TL;DR
AI agent security tools fall into six categories, and each one controls a different point where an agent can go wrong. Identity tools prove who the agent is, gateways limit which tools it reaches, and policy tools decide which actions it may take. Observability tools record what it did, sandboxes contain the code it runs, and discovery tools find agents nobody registered. No single product covers all six, so buy by the gap each tool closes rather than by a vendor ranking. Start a write-capable agent with scoped identity, a gateway that enforces action policy and approval, and logs flowing to your SIEM. Add sandboxing once the agent runs code, and add discovery once your agent count outgrows a spreadsheet.
Key Takeaways AI agent security tools protect what an agent does, which is a different job from filtering what a model says. Six control points matter: identity, tool gateways, action policy and approval, observability, sandboxing and discovery. Every category has a blind spot, so judge each tool by what it does not stop as well as by what it stops. Open-source projects, agent-security platforms and cloud-native services overlap heavily, so map them to gaps before you compare vendors. Run six pilot tests before signing, from revoking a credential mid-session to calling an API around the gateway. The 2025 acquisitions of Protect AI, Lakera and Invariant Labs moved products to new owners, so ask every vendor about its roadmap and its contract terms. Watch on YouTube
Rogue AI Explained: Real Incidents, Why Agents Go Rogue & the Controls That Stop Them
Kanerika walks through real incidents where AI agents went off course, why it happens, and the identity, approval and monitoring controls that stop it.
No Single Product Secures an AI Agent, Whatever the Rankings Say
Search for AI agent security tools and you get ranked lists, nearly all written by a vendor whose product sits at number one. The ranking implies one purchase covers the problem, but it does not.
An agent fails at specific points. It can hold a credential it should never have, call a tool outside its job, send an email nobody approved, or run without anyone knowing it exists. Because each failure happens at a different layer, each layer has its own tools.
This map sorts tools by the control point each one enforces and the gap it leaves open, and no vendor paid for placement. Kanerika is a Microsoft Solutions Partner for Data and AI, so Microsoft products sit beside open-source and other commercial options, never above them. Every product fact comes from vendor documentation checked on 6 November 2026.
What Counts as an AI Agent Security Tool?
An AI agent security tool is any product or open project that controls, records or contains the actions an agent takes on real systems. That is a narrower definition than AI security in general. It also leaves out AI agents that help a security operations team triage alerts, which is a separate buying decision with separate vendors.
Agents Act, So the Control Sits Where the Action Happens
When a chatbot gets something wrong, it produces a bad answer. But an agent with tool access can turn the same mistake into a refund, a deleted record or an email to every customer. That shift is why the OWASP Top 10 for Agentic Applications appeared in December 2025. Its ten items include tool misuse (ASI02), identity and privilege abuse (ASI03) and unexpected code execution (ASI05).
Each of those risks lands at a specific enforcement point. A credential gets issued, a tool call crosses a gateway, a write reaches an API, or code runs in a container. A tool earns a place on this map only when it sits at one of those points.
Prevent, Detect, Prove
Buyers often blur three different jobs. Prevention tools stop an action before it happens, and detection tools notice a bad action while it runs or soon after. Proof tools record who did what, under whose authority, so an auditor or incident responder can rebuild the sequence later.
A tracing product that records every tool call is a proof tool. However good its dashboards look, it cannot block anything on its own. Confusing these jobs is how teams end up with excellent visibility into an incident they had no way to stop.
Where This Map Stops
Prompt and output filtering for LLM applications sits next door, and our guide to LLM security tools covers it. Guardrail design gets its own treatment in AI agent guardrails , while the wider controls architecture lives in our agentic AI security guide. This post stays with the tools that act on what agents do.
Runtime prompt-injection protection is the adjacent layer every buyer meets first, so it earns a line here. These filters screen text going into the model for instructions that try to override the developer’s own. For example, Amazon Bedrock Guardrails has a prompt attack filter configured as type PROMPT_ATTACK with an input strength from NONE to HIGH.
