security effectiveness. An enterprise might block dozens of tools while employees continue to create unmonitored agents through approved environments.
A stronger measurement approach considers visibility, risk reduction, policy enforcement, and the ability to enable legitimate business activity.
| Metric | What it indicates |
| AI builder discovery coverage | How much employee AI creation activity is visible |
| Unapproved connector usage | Exposure associated with unvetted MCP servers, plugins, and integrations |
| Artifact ownership coverage | Percentage of discovered AI-built applications with identified owners |
| High-risk permissions | AI tools and agents with excessive access to data or systems |
| Prevented risky actions | Policy violations blocked before execution |
| Policy exception rate | Frequency of activities requiring additional review |
| Approved AI adoption | Employee use of sanctioned tools and workflows |
| Time to assess AI tools | Operational effort required to approve new AI capabilities |
Frequently Asked Questions
Why do enterprises need security for AI app builders?
AI app builders allow employees to create software and connect business systems without conventional engineering processes. This can introduce risks involving sensitive data, untrusted dependencies, excessive permissions, and unauthorized actions. Security platforms help organizations identify what employees are creating, evaluate the associated connections, and enforce policies without necessarily prohibiting AI-assisted development.
What is the difference between AI builder security and AI agent security?
AI builder security addresses the broader environment used to create applications and automations, including development tools, plugins, dependencies, generated artifacts, and runtime activity. AI agent security focuses more specifically on autonomous systems that make decisions and perform actions. The categories overlap because many AI builders generate agents or use agents to complete development tasks.
Can AI builder security platforms detect shadow AI applications?
Some platforms can identify unapproved AI tools and applications created through employee workflows. Discovery capabilities vary according to deployment methods, endpoint coverage, supported integrations, and visibility into generated artifacts. Pluto Security focuses on identifying AI tools, their ecosystems, and AI-generated applications, helping security teams understand activity that may fall outside conventional SaaS inventories.
How can enterprises secure MCP servers used by AI builders?
Enterprises can evaluate MCP servers according to their origin, permissions, capabilities, and access to sensitive resources. Security controls may include approved catalogs, connection restrictions, runtime monitoring, and policy enforcement for specific tool actions. Organizations should also reassess MCP integrations when their permissions or behavior change, because a previously approved connection can introduce new risks.
