Best 10 Agentic Development Lifecycle (ADLC) Security Solutions (2026)
We review 10 ADLC security tools that govern AI coding agents, MCP servers and skills and check AI code before it's merged.
Written by
Joel Witts
AI coding agents such as Claude Code, Cursor, GitHub Copilot, and OpenAI Codex now do much more than write code. They autonomously run shell commands, install packages, and read files on the developer’s machine. They also connect to outside tools and data through Model Context Protocol (MCP) servers and commit changes, often without a developer checking each step.
Each of these actions can put an organization at risk before any code reaches review. This means that without strong governance in place, coding agents can be a major security risk. And while traditional static analysis (SAST) and software composition analysis (SCA) tools check code, they only do so once it reaches the repository or a pull request. By then, an agent may already have installed a malicious package, read an .env file or SSH key, or sent data to an unapproved MCP server.
Enter agentic development lifecycle (ADLC) security tools, also called agentic development security tools. These solutions give security teams control over these agents. They discover which agents, models, MCP servers, and skills (packaged instructions that extend an agent) developers use, and build an AI bill of materials (AI-BOM) from them. They also let admins approve or block MCP servers and stop risky agent actions as they happen, and they scan the code an agent writes inside the agent’s own workflow, so the agent can fix issues before the code is committed.
The team here at Expert Insights has compared 10 ADLC security solutions. We looked at features like AI inventory, MCP server controls, real-time guardrails on agent actions, code scanning and fixing inside the agent, discovery of agents on developer machines, and malicious package blocking, as well as which coding agents each tool supports and how it is priced.
This guide gives you the framework to choose a tool that fits the agents your developers use, where you want controls to run, and which agent actions you need to control.
What is Agentic Development Lifecycle (ADLC) Security?
ADLC security is application security for software that AI agents help build. In the traditional software development lifecycle (SDLC), developers write the code and security tools check it at the commit, pull request and build. In the agentic development lifecycle, a coding agent writes code, runs commands and installs dependencies itself, and developers review the results.
ADLC security tools, which some analysts and vendors call agentic development security tools, cover that new stage: what the agent is allowed to use, what it is allowed to do, and whether the code it writes is safe.
Tools apply these controls in two places. On the developer's machine, they run as an endpoint app, an IDE extension, a proxy between the agent and its Model Context Protocol (MCP) servers, or through hooks, which are scripts the agent runs before or after an action and which can stop that action. These controls act before code reaches a repository, so they can block a shell command or a package install as it happens.
In the repository and pipeline, tools scan what agents commit and build an inventory of AI models and frameworks from the code. Endpoint controls see what the agent does; repository controls cover every commit, whichever machine it came from. Several products work in both places.
The detail of each control varies between products. MCP governance ranges from allow and block lists for whole servers to controls on single tools within a server, and some products limit agents to MCP servers that run locally.
Runtime guardrails can cover shell commands, reads of secrets and key files, tool calls and prompts, and some products scope policies by user or team. In-loop scanning gives the agent code, dependency and secrets findings (SAST, SCA and secrets detection) through an MCP server, skill or CLI, so the agent can fix them before commit.
Package firewalls block malicious and typosquatted packages, and some also cover slopsquatting, where attackers publish packages under names that AI models invent. Audit logs and AI bill of materials (AI-BOM) exports in standard formats such as CycloneDX give compliance teams a record of what agents used and did.
Agentic Development Lifecycle (ADLC) Security Solutions Compared
Here is how the leading ADLC security solutions compare on AI inventory, MCP server control, real-time guardrails, in-agent code scanning, developer machine discovery, and malicious package blocking.