The same AWS page says the filter does not evaluate tool results, and that is exactly where an indirect injection reaches an agent. OWASP LLM01 also says it is unclear if fool-proof prevention exists. So its mitigations include least-privilege access and human approval for high-risk actions, which are the categories this map covers.
Adjacent layer Stops for an agent Does not stop Pair it with Prompt-injection runtime filters Jailbreaks and override attempts in user input Instructions hidden in tool results; a valid tool call made after the model is fooled Tool and MCP gateways, action approval, scoped agent identity
The Six Categories of AI Agent Security Tools at a Glance
Six categories cover the points where an agent touches the outside world. The table also lists what each one stops, what it misses, and example tools from open projects, commercial vendors and cloud providers. Vendors appear as examples inside a category rather than as a ranking.
Read the “does not stop” column first, because vendor pages tend to leave it out. That column tells you which second category you need beside the first. It also explains why a credible stack always combines several rows.
The Category Map
Category Enforcement point Stops Does not stop Open-source or self-hosted examples Commercial or cloud-native examples OWASP Agentic items Agent identity and secrets Where a credential is issued and used Shared keys, standing secrets, actions nobody can attribute A valid identity doing the wrong thing HashiCorp Vault, SPIFFE/SPIRE Microsoft Entra Agent ID, Aembit, Astrix ASI03 Tool and MCP gateways Where a tool call leaves the agent Unapproved tools, token passthrough, unscoped servers Calls that skip the gateway; a trusted server that turns malicious Docker MCP Gateway, IBM ContextForge, agentgateway Noma MCP Security ASI02, ASI04 Action policy and approval Before a write or high-risk step Out-of-policy actions, unapproved high-impact writes Approval fatigue; parameters changed after approval Open Policy Agent, LangGraph interrupts, Microsoft Agent Governance Toolkit (preview) Amazon Verified Permissions with Cedar ASI02, ASI09 Observability and detection After every step Blind investigations, slow detection Anything by itself, because a trace records rather than blocks Langfuse, OpenTelemetry GenAI conventions LangSmith, Astrix, Noma Agent Runtime Security ASI10 Sandboxing and egress Where code runs and traffic exits Host escape, data sent to unknown hosts Misuse of an allowed destination; SaaS writes through valid tokens gVisor, Firecracker E2B, Docker Sandboxes ASI05 Discovery and inventory Across the whole estate Unregistered agents, orphaned credentials What a registered agent does at runtime Few mature options Zenity, Noma AI Asset Discovery, Astrix, Microsoft Agent 365 ASI03, ASI10 Testing (not a control) Before release Finds weaknesses early Nothing in production garak, PyRIT, Promptfoo See LLM security tools All ten, as test targets
Two Patterns in the Grid
Two patterns stand out in that grid. Each category stops one class of failure and leaves at least one open. Several vendors also span three or four rows as of November 2026, which helps integration but makes the right-hand gaps easier to forget.
The testing row sits apart on purpose. Red-team scanners such as garak , PyRIT and Promptfoo find weaknesses before release, but they enforce nothing in production. Our AI red teaming guide covers them in depth.
Agent Identity and Secrets Tools
Identity tools answer the first question in any incident, because attribution comes before everything else. Which agent did this, acting for whom, and with what credential? Without them, every agent call looks like the service account it borrowed, so the audit trail ends there.
What They Stop
They stop shared API keys, long-lived secrets in config files and actions nobody can attribute. For example, the HashiCorp Vault database secrets engine generates database credentials on demand for a configured role. Each credential carries a lease, and Vault revokes it once the lease runs out.
SPIFFE takes a standards route instead. It defines a workload identity, delivered as an X.509 or JWT document through a local Workload API, and SPIRE is the implementation that issues it. SPIFFE is a general workload identity standard, so it covers agents because agents are workloads.
Agent-Specific Options
Microsoft Entra Agent ID gives AI agents their own identity accounts inside Entra ID, and Microsoft makes it available to all Entra customers. Aembit describes itself as identity and access management for agentic AI and MCP, built on secretless access. Astrix focuses on AI agents and other non-human identities, with inventory, privilege review and threat detection.
What They Do Not Stop
A correctly authenticated agent can still do the wrong thing with valid credentials. Identity proves who is acting, but it says nothing about whether this particular action is wise. That judgment belongs to the policy layer further down this map.
Our AI agent authentication guide covers token patterns in detail. For scoping what each identity may touch, the agentic AI permission control framework is the companion read.
Buyer Test for Identity Tools
First, revoke an agent’s credential in the middle of a live session. Then check whether cached tokens, refresh tokens and downstream sessions stop working within minutes. If the agent keeps acting until its next restart, revocation exists only on paper.
AI Assessment
See Where Your AI Agents Stand on Security and Governance
Score your organization’s AI maturity across data, governance and security to see which agents need identity, policy and audit controls first.
Start Your AI Assessment → Tool and MCP Gateways for AI Agents
A tool gateway sits between the agent and the tools it calls. It decides which servers an agent may reach, which tools on those servers it may use, and whose credentials travel with each call. The Model Context Protocol made this layer urgent, because one MCP connection can expose dozens of tools at once.
What the MCP Spec Already Requires
Start with the protocol, since a gateway should enforce it. The MCP authorization specification treats a protected MCP server as an OAuth 2.1 resource server. Servers must check that each token was issued for them specifically, and they must also reject invalid or expired tokens with HTTP 401.
The MCP security best practices then go further. They name confused deputy, token passthrough, server-side request forgery and session hijacking as known attacks. They also state that an MCP server must not accept any token that was not explicitly issued for it.
Gateway Options
The Docker MCP Gateway is Docker’s open source proxy between MCP clients and servers. It also manages configuration, credentials and access control in one place. IBM ContextForge reaches past MCP: it is an Apache-2.0 gateway, registry and proxy that fronts MCP, A2A, REST and gRPC APIs.
agentgateway is a Linux Foundation project that handles service, LLM and MCP traffic, with native MCP and A2A support. On the commercial side, Noma sells an MCP Security module inside its wider platform. Our Model Context Protocol guide explains how these connections work before you put a gateway in front of them.
Watch on YouTube
Can LLM Gateways Make Enterprise AI Safer?
A Kanerika discussion of what a gateway in front of AI traffic can enforce, from routing and access rules to logging, and where its reach ends.
What Gateways Do Not Stop
A gateway only sees traffic that passes through it, so its reach ends where the traffic goes around. For example, an agent holding a raw API key can call the downstream service directly and skip every rule. An approved MCP server can also turn malicious after an update, which is a provenance problem covered in our AI supply chain security guide.
Before you buy a gateway, hand the agent a direct API credential and confirm the call fails. It should fail because the downstream service only trusts tokens minted for the gateway. Our LLM gateway explainer covers the model-routing side of the same pattern.
Runtime Action Policy and Human Approval
Policy tools decide whether a specific action is allowed before it runs. They weigh who asked, what the agent wants to change and how risky the change is. Approval tools then pause the agent and ask a person when the answer depends on judgment.
A Decision Is Not Enforcement
Open Policy Agent separates policy decisions from enforcement. Your software sends OPA structured input, and OPA returns a decision written in its Rego language. So nothing gets blocked unless the calling code honors that answer.
Amazon Verified Permissions works the same way for custom applications, with policies written in Cedar. Both are strong decision engines, but neither can stop an agent that reaches a tool by a path that never asks. So wire them into the gateway or into the tool itself.
