Best AI Code Security Tools for Detecting Risks in AI-Generated Code

AI can produce working code faster than many existing review processes were designed to absorb. That does not make the code inherently insecure, but it changes the security workload. A generated function can compile, pass unit tests, and satisfy the requested behavior while still containing an injection flaw, exposing a credential, adding a risky dependency, or introducing an unsafe configuration.

Functional correctness and security correctness are different properties. Veracode's Spring 2026 benchmark illustrates the gap: across 80 coding tasks in Java, JavaScript, C#, and Python, syntax correctness exceeded 95%, while only 55% of generation tasks passed the security checks used in the study. The benchmark covered four CWE categories, so the result should be read as evidence from a controlled test rather than a universal vulnerability rate for all AI-generated code.

AI-generated code security is the practice of applying code, dependency, secret, infrastructure, and workflow controls to software produced or modified with AI coding tools. The security challenge therefore extends beyond finding vulnerabilities after code reaches a repository. Verification has to keep pace with the way source code, dependencies, infrastructure definitions, and CI/CD configuration are being created. It also needs an enforcement point that remains independent of the system generating the code.

This guide compares security tools that can help detect risks in AI-generated code, explains what different types of scanners cover, and provides a framework for choosing the right security capabilities for different development environments.

In short

• AI-generated code still needs conventional application security controls, including SAST, SCA, secrets detection, IaC scanning, code review, and security testing.
• AI-assisted development mainly changes the scale and timing of verification. More code can reach review faster, and developers may be evaluating implementations they did not build line by line.
• A single scanner rarely covers first-party code, dependencies, secrets, infrastructure configuration, and AI-specific application risks equally well.
• IDE or agent-time scanning can shorten the feedback loop, but it should be backed by an independent pull request or CI control.
• Compare tools by risk coverage, detection method, workflow placement, remediation, governance, and deployment fit. The presence of an AI assistant or generated fix does not necessarily expand detection coverage.

Why AI-generated code needs independent security verification

AI-generated code needs the same application security controls as human-written code, but those controls may need to run earlier and more often. The main change is not a completely new vulnerability taxonomy. It is the speed and scope at which code, dependencies, and configuration can reach review.

More code can be created before human review

AI-assisted development can increase the amount of code produced between review points. Even if the defect rate remained unchanged, additional code would create more work for reviewers and automated security controls.

The practical consequence is a shorter useful feedback window. A security check that runs only after a large pull request reaches CI can still detect the same vulnerability, but the developer or coding agent may already have built several more changes around the unsafe implementation.

Moving some checks earlier can reduce that rework. It does not remove the need for downstream controls.

This layered approach is already consistent with conventional DevSecOps practice. NIST's DevSecOps scenarios include secret scanning before commit, SAST and SCA in commit hooks, code review, IaC scanning, and branch or merge protections as separate controls in the development lifecycle.

AI-generated code changes the pressure on these controls more than it changes their purpose.

Functional correctness can create false confidence

A unit test answers whether code behaves as expected for the cases the test covers. It does not automatically establish that authorization is correct, inputs are safely handled, cryptography is appropriate, or a dangerous data flow does not exist elsewhere in the application.

Generated code can therefore look convincing. It may be syntactically clean, the happy path may work, and tests may pass. A security review is asking a different set of questions.

This is also why broad claims that AI-generated code is inherently more or less secure are difficult to defend. Results depend on the model, programming language, prompt, task, vulnerability class, and evaluation method.

The useful operational assumption is simpler: code entering the system needs to satisfy the same security requirements regardless of whether it was written manually, suggested by an assistant, or produced by an autonomous coding agent.

Review dynamics are changing

A developer reviewing generated code may be evaluating an implementation they did not construct incrementally. They have to reconstruct assumptions, data flows, error handling, dependency choices, and side effects before deciding whether the change is safe.

The scope grows further when coding agents can modify multiple files, install packages, edit GitHub Actions workflows, update Terraform, or run commands.

At that point, reviewing only the final application code misses part of the change. The package selected by the agent, the CI workflow it modified, and a credential it placed in a config file can each introduce a separate security problem.

What risks should AI-generated code be checked for?

AI-generated code should be checked for source-code vulnerabilities, vulnerable or malicious dependencies, exposed secrets, IaC and CI/CD misconfigurations, and AI-specific application risks when the product itself uses LLMs or agents.

1. Source-code vulnerabilities

This is the main SAST domain: injection, unsafe deserialization, path traversal, cross-site scripting, insecure cryptographic use, missing validation, and other weaknesses that can be inferred from source code.

Detection depth varies considerably.

Simple pattern matching can identify known dangerous constructs. AST-aware and semantic analysis can reason about syntax and types. Data-flow and taint analysis can trace untrusted input across functions or files toward a sensitive sink.

No static technique detects every business-logic or architectural flaw. When evaluating a product, the relevant questions are which analysis methods it uses and whether those methods cover the languages and frameworks in your production stack.