| Product | Best For | AI Inventory (AI-BOM) | MCP Server Allow/Block | Real-Time Guardrails on Agent Actions | Scans and Fixes Code Inside the Agent | Developer Machine Discovery | Malicious Package Blocking |
|---|---|---|---|---|---|---|---|
|
Cycode
|
Securing AI-generated and human-written code under one set of policies
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
|
Apiiro Guardian Agent
|
Context-aware secure prompting
|
Yes
|
No
|
No
|
Yes
|
Yes
|
Yes
|
|
Backslash Security
|
Agentic endpoint visibility
|
Yes
|
Yes
|
Yes
|
No
|
Yes
|
No
|
|
Checkmarx One
|
Find, fix, and verify inside AI coding agents
|
Yes
|
Yes
|
No
|
Yes
|
No
|
Yes
|
|
Endor Labs
|
Hook-based policy enforcement across coding agents
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
|
Knostic Kirin
|
Lightweight coding agent guardrails with published pricing
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
|
Legit Security VibeGuard
|
Command-level guardrails on the developer endpoint
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
No
|
|
OX Security
|
Per-tool MCP control linked to prompt-to-runtime AppSec
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
|
SentinelOne Prompt Security
|
Shadow AI control across developers and the wider workforce
|
Yes
|
Yes
|
Yes
|
No
|
Yes
|
No
|
|
Snyk Evo Agentic Development Security
|
Discovering and scanning agents, MCP servers, and skills
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
How We Tested
Expert Insights is an independent editorial team. We’ve looked at 10 ADLC security tools, assessing how each one finds the AI coding agents, MCP servers, and skills developers use (on their machines as well as in code), whether admins can approve or block MCP servers, which agent actions it can block as they happen, whether it scans and fixes agent-written code before it’s committed, and whether it stops malicious packages from being installed. This guide was researched and written by Joel Witts. Read our full methodology
Cycode
Cycode
Best for Securing AI-generated and human-written code under one set of policies
Cycode is an Agentic Development Security Platform. Its ADLC Security solution is built for the Agentic Development Lifecycle, where AI agents generate code, pull dependencies, call external tools, and commit changes without a developer ever opening an IDE. The platform combines three core functions: AI Visibility discovers shadow AI across the development environment and maintains an AI Bill of Materials; AI Governance controls which tools are authorized; and AI Guardrails intercept prompts, file reads, and tool calls at both the IDE and the CLI, before anything sensitive reaches an external AI service.
- Traditional application security coverage across SAST and AI SAST, SCA, secrets detection, container security, IaC, CI/CD and pipeline security, code leakage, and ASPM, applied to human and agent-generated code alike
- AI Visibility discovers AI coding assistants, MCP servers, models, rules and skills files, AI secrets, and packages, surfaced as a continuously updated AIBOM
- AI Governance with allowlists, blocklists, approval workflows, and MCP server controls including localhost-only execution
- AI Guardrails enforced at the IDE and CLI, before prompts are sent, before files enter agent context, and before tool calls execute
- Context Intelligence Graph maps code, infrastructure, identities, and runtime, queryable in natural language
- AI Teammates handle specific jobs: an Exploitability Agent, a Change Impact Analysis Agent, a Fix and Remediation Agent, and a Graph Agent. Cycode reports the Exploitability Agent cuts noise by 94% against the OWASP Benchmark
- The Cycode MCP Server lets Cursor, Windsurf, and Copilot trigger scans on generated code, then scans, fixes, and rescans until it is clean before it leaves the developer environment
- Maestro orchestration coordinates agents across scanning, triage, investigation, and remediation without manual handoffs
- 100+ native connectors across SDLC and ADLC environments
One of the main reasons we liked Cycode was that, rather than reporting on agent-generated code after the fact, Cycode puts scanning and fixing inside the loop the agent is already running, so problems are resolved before code leaves the developer’s machine. We were also impressed by the AI Teammates, because each one does a named job rather than being a general assistant.
Overall, we’d recommend Cycode for enterprise and upper mid-market organizations with high developer density, where the volume of AI-generated code is already outpacing what traditional security review can absorb.
Strengths
Cautions
Apiiro Guardian Agent
Apiiro
Best for context-aware secure prompting
Guardian Agent is an application security platform built on Apiiro’s Data Fabric. The platform rewrites developer prompts in real time, so they comply with pre-written security and compliance policies. It also inventories the AI agents, MCP servers, GenAI frameworks, models, datasets, and AI secrets in your code and maps each AI component to a business service and an accountable owner, with audit-ready, code-based evidence.
- Secure Prompts rewrite developer prompts in real time with your architecture, runtime, and policy context
- Guardian Agent stops coding agents and developers from committing vulnerable and non-compliant code
- AI-SPM inventories agents, MCP servers, frameworks, models, datasets, and AI secrets in your code, including those that arrive through transitive dependencies
- The AI-BOM refreshes on every commit and maps each component to a business service and owner
- Pull request policies block unapproved AI frameworks and models
- Risk Graph context on APIs, sensitive data, and internet exposure sets the priority of each finding
We think Apiiro Guardian Agent is a strong solution for large enterprises with complex application environments that want their AI coding agents to generate code with their specific requirements and risks in mind.
The concept of Secure Prompts is a great one. Apiiro puts security at the very start of the process, before any code is written, so there’s less to scan and fix afterward.
If you want your coding agents guided by your own architecture and risk context before they write code, Apiiro Guardian Agent is well worth considering.
Strengths
Cautions
Backslash Security
Backslash Security
Best for agentic endpoint visibility
Backslash Security is an agentic AI endpoint security platform that discovers and manages the AI agents, MCP servers, and skills running on developers’ workstations. The platform continuously scans endpoints to identify these tools, along with the identities, credentials, permissions, and network connections they use. This means it can detect shadow AI tools and the identities behind them.
- Continuous endpoint scanning discovers agents, models, MCP servers, hooks, plug-ins, and skills, with the credentials and permissions they use
- The AI Fabric Graph maps the relationships between agents, MCP servers, models, skills, and workstations
- The MCP Proxy inspects MCP traffic on the developer machine to block data leakage and prompt injection
- Forensic traces follow each prompt through the agent and tool call to its outcome, mapped to the EU AI Act, NIS2, DORA, and SOC 2
- Free tools include an AI Endpoint Exposure Assessment, an MCP Server Security Hub, and a Skills Security Scanner
We think Backslash Security is a strong solution for security teams looking for greater visibility and control over AI agents. The MCP Proxy, which inspects all MCP traffic, is a great way to make sure information isn’t shared without proper permissions, and it stops MCP connections from becoming an attack vector.
The platform comes with a free exposure assessment, which is a good starting point to understand your shadow AI risks. Something to be aware of is that Backslash secures the agents and tools themselves, not the code those agents write, so you’ll need a separate code security tool for that.
If your priority is seeing and controlling the AI agents on your developers’ machines, we recommend considering Backslash Security.
Strengths
Cautions
Checkmarx One
Checkmarx
Best for find, fix, and verify inside AI coding agents
Checkmarx One is an application and AI agent security platform. Its Developer Assist scans human- and AI-generated code as it’s written, checking for vulnerabilities, malicious packages, secrets, misconfigurations, and container issues, and generates fixes within the IDE. The platform also offers an autonomous mode, where Checkmarx automatically detects, fixes, and verifies issues in a loop inside the coding agent.
- Developer Assist scans code in the IDE for SAST, SCA, malicious package, secrets, IaC, and container issues as human developers and AI agents write it
- Autonomous mode runs a detect, fix, and verify loop inside Cursor, Windsurf, Kiro, and Claude Code, and rescans code before commit
- Developer Assist covers VS Code, Cursor, Windsurf, Kiro, JetBrains, Visual Studio, and Eclipse, with CLI plugins for Claude Code, GitHub Copilot, Cursor, Gemini, and Codex
- AI-BOMs export on demand in CycloneDX 1.7, with mappings to the EU AI Act, NIST AI RMF, and ISO/IEC 42001
- Remediation Assist opens merge-ready pull requests, and Safe Refactor handles dependency upgrades
We think Checkmarx One is a good fit for enterprises running a centralized AppSec program that want AI coding agents to fix vulnerabilities before code reaches a pull request. The platform offers a good deal of coverage across seven IDEs and five agent CLIs, and allows you to use the same SAST, SCA, and secrets engine across your pipeline.
The AI Inventory traces every model and MCP server to a specific file and line in your codebase, giving you strong visibility into AI activity.
Something to be aware of is that the inventory works on your repositories and doesn’t scan coding assistants installed on developer machines, although Checkmarx lists an MCP Scanner as coming soon. Another thing to note is that Checkmarx One for Government is FedRAMP Moderate certified, which is important if you sell to US federal agencies.
Strengths
Cautions
Endor Labs
Endor Labs
Best for hook-based policy enforcement across coding agents
Endor Labs is an application security platform. AURI is its security intelligence layer for AI coding agents; it scans code as agents or developers write it and fixes issues in real time. AURI is installed as a skills plugin, MCP server, and CLI, and works with Cursor, Claude Code, OpenAI Codex, OpenHands, VS Code, GitHub Copilot, Gemini, and other MCP-compatible clients.
- AURI scans code as agents write it and returns findings to the agent so it can fix them
- MCP server and skill inventory lists every local and remote server with usage and last activity, with allow lists and deny lists
- Runtime policies block destructive shell commands such as rm -r, reads of .env, .pem, and SSH files, and known-malicious MCP servers, and ask for approval before dangerous MCP calls such as DROP and DELETE
- More than 50 system policies plus custom regex rules decide whether each agent action is allowed, blocked, or audited
- Package Firewall works across package managers, registries, and CI pipelines, with cooldown windows and license policies
- AI SAST runs on every pull request, detecting broken access control, insecure direct object references (IDOR), and authentication bypass
We would recommend Endor Labs for engineering teams working across Claude Code, Cursor, Codex, or GitHub Copilot that need to govern agent actions and dependencies with clear policies.
We think the depth of the policy library is great: it covers shell, file, MCP, and prompt activity, putting security teams on a firm footing from day one. The Package Firewall is another strong part of the platform, defending against AI-hallucinated packages. Something to be aware of is that agents without native hooks, or outside Endor’s supported list, are discovered but not governed.
AURI for Developers is free, so you can try it before you commit to the enterprise governance features.
Strengths
Cautions
Knostic Kirin
Knostic
Best for lightweight coding agent guardrails with published pricing
Knostic Kirin is a security tool for AI coding assistants that applies your policies inline, before the agent acts. It runs as a user-space client for Claude Code and Claude Cowork and as an IDE extension for Cursor and VS Code, so developers don’t need admin rights to install it. Kirin works with Claude Code, Cursor, Windsurf, and other coding tools on macOS and Windows, with Linux support in beta., and you can roll it out through your MDM.
- Kirin applies policy inline and blocks risky agent commands before they run
- MCP allow and block lists, with unverified or risky servers quarantined in Enforce mode
- Real-time scanning for prompt injection and malicious instructions
- Secrets such as API keys, tokens, and passwords are redacted or blocked before they reach the model
- Kirin monitors IDE extensions and plugins and audits rules files such as CLAUDE.md and .cursorrules
- An inventory shows the packages coding agents install, and Kirin identifies vulnerable or malicious libraries
- Each policy runs in Off, Monitor, or Enforce mode
We think Knostic Kirin is a strong option for development teams that want guardrails on their coding agents quickly, without a long sales process. The Free plan covers up to five users, and Team costs $15 per user per month.
We like the Monitor mode, because you can see what Kirin would block before you switch on enforcement. Something to be aware of is that the Free plan doesn’t include the dashboard, organization-wide policy, or audit logs, and SIEM integration and SSO need the Enterprise plan.
Strengths
Cautions
Legit Security VibeGuard
Legit Security
Best for command-level guardrails on the developer endpoint
Legit Security VibeGuard is part of the Legit Security platform, which also includes AI-native ASPM, code security, and agentic remediation. VibeGuard runs as a desktop app. Once installed, it discovers coding agents on a developer’s machine (including Claude Code, Cursor, GitHub Copilot, and Windsurf) and connects to them automatically. It maps the agents, skills, MCP servers, and models installed, and gives AI assistants, models, and MCP servers reputation scores, so you can approve, block, or flag each one.
- The VibeGuard desktop app discovers and connects to coding agents on the developer endpoint
- Command Monitoring & Enforcement blocks dangerous commands with built-in or custom policies
- Reputation scores for AI assistants, models, and MCP servers, with approve, block, and flag actions
- Risky operations such as deleting code or changing production data go to the developer for a decision
- Guardrails stop agents reading .env and cloud credential files, and secrets are blocked before they reach AI tools
- A secrets MCP server shares credentials with agent sessions from the operating system’s keychain
- SAST and SCA scans check AI-generated code before it leaves the IDE
The command-level enforcement is what makes this platform stand out: you can see what agents actually run, stop destructive commands before they execute, and send risky operations back to the developer for a decision.
Something to be aware of is that Legit doesn’t publish pricing, although a free trial is available.
Overall, we think VibeGuard is a strong option for organizations that use, or are considering, Legit’s application security platform and want to extend it to the AI agents running on your machines.
Strengths
Cautions
OX Security
OX Security
Best for per-tool MCP control linked to prompt-to-runtime AppSec
OX Security is an AI-native application protection platform that covers agentic development, from prompts through to runtime. Its VibeSec component secures AI coding agents as they generate and modify code, alongside OX Code for code and supply chain scanning, OX Cloud for cloud and runtime security, and an Agentic Pentester.
VibeSec installs with a script on Mac, Linux, and Windows and connects to coding agents through their hooks. It works with Claude Code, Codex, Cursor, GitHub Copilot, and Kiro.
- VibeSec sends your security rules and organizational context to the coding agent before it generates code
- The Agent AI BOM records the MCP servers in use, with their tools and users
- You can allow an MCP server and block individual tools on it
- The Agent Activity Log shows each prompt and tool call, the action OX took, and the policies that applied
- OX Code and OX Cloud extend coverage to code scanning, SBOMs, APIs, cloud posture, and runtime
We think OX Security is a strong option for teams that want coding agent security tied into a wider application security platform, from the prompt through code scanning to cloud runtime. Per-tool MCP control stands out: you can keep a useful MCP server while blocking the specific tools you don’t want agents to call.
VibeSec focuses on preventing vulnerable code by guiding the agent, with fix suggestions in the IDE. Something to be aware of is that malicious dependency blocking is in early access, and OX doesn’t say which of its published plans include VibeSec, so check both before you buy.
Strengths
Cautions
SentinelOne Prompt Security
SentinelOne
Best for shadow AI control across developers and the wider workforce
SentinelOne Prompt Security controls how developers, AI agents, and the wider workforce use AI. Prompt Security is now part of the broader SentinelOne platform, and the same product covers coding assistants, employee AI use, and AI agents. For coding assistants specifically, it stops secrets, credentials, and proprietary code from leaking into prompts, and its Vulnerable Code Scanner blocks insecure or malicious AI-generated code in real time. It works with GitHub Copilot, Cursor, and Claude Code.
For agents, it maps every agent and MCP server, scores each one for risk against your policy, and inspects tool calls in real time to stop attacks as they run. Prompt Security can be deployed through an MCP Gateway, a browser extension, security plugins, and API integrations.
- Secrets, credentials, and proprietary code are kept out of coding assistant prompts
- The Vulnerable Code Scanner blocks insecure or malicious AI-generated code in real time
- An inventory covers every AI tool and code assistant in use, including shadow AI
- Sensitive data is redacted on the device across more than 15,000 AI services
- Every agent and MCP server is mapped and scored for risk against your policy
- Tool calls and agent interactions are inspected in real time, with attacks stopped as they run
- Role-based controls set who can use AI, how, and with what data
We think SentinelOne Prompt Security is a strong option for enterprise teams that want to control AI use by developers and by the rest of the workforce with one product. The shadow AI discovery and on-device redaction features stand out, because the same policies cover coding assistants, everyday AI tools, and agents.
If you need shadow AI control that reaches beyond your development team, SentinelOne Prompt Security is a strong choice.
Strengths
Cautions
Snyk Evo Agentic Development Security
Snyk
Best for discovering and scanning agents, MCP servers, and skills
Evo Agentic Development Security is Snyk’s security tool for AI coding agents. Sitting within Snyk’s wider application security platform, it secures what agents use, what they do, and what they generate. Evo inspects the agents installed on developers’ machines and builds an inventory of the MCP servers, skills, and tools they use, including ones your teams haven’t told you about. It matches local MCP servers against Snyk’s catalog without starting them and gives each a risk score from 0 to 1,000. Evo works with Claude Code, Cursor, VS Code, GitHub Copilot, Windsurf, Kiro, Gemini CLI, Codex, and other agents.
- Evo discovers the MCP servers, skills, and tools that agents use on developers’ machines
- Local MCP servers are checked against Snyk’s catalog without being started, and scored from 0 to 1,000
- Runtime policies block destructive agent actions before they execute
- MCP Governance limits agents to an allowed list of MCP servers
- Scans flag prompt injection, secrets, personal data, and shell commands
- Snyk Studio fixes vulnerabilities in agent-generated code before it’s suggested to the developer
- Evo AI-SPM builds an AI-BOM across your code and running applications
We think Snyk is a strong option for enterprises that already use Snyk for code and dependency scanning and want to extend it to their coding agents. The range of supported agents stands out, as does the risk scoring of MCP servers and skills before they run.
At the moment, Evo is only available on Snyk’s Enterprise plan, and blocking malicious package installs is still in private preview.
If you want agent discovery, runtime control, and in-agent fixes from one application security vendor, we recommend that you consider Snyk.
Strengths
Cautions
Other ADLC Security Solutions To Consider
Beyond our top 10, these agentic development security tools may also suit your environment and existing stack.
Adds guardrails for AI-generated code, including blocking malicious packages and blocking secrets before developers commit them.
Finds AI agents, MCP servers and stored credentials on developer laptops, and stops AI-generated code from leaking credentials.
Monitors coding assistants such as Claude Code, Cursor and Codex, checks every tool call before it runs, and detects prompt injection in files, web responses and MCP outputs.
Scans code as AI coding assistants write it and sends fix guidance back to the assistant, connecting to tools such as Cursor, Claude Code and GitHub Copilot through an MCP server.
Finds the coding assistants, MCP servers and skills on developer machines through existing EDR or MDM tools, and checks agent actions against policy at runtime.
Plugin for AI coding agents that scans every file the agent writes and stops security issues as the code is generated.
Socket Firewall sits between package managers and registries, checks each package an agent or developer requests, and blocks malicious ones.
Monitors coding agents including ChatGPT Codex, Claude Code, Cursor and GitHub Copilot, and can block or change a risky tool call before it runs.
ADLC Security Pricing
Most ADLC security platforms are sold on a quote basis, usually priced on the number of developers, and sometimes on AI usage or the number of machines running coding agents. Knostic, OX Security and Snyk publish starting prices, and Endor Labs, Knostic and Snyk offer free plans. Prices below are in US dollars, as each vendor publishes them on its own website at the time of writing.
| Product | Starting Price | Billing | Link |
|---|---|---|---|
|
Cycode
|
Contact for quote, based on active developers and AI usage
|
|
|
|
Apiiro Guardian Agent
|
Contact for quote
|
|
|
|
Backslash Security
|
Contact for quote
|
|
|
|
Checkmarx One
|
Contact for quote, based on developer seats, applications and usage. 30-day free trial of Checkmarx Developer Assist (one seat)
|
|
|
|
Endor Labs
|
Free Developer plan; Core and Pro plans: Contact for quote
|
Per contributing developer
|
|
|
Knostic Kirin
|
Free for 1 to 5 users; Team from $15 per user, per month (5+ users). 14-day free trial
|
|
|
|
Legit Security VibeGuard
|
Contact for quote. Free trial available
|
|
|
|
OX Security
|
From $19 per developer, per month, billed annually (Startup, 1 to 10 developers)
|
Annual
|
|
|
SentinelOne Prompt Security
|
Contact for quote
|
|
|
|
Snyk Evo Agentic Development Security
|
Free plan; Team from $25 per month (up to 10 developers). Snyk Evo, which includes coding agent security, needs an Enterprise plan: Contact for quote
|
Monthly (Free and Team); Enterprise uses prepaid credits
|
|
ADLC Security Buyer's Checklist
Work through these questions before you choose an ADLC security tool.
Developers often use several coding agents, such as Claude Code, Cursor, GitHub Copilot, Codex and Windsurf. Ask for the supported list, then ask how the tool connects to each agent: through the agent's own hooks, an IDE extension, an app on the developer's machine, or an MCP server. Check what the tool can block through each connection. Some tools discover agents that don't expose hooks but can't enforce policy on them.
Some tools build their inventory of agents, models and MCP servers from code in your repositories. Others scan developers' machines, which finds tools that never appear in a commit. Check which approach a product uses, and whether it shows the account each agent is signed in with, so you can spot developers using personal accounts.
MCP servers connect coding agents to outside tools and data. Check whether admins can approve or block servers, restrict single tools within a server, or limit servers to running locally. Some tools score a server for risk before a developer runs it. Others check MCP settings only in pull requests and CI pipelines, not on the developer's machine.
Ask which actions the tool can stop as they happen: destructive shell commands, reads of .env and cloud credential files, changes to database data or production systems, and prompts that contain secrets. Some tools block the action outright, some pause it and ask the developer, and some only advise the agent. Check whether admins can set different policies for each team or user.
Ask which scans run on agent-written code before it's committed: static code analysis (SAST), open-source dependency checks (SCA), secrets detection and infrastructure-as-code checks. Then check whether the agent receives each finding, fixes it and has the code rescanned until it passes, or whether the tool only reports the problem.
Coding agents install packages on their own, and some packages are malicious or copy the name of a popular package (typosquatting). Check whether the tool blocks a bad package when the agent installs it and in CI pipelines, or only flags it at commit. Ask whether it catches package names that AI models make up, which attackers can register. Check release status too, as some install-blocking features are still in preview.
Ask for a sample activity log. Check that it records each prompt and tool call, the action the tool took and the policy that triggered it. Confirm the tool can send events to your SIEM and export the AI inventory in a standard format such as CycloneDX, so other security and compliance tools can use it.
Check whether the tool needs admin rights or an install on every developer machine, whether IT can roll it out through a device management (MDM) tool, and which operating systems it supports. Vendors count pricing per user, per developer, per contributing developer or in usage credits, and agent security features can sit in a higher plan than the free or entry tier. Check that the plan you trial includes the features you're testing.
The Bottom Line
AI coding agents now run shell commands, install packages, connect to MCP servers and commit code, often without a developer checking each step. SAST and SCA tools in your pipeline see that code only once it reaches the repository, and by then an agent may already have installed a malicious package or read a credentials file.
An ADLC security tool has to show which agents, models, MCP servers and skills your developers use, let admins set what those agents can connect to and do, and check the code they write before it’s committed.
Start with where you need the controls to run. Tools on the developer’s machine, whether a desktop app, an IDE extension, an MCP proxy or agent hooks, can block a command or package install as it happens and find agents that never appear in a commit. Tools that work from the repository and pipeline build an AI inventory from your code and check every commit, whichever machine it came from. Several do both.
If your concern is shadow agents, destructive commands and credential reads on developer machines, shortlist tools with endpoint discovery and real-time guardrails. If you need an audit-ready inventory of the AI in your code and want agents to fix vulnerabilities before review, look at the repository-led platforms. Then check each shortlisted tool’s supported list against the agents your developers actually use.
Before buying, test the controls that matter most, because coverage is uneven. Several tools in this guide don’t describe malicious package blocking, and three offer install blocking only in early access or private preview.
Some tools return scan findings to the agent so it can fix the code, while others block insecure output or only advise the agent. Run the trial with the agents your developers use, check which actions the tool blocks, pauses or only logs, and confirm the plan on trial includes the agent security features and audit logs, since some vendors keep these out of their free and entry plans.
Everything You Need To Know About ADLC Security (FAQs)
The agentic development lifecycle (ADLC) is the series of processes used to develop software, when AI coding agents do much of the work. Agents such as Claude Code, Cursor, and GitHub Copilot write code, run commands, install packages and commit changes, while developers direct the work and review the results. Adoption is high: in Stack Overflow’s 2025 Developer Survey, 84% of respondents said they use or plan to use AI tools in development. In the survey’s section on AI agents, 81% agreed they have concerns about the security and privacy of data.
Yes. Both terms describe tools that control what coding agents use and do, and check the code they write. Cycode, Checkmarx, and Endor Labs use “ADLC”. Forrester calls the category agentic development security and published “The Agentic Development Security Tools Landscape, Q2 2026”. Snyk sells its product as Evo Agentic Development Security, and Cycode calls its platform an Agentic Development Security Platform.
Static analysis (SAST) and software composition analysis (SCA) tools check code once it reaches the repository, a pull request or a build. ADLC security tools also act on the agent itself, before code is committed: which MCP servers it can connect to, which commands it can run, and which files it can read.
Many ADLC tools still run SAST and SCA, but inside the agent’s workflow. Checkmarx Developer Assist works with the coding agent until the security check passes, and Cycode’s MCP server scans agent output, fixes issues and rescans. This matters because AI models often write insecure code: in Veracode’s 2025 GenAI Code Security Report, AI-generated code “introduces security vulnerabilities in 45 percent of cases” (veracode.com). For tools that scan at the repository, see our guides to SAST tools and SCA tools.
An AI-BOM is an inventory of the AI components an organization uses: models, agents, MCP servers, frameworks, datasets and skills. It does for AI what a software bill of materials (SBOM) does for software components.
Model Context Protocol (MCP) servers connect coding agents to outside tools and data. Anthropic counted “more than 10,000 active public MCP servers” in December 2025 (anthropic.com). Each one gives an agent new abilities, and a server can carry vulnerabilities, malicious behavior or excessive permissions.
ADLC tools control MCP in several ways. Some let admins allow or block servers, others can restrict MCP servers to run on the local machine only or block single tools within an approved server. Some run an MCP proxy that inspects traffic between agents and servers, or score MCP servers for risk.
Hooks are scripts a coding agent runs before or after an action, such as a shell command, a file read or a tool call. A hook can stop the action, so security tools use hooks to enforce policy at the moment the agent acts.
AI models sometimes suggest packages that don’t exist. Slopsquatting is when an attacker publishes a malicious package under one of those invented names, so an agent that installs it pulls in the attacker’s code. Researchers at USENIX Security 2025 found that, on average, at least 5.2% of packages suggested by commercial models were hallucinated, and 21.7% for open-source models. They recorded 205,474 unique invented package names.
Some do. Legit Security VibeGuard runs as a desktop app on the developer’s machine. Knostic Kirin installs a client that runs without admin rights and supports rollout through mobile device management (MDM). OX Security installs VibeSec with a script on Mac, Linux and Windows.
Others don’t. Endor Labs uses the agents’ built-in hooks and doesn’t require “a traditional endpoint-security agent.” Checkmarx builds its AI inventory from code in the repository. Backslash Security sits between the two: its free exposure assessment of developer machines needs no agent install, but its MCP proxy runs on the developer’s machine.
Controls on the machine see what an agent does as it happens. Controls on the repository cover every commit, whichever machine it came from.
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