Approval in the Loop
LangGraph interrupts pause a running graph so a person can approve, reject or edit the next step, even from inside a tool. Microsoft’s Agent Governance Toolkit , MIT-licensed and in public preview as of November 2026, bundles policy enforcement, zero-trust identity and execution sandboxing for agents. Its repository says it covers all ten OWASP Agentic risks, which is a self-assessment worth testing before you rely on it.
Gaps to Plan For
Approval fatigue is the first gap, because a reviewer who sees forty prompts an hour starts clicking yes without reading. Drift is the second, when the agent changes a parameter after a person signed off on the original request.
The third is outage behavior. When the policy service goes down, writes should fail closed, even if reads keep working. Our guide to human-in-the-loop AI agents covers approval design, while AI guardrails covers the content checks that sit beside these controls.
On-Demand Webinar
The Real Cost of LLM Security Risks and How to Reduce Them
An on-demand Kanerika webinar on the AI security risks that cost enterprises the most and the practical controls that reduce them.
Watch the Webinar → Observability, Audit Trails and Anomaly Detection
Observability tools record what an agent saw, decided and did, step by step. Security teams then use those records in two ways. They catch a bad run while it happens, and they also prove afterward what took place.
Tracing Tools
Langfuse is an open source platform for observability, prompt management and evaluation, built on OpenTelemetry and documented for self-hosting. LangSmith describes traces as the record of what agents did in production. OpenTelemetry also maintains generative AI semantic conventions with agent spans and MCP conventions, which helps traces flow into the SIEM you already run.
Security Detection
Tracing tools are built for engineers chasing quality problems, while detection products read the same activity through a security lens. Astrix offers detection and response for compromised credentials and out-of-scope agent actions, and Noma sells an Agent Runtime Security module.
Fields a Security Trace Needs
A trace built for debugging rarely holds everything an investigator needs, so check that each agent action records the fields below before you call your logging finished.
Field Why it matters Agent ID Ties the action to one registered agent Delegated user Shows whose authority the agent used Tool and target Names the system and the record touched Policy decision Shows whether policy allowed the action, and under which rule Approval reference Links a high-risk step to the person who approved it Result Records success, failure or a partial change Correlation ID Joins the agent trace to gateway and SIEM logs
The approval reference and the correlation ID are the two fields teams most often skip. Without them, an investigator can see that an email went out but cannot connect it to the person who allowed it.
What a Trace Cannot Do
A trace records the damage but does not prevent it. That limit matters most for ASI10, rogue agents, where an agent keeps acting outside its intended purpose. Our rogue AI explainer and AI agent observability guide pick up the detection side. For a wider product comparison, see our roundup of AI observability tools .
Sandboxing and Egress Control for Code-Running Agents
Sandboxes matter as soon as an agent writes and runs code. Coding agents, data-analysis agents and especially anything with a shell tool can execute instructions planted in a file, a ticket or a web page. Isolation then limits what that code can reach.
Isolation Layers
gVisor is an application kernel that runs in user space between the application and the host, shipped as an OCI runtime called runsc. Its documentation also makes clear that it does more than filter system calls. Firecracker takes the virtual machine route instead, using KVM-based microVMs, and the project cites boot times under 125 milliseconds.
Agent Sandboxes
E2B provides isolated cloud sandboxes where agents run code and tools. Its network settings accept allow and deny lists for outbound traffic, so internet access can be switched off entirely. Docker Sandboxes run coding agents such as Claude Code, Codex, Copilot and Cursor in microVM isolation, with credential handling and network policies.
Coding agents deserve the strictest setup because they combine code execution, repository credentials and a habit of installing packages. Our overview of AI coding agents covers how teams roll them out.
Checklist
Enterprise Agentic AI Checklist
A practical checklist for scoping, securing and governing AI agents before they reach production, from access and approvals to monitoring.
Get the Checklist → What Sandboxes Do Not Stop
Isolation is not authorization. An agent inside a well-built sandbox can still call a SaaS API it holds a token for and delete records there. Egress allow-lists also leave allowed destinations open, so a permitted storage bucket can still receive data it should never get.