2. Dependencies and software supply-chain risk

Generated code can add a dependency as easily as it adds another function.

That creates several distinct questions:

• Does the package actually exist?
• Is the selected version affected by known vulnerabilities?
• Is the package malicious or suspicious?
• Is a safer version available?
• Does the package violate a license or internal policy?

Package hallucination is particularly relevant to code generation. OWASP's Secure Coding with AI guidance recommends verifying AI-suggested packages before installation rather than assuming a suggested dependency exists or is trustworthy.

Measurement of this problem also requires care. A 2026 research preprint found that previous evaluation methods could overstate package-hallucination rates by classifying standard-library modules as nonexistent dependencies. For Python, the authors reported an overestimation of 9.4 percentage points under affected methodologies.

That is a useful warning for security tooling as well. A dependency control used to block builds should understand enough package and language context to avoid treating every unfamiliar import as a malicious or hallucinated dependency.

Traditional SCA remains necessary for known CVEs, transitive dependencies, license policy, and SBOM generation. Package reputation, malicious-package intelligence, and pre-install controls can extend that coverage for AI-assisted development.

3. Secrets and credentials

Generated code can place API keys, passwords, access tokens, connection strings, or example credentials into application code and configuration files.

Secrets scanners are designed to detect this problem and can run at several points:

• in the editor;
• before commit;
• during push;
• on a pull request;
• across repository history;
• in CI.

Detection approaches differ. Some scanners primarily use known token formats or custom regex patterns. Others add entropy analysis, contextual detection, token validation, or machine-learning models.

GitHub, for example, combines conventional secret scanning with AI-powered generic secret detection for unstructured credentials such as passwords.

Detection is only the first part of incident response. If a valid production credential reaches a repository, removing it from the latest commit may not be sufficient. The credential may need to be revoked or rotated, and historical exposure may need investigation.

4. Infrastructure and CI/CD configuration

Coding tools increasingly generate Terraform, Kubernetes manifests, Dockerfiles, shell scripts, GitHub Actions workflows, and cloud policies alongside application code.

A scanner that is effective at SQL injection does not automatically understand whether a cloud bucket is public, a workload runs with excessive privileges, or a CI workflow grants unnecessary repository permissions.

IaC and configuration scanning therefore deserve their own coverage.

Typical findings include:

• overly permissive IAM policies;
• public network exposure;
• missing encryption;
• unsafe container configuration;
• privileged workloads;
• insecure defaults;
• risky CI/CD permissions.

A short configuration change can alter the security boundary of an entire deployed system, even when the application source code itself is unchanged.

5. AI-specific application risks

This category applies when the application itself contains LLM or agent functionality. It should not be added to every code-security program simply because AI helped write the code.

Applications that invoke models, use RAG, expose tools to agents, or process model output can introduce additional trust boundaries. OWASP maintains a separate risk taxonomy for generative AI applications, including prompt injection, sensitive information disclosure, excessive agency, supply-chain weaknesses, and improper output handling.

Security tools are beginning to add rules for these patterns. Semgrep, for example, documents rules for prompt injection, unrestricted tool use, and data-exfiltration paths.

For an ordinary backend service built with coding assistance, those rules may not matter. For an agent that can call internal tools or modify external systems, they can become part of the application-security scope.

Which security controls can detect risks in AI-generated code?

The main controls are SAST for first-party source code, SCA and package intelligence for dependencies, secrets scanners, IaC scanners, and agent-time security controls that run these checks while code is being generated.

SAST

Static application security testing analyzes first-party source code without executing it.

Depending on the engine, SAST may use syntax rules, semantic analysis, control-flow analysis, interprocedural analysis, or taint tracking. It is useful for many implementation-level vulnerabilities and can run repeatedly as code changes.

Its main limitation is context. Automated static analysis cannot reliably infer every business requirement or authorization rule.

A tool may determine that a query is parameterized correctly while having no way to know that a particular user must never access another customer's record.

SCA and package intelligence

Software composition analysis examines third-party components. Classic SCA maps dependencies to vulnerability databases, license information, and version data. More specialized supply-chain tooling can add:

• malicious-package detection;
• package reputation;
• maintainer and release signals;
• behavioral inspection;
• pre-install controls;
• dependency reachability.

These capabilities matter when an AI coding tool can introduce packages without the developer deliberately researching each dependency first.

Secrets and IaC scanners

Secrets scanners and infrastructure scanners solve narrower problems, but those problems can have high impact.

A production token committed to Git or an overly broad cloud policy can create immediate exposure even if the application contains no conventional SAST finding.

For this reason, "we already scan the code" is not a complete answer unless the organization can say which control owns secrets, dependencies, and infrastructure configuration.

AI-assisted remediation

Many products now use LLMs to explain findings or propose fixes. That does not necessarily mean AI found the vulnerability.

GitHub Copilot Autofix, for example, generates code changes from CodeQL security alerts rather than replacing the underlying scanner.

Detection and remediation should therefore be evaluated separately.

A mature deterministic scanner with no chatbot may detect exactly the vulnerability class you need. A polished AI remediation interface may reduce fix time without increasing detection coverage.

Agent-time security controls

A newer category moves security checks into the coding-agent loop itself. Instead of waiting for the finished change to reach CI, a coding agent can invoke a scanner as it edits files, selects dependencies, or responds to security feedback.

This placement can reduce feedback time, but it raises another question: who controls the final gate?

If the same agent writes the code, decides whether to scan it, interprets the result, and decides whether its own fix is acceptable, the control is not independent. Agent-time scanning is most useful when a separate PR or CI policy still evaluates the resulting change.

Teams formalizing AI-assisted development can also move security requirements further upstream. Mad Devs describes one such approach in its guide to security requirements for AI-assisted development, where automated checks, CI gates, and review protocols back security constraints defined in the specification layer.

How to evaluate AI code security tools

A comparison becomes much easier when every product is assessed against the same questions.

What risks does it cover natively?

Map the tool against:

• first-party source vulnerabilities;
• dependencies;
• malicious packages;
• secrets;
• IaC and configuration;
• AI-specific application patterns, if relevant.

Keep native capabilities separate from custom rules, third-party integrations, and additional products sold under the same platform.

How are findings detected?

Ask what sits behind a result. Is it:

• deterministic pattern matching;
• semantic analysis;
• data-flow or taint analysis;
• dependency metadata;
• vulnerability intelligence;
• package-behavior analysis;
• machine learning;
• LLM reasoning?

The detection method influences both coverage and the amount of human validation a finding needs.

Where does the check run?

Security feedback can appear:

• while the agent is generating code;
• in the IDE;
• before commit;
• during push;
• on a pull request;
• in CI;
• before release.

Early controls optimize for feedback speed. Repository and CI controls provide a more independent enforcement point.

Can policy block an unsafe change?

A dashboard notification is different from an enforced security gate.

Check whether the tool can block or fail a workflow based on:

• severity;
• vulnerability type;
• dependency policy;
• secrets;
• malicious packages;
• IaC violations;
• custom organizational rules.

Also check who can override a finding and whether that decision is logged.

What happens after detection?

Good remediation can include code context, an explanation, a safe upgrade path, a suggested patch, and a rescan. Generated patches still need engineering validation.

A security tool should make a fix easier to produce and verify. It should not turn a scanner result directly into unreviewed production code.

What governance evidence does it provide?

Larger environments usually need more than individual findings.

Useful capabilities include:

• centralized policies;
• repository coverage;
• exception workflows;
• RBAC;
• audit history;
• trend reporting;
• security ownership;
• visibility into disabled or bypassed controls.

Agentic development adds another governance question: which tools, MCP servers, and permissions can coding agents use?

Does it fit your environment?

A technically capable product can still be a poor fit if it does not support the required language, deployment model, repository topology, IDEs, or data-handling requirements.

Verify:

• supported languages and frameworks;
• monorepo behavior;
• SCM and CI integrations;
• cloud versus self-hosted requirements;
• IDE support;
• scan latency;
• data residency;
• licensing and edition boundaries.

Best AI code security tools to evaluate in 2026

There is no single product that provides equally deep coverage across every risk category. Some tools specialize in SAST, software composition analysis, secrets, or supply-chain security. Others extend security checks into coding-agent workflows or take a broader audit approach to repository health.

The list below includes both third-party products and Enji Guard, which is developed by Enji.ai, a Mad Devs product company. We include it because it directly addresses AI-written code and repository-level engineering risks, but its scope is different from a dedicated SAST or SCA platform. The comparison below reflects that distinction rather than treating every product as interchangeable.

Semgrep: SAST, supply-chain, secrets, and agent-time scanning

Semgrep combines first-party code analysis, supply-chain scanning, and secrets detection. Its security model is particularly relevant to AI-assisted development because those scanners can now be placed inside coding-agent workflows.

Semgrep Guardian scans AI-generated changes while they are being created and integrates with AI coding environments through agent-oriented workflows. Semgrep describes Guardian as using its code, supply-chain, and secrets capabilities inside that loop.

The supply-chain product also covers dependency and malicious-package risk, while Semgrep Secrets combines multiple detection techniques for credential exposure.

This makes Semgrep relevant when a team wants security analysis both during AI-assisted development and again at repository or CI control points.

Its AI Security rules also extend into applications that use LLMs or agents themselves, including prompt-injection and unrestricted-tool-use patterns.

Snyk: developer security plus governance for agentic development

Snyk covers several conventional AppSec layers through dedicated products.

Snyk Code provides SAST, Snyk Open Source covers dependency risk, Snyk IaC analyzes infrastructure configuration, and Snyk Secrets addresses credential exposure.

The more distinctive 2026 development is Snyk's expansion into Agentic Development Security.

That layer addresses more than generated source code. It considers the tools and infrastructure coding agents use, including MCP servers, skills, and other agent capabilