AI app for it and development · no coding needed
AI agent action guard and audit workspace
Reduce exposure to risky AI agent actions while keeping a reviewable record of every decision.
Made for: Security and platform engineers running AI agents and MCP-connected tools inside their own systems
What it does for you
The problem
AI agents and their tool calls can execute risky commands, leak secrets or be manipulated by prompt injection, and teams lack one place to intercept, decide, audit and report on those actions.
What it gives you
Reviewed allow, block or approval decisions with evidence tied to a specific agent version
What you give it
Agent tool callslocal trafficpolicy rulesaudit simulations
Build your own version of HOL Guard, MCP Defender and more
One app with what these 3 AI tools do, yours to keep and change: HOL Guard, MCP Defender, iFixAi.
Everything these tools do, in one app
- Real-time action interception Stops or inspects AI agent actions before they are executed.Found in HOL Guard, MCP Defender
- Threat detection Identifies malicious activities such as prompt injection, credential theft, and unauthorized code execution.Found in HOL Guard, MCP Defender
- Block or allow control Lets users or policies block or allow suspicious tool calls.Found in HOL Guard, MCP Defender
- Local-only operation Runs entirely on the user's machine without sending data externally.Found in HOL Guard
- Deterministic policy enforcement Applies fixed security rules without relying on live model reasoning for decisions.Found in HOL Guard
- Structured command parsing Distinguishes between execution, dry runs, and quoted examples to reduce false alarms.Found in HOL Guard
- Three-tier action handling Automatically allows known-safe work, blocks clear threats, and requests approval for ambiguous high-impact actions.Found in HOL Guard
- Local evidence storage Stores analytics showing which tool calls were risky, why a rule matched, and whether actions were blocked or approved.Found in HOL Guard
- Security heuristics Uses hundreds of rules covering package safety, prompt injection, secret exfiltration, and catastrophic filesystem or database operations.Found in HOL Guard
- Open-source codebase Allows teams to audit and modify the rules, parsers, and test corpus.Found in HOL Guard, MCP Defender
- Automatic traffic proxying Proxies and scans communications between AI apps and the system.Found in MCP Defender
- Real-time alerts Notifies users of suspicious behavior and allows them to control access.Found in MCP Defender
- LLM-based scanning Uses large language models to identify potential threats.Found in MCP Defender
- Log history Provides a record of past alerts for review.Found in MCP Defender
- Multi-platform support Works on multiple operating systems, with current and planned support.Found in MCP Defender
- AI agent auditing Runs audits to assess AI agents for misalignment and failures.Found in iFixAi
- Simulation environment Models the agent's expected workflows, roles, rules, permissions, and tool calls.Found in iFixAi
- Independent AI judging Evaluates results using models the agent under audit never runs on.Found in iFixAi
- Compliance mapping Maps identified gaps to frameworks like EU AI Act, NIST AI RMF, OWASP Top 10, and ISO/IEC 42001.Found in iFixAi
- Severity scoring Assigns weighted severity levels to findings based on simulation context and misalignment category.Found in iFixAi
- Evidence reporting Produces concrete evidence tied to specific agent versions for engineers to act on.Found in iFixAi
How it works, step by step
- Intercept AI agent tool calls before execution
- Detect prompt injection, credential theft and unauthorized code execution
- Apply fixed policy rules to block or allow each call
- Request human approval for ambiguous high-impact actions
- Parse commands to separate execution, dry runs and quoted examples
- Proxy and scan traffic between AI apps and the system
- Run entirely on the user's machine without external data transfer
- Store local evidence of risky calls, matched rules and decisions
- Maintain hundreds of heuristics for package safety, secret exfiltration and destructive filesystem or database operations
- Send real-time alerts and let users control access
- Keep a searchable log history of past alerts
- Simulate the agent's workflows, roles, rules, permissions and tool calls
- Run independent AI judging on models the audited agent never uses
- Score finding severity from simulation context and misalignment category
- Map gaps to EU AI Act, NIST AI RMF, OWASP Top 10 and ISO/IEC
- Export an evidence report tied to a specific agent version
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed allow, block or approval decisions with evidence tied to a specific agent version with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for AI agent action guard and audit workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds AI agent action guard and audit workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare27 KB
- prompt-vps.mdThe same build on your own server (Docker)27 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
- demo/index.htmlThe working demo on sample data202 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce exposure to risky AI agent actions while keeping a reviewable record of every decision. For security and platform engineers running AI agents and MCP-connected tools inside their own systems, convert agent tool calls, local traffic, policy rules and audit simulations into reviewed allow, block or approval decisions with evidence tied to a specific agent version. The benefit is a testable hypothesis, measured through blocked risky actions before execution and reviewer time per resolved alert; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect agent tool calls, local traffic, policy rules and audit simulations, then follow this sequence: 1. Intercept AI agent tool calls before execution. 2. Detect prompt injection, credential theft and unauthorized code execution. 3. Apply fixed policy rules to block or allow each call. 4. Request human approval for ambiguous high-impact actions. Resolve uncertain cases with qualified reviewers, approve reviewed allow, block or approval decisions with evidence tied to a specific agent version, and measure blocked risky actions before execution and reviewer time per resolved alert against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed agent version and rule set; final security decisions and compliance judgments remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve agent version, source attribution, rule accuracy and usage permissions. Security owners approve substantive changes and enforcement scope. One fixed agent version and rule set; final security decisions and compliance judgments remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One fixed agent version and rule set; final security decisions and compliance judgments remain human. Implement one approved input format, a bounded representative case set and the first two task modules: intercept AI agent tool calls before execution; detect prompt injection, credential theft and unauthorized code execution. Support the third module with operator review: apply fixed policy rules to block or allow each call. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Agent-owned tool calls, authorized local traffic and permitted policy sources. Local log storage, SIEM export and ticketing destinations. Start with file exchange and validate destination specifications before promising direct enforcement. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Policy and rules, Live action review, Audit and evidence report. Use a queue of intercepted tool calls, a large central detail view showing the parsed command, matched rule and proposed decision, and a right-hand panel for policy, simulation context and comments. Let users compare a dry run against an execution and a quoted example. Display allowed, blocked, approval requested and approved states. Provide a client or auditor preview link with findings anchored to the relevant agent version. Make the task-specific outcome reviewed allow, block or approval decisions with evidence tied to a specific agent version visible beside its evidence, review state and value baseline.