That is why sandboxing pairs with the identity and policy rows rather than replacing them. The sandbox contains the code, while scoped tokens and policy contain what the code can change elsewhere.
Agent Discovery and Inventory Tools
Discovery tools find agents, even when nobody registered them. That sounds trivial until you count the copilots built in low-code tools and the agents inside SaaS apps. Then add the scripts a team wired to an LLM last quarter. Every other category on this map assumes you already know the agent exists.
What an Inventory Record Holds
A useful record names the owner, runtime, identity, connected tools, credentials, data reached and the status of each control. If a record cannot answer who switches the agent off, it is only a list. The question gets harder with multi-agent AI systems , where one agent can start others.
Tool Options
Zenity says its Surface module builds a live inventory of agents, evaluates their configuration and tests exploitability. Noma also lists AI Asset Discovery as a module, while Astrix inventories agents, MCP servers and non-human identities. Inside Microsoft 365, Microsoft Agent 365 gives admins a central agent registry and an agent map, licensed per user.
Coverage Test
First, seed ten agents you already know about across different platforms. Then measure how many the tool finds, and how quickly. Zenity argues that cloud posture tools govern infrastructure rather than agents inside applications. A seeded test is how you check that claim against your own estate, so run it before you sign.
Unsanctioned AI use by employees is a related but separate problem, with detection tools of its own. Retiring agents and their credentials belongs to AI agent lifecycle management , which an inventory makes possible.
A Reference Stack for One Customer Support Agent
Categories make more sense when they meet one agent. Take an illustrative support agent that reads the CRM, updates tickets and sends email replies. Many teams put an agent like this live first, although it can already do real damage.
Before buying anything, threat-model it. Our AI threat modeling guide walks through that exercise step by step. The agentic AI vulnerability assessment guide then shows how to test what the model finds.
Following One Poisoned Email
Follow one attack through it. A customer email arrives with hidden text telling the agent to export every open ticket for the account to an outside address. The agent reads email as part of its job, so the instruction is already inside.
Attack step Layer that catches it Open-source pick Microsoft pick Agent tries to pull every ticket for the account Scoped identity limits what it can read Vault dynamic secrets or SPIFFE/SPIRE Microsoft Entra Agent ID Agent calls a bulk-export tool outside its job Tool gateway allows only support tools agentgateway or IBM ContextForge Agent Governance Toolkit policy (preview) Agent emails the export to an outside address Action policy routes the send to a person OPA with LangGraph interrupts Agent Governance Toolkit with human approval (preview) Investigators need to know what left Traces with the security fields above Langfuse with OpenTelemetry into the SIEM No Microsoft-specific pick: OpenTelemetry traces into the SIEM A forgotten copy of the agent keeps old permissions Discovery and inventory None mature; use a commercial tool Microsoft Agent 365 registry
Where Each Layer Catches the Attack
Notice that no single layer stops the whole attack. The gateway blocks the export tool, while policy catches the outbound email. Scoped identity limits how much a successful trick could reach, while the trace lets someone prove exactly what went out.
Retrieval-time variants work differently, because the poisoned text sits in the knowledge base rather than the inbox. Those need controls of their own, covered in our RAG security guide.
If this agent also ran code, say to build a spreadsheet for the customer, add a sandbox with outbound traffic denied. And if the company runs dozens of support agents, add discovery so a forgotten copy cannot keep its old access.
Kanerika Service
Agentic AI Services: Build and Run AI Agents
Kanerika designs, builds and runs AI agents with access scoped to each job, every action logged and a person approving higher-risk steps.
Explore Agentic AI Services How to Choose AI Agent Security Tools: Platform, Assembled Stack or Cloud-Native
Three buying models exist, but most enterprises end up mixing them. The right mix depends on how many agents you run, where they run and how much engineering time you can give to security plumbing.
Buying model What you get Fits Watch for Agent-security platform Several categories from one vendor in one console Many agents spread across SaaS and cloud Thin coverage in the categories the vendor added last; lock-in Assembled open-source stack Vault or SPIRE, a gateway, OPA, Langfuse and a sandbox Teams with platform engineers and custom-built agents Integration and upkeep fall on your team Cloud-native services Identity, registry and logging from your cloud or productivity suite Agents built mainly on one vendor’s stack Coverage stops at that vendor’s boundary
Acquisitions Change the Question
Ownership in this market changed hands several times in 2025, and product names changed with it. Palo Alto Networks completed its acquisition of Protect AI in July 2025 and folded it into Prisma AIRS. Check Point announced its acquisition of Lakera in September 2025, while Snyk announced it was acquiring Invariant Labs in June 2025.
For a buyer, that means asking where a product sits on the new owner’s roadmap. It also means reading what happens to pricing and support terms when the contract renews.
Six Pilot Tests Before You Sign
Vendor demos usually run on happy paths, so they show little about failure. These six tests run on the failure paths this guide has covered, and each one maps to a category.
Revoke a credential mid-session and time how long the agent keeps acting. Call a downstream API around the gateway with a direct key. Replay an old approval against a changed action. Send data to a blocked destination from inside the sandbox. Switch off telemetry and see whether anyone gets an alert. Start an unregistered agent and measure how long discovery takes to find it. A tool that fails one of these tests can still be worth buying. You just need to know which gap stays open and which other row of the map will cover it.
Pricing and Compliance
None of the vendor pages reviewed for this guide publish list prices for agent-security platforms. So budget for three lines: licences, the engineering time to wire each control into your agents, and the log volume your SIEM will ingest.
On compliance, the OWASP Agentic Top 10 is the most direct coverage checklist. Unlike EU law, the NIST AI RMF is voluntary, and our NIST AI RMF playbook shows how teams map controls to it.
For agents that count as high-risk under the EU AI Act , two articles map straight onto this grid. Article 12 requires high-risk systems to allow the automatic recording of events (logs) over their lifetime, which is the observability row. Article 14 requires that people can effectively oversee them while in use, which is where the approval row earns its place.
In the EU, Regulation (EU) 2026/1744 moved obligations for Annex III high-risk systems to 2 December 2027. Our EU AI Act compliance guide covers the detail. Whichever rules apply, logs and approval records from these tools become the evidence your AI governance program and its agentic AI governance policies rely on.
Kanerika Service
AI Governance Consulting Services
Kanerika sets up the policies, review cycles and evidence trail that turn agent logs and approvals into governed AI in production.
Explore AI Governance Services How Kanerika Helps Secure AI Agents
Kanerika designs, builds and runs AI agents for enterprises, so the rows of this map show up in delivery work. The agentic AI services page sets the baseline. Each agent gets access only to the systems its job needs, and every action it takes is logged so you can audit it later.
Each agent also has written limits on what it may do alone. Anything outside those limits, or below a confidence threshold, goes to a named person with the agent’s reasoning and sources attached. Clients decide where approvals sit, so high-risk steps always need a person before the agent acts.
Those limits can then be adjusted as the agent proves itself. A first agent on a single workflow is tested on historical cases before go-live. When agents run inside a client’s Microsoft 365 or Azure tenant, access controls and audit trails are built in from day one.
Governance Built Into the Architecture
The AI governance consulting practice runs on Kanerika’s IMPACT framework (Identify, Map, Prove, Analyze, Create, Transform). Identify surfaces governance gaps before they become audit findings, and Prove tests governance controls on the highest-risk systems first. Transform then embeds governance into ongoing AI operations as the operating standard.
When Kanerika develops the AI itself, governance requirements are captured in discovery. They decide the access control design, the audit logging scope and the monitoring built into production. Kanerika also holds ISO 27001 and ISO 27701:2019 certifications and SOC 2 Type II compliance.
A Production Support Agent
A support agent like the one in the reference stack is a common starting point. Kanerika built one for a B2B SaaS company serving SMB clients in more than 40 countries, starting with a knowledge base and prepared historical tickets.
The case study reports 80% of tickets answered automatically, 70% lower staffing cost and 50% faster ticket resolution. That makes it the kind of production agent this stack exists to protect.
Case Study
80% Ticket Auto-Resolution with LLM-Driven AI
Kanerika built an LLM-driven ticket response system for a B2B SaaS company that auto-answers 80% of tickets, with 70% lower staffing cost and 50% faster resolution.
Read the Case Study → Which AI Agent Security Tools Does Your Agent Need First?
AI agent security tools come in six categories, and none covers the whole path from credential to action to audit. But for any agent that writes to real systems, three come first. Give it scoped identity, route its tool calls through a gateway that enforces action policy and human approval, and send every step to your SIEM.
Add a sandbox with outbound traffic denied once the agent runs code. Then add discovery once your agents outnumber what a spreadsheet can track. Finally, run the six pilot tests, because a tool nobody has tried to break is a tool you have only read about.
Frequently Asked Questions
What are AI agent security tools?
AI agent security tools control, record or contain the actions an AI agent takes on real systems. They fall into six categories: agent identity and secrets, tool and MCP gateways, action policy and human approval, observability and detection, sandboxing and egress control, and discovery and inventory. Each category works at a different point where an agent can go wrong.
How are AI agent security tools different from LLM security tools?
LLM security tools mostly inspect what goes into and comes out of a model, such as prompt injection filters and output data-loss checks. AI agent security tools act on what the agent does next, like which credential it holds, which tool it calls and whether a person approves a write. Most agents need controls from both groups.
Does an MCP gateway protect every action an agent takes?
No. A gateway only governs traffic that passes through it. An agent holding a raw API key can call a service directly and bypass every gateway rule. Close that path by making downstream services accept only tokens issued for the gateway, then test it by giving the agent a direct key and confirming the call fails.
Can existing IAM and SIEM tools secure AI agents?
Partly. Identity platforms can issue agents their own scoped identities, and a SIEM can store agent traces once they use common formats such as OpenTelemetry. Neither decides whether a specific tool call is allowed or pauses a risky write for approval. Those jobs need a gateway, a policy engine and an approval step added beside them.
How do you secure AI coding agents?
Run coding agents inside an isolated sandbox, such as a microVM, with outbound network traffic denied by default and only required package sources allowed. Give them short-lived repository credentials scoped to one project. Route any push, deploy or database change through a policy check or a human approval, and record every command they run.
Are Langfuse and LangSmith security tools?
They are observability tools that record what agents did, step by step, which makes them useful for audit trails and investigations. They do not block actions on their own. Pair them with a gateway and a policy engine for prevention, and add security fields such as delegated user, policy decision and approval reference to each trace.
Which category of AI agent security tool should we buy first?
For an agent that can write to real systems, start with scoped identity, a tool gateway that enforces action policy and human approval, and traces sent to your SIEM. Add sandboxing once the agent runs code. Add discovery and inventory once the number of agents grows beyond what a spreadsheet and a few owners can track reliably.
What is the OWASP Top 10 for Agentic Applications?
It is an OWASP list of the ten most significant risks for AI agents, published in December 2025. The items include agent goal hijack, tool misuse, identity and privilege abuse, agentic supply chain vulnerabilities, unexpected code execution, memory and context poisoning and rogue agents. Buyers can use it as a coverage checklist when comparing tools.
Do agent-security vendors publish their prices?
The vendor pages reviewed for this guide do not publish list prices for agent-security platforms, so expect a sales conversation. Budget for licences, for engineering time to connect each control to your agents, and for the extra log volume your SIEM will ingest. Open-source options shift that spend from licences to engineering time.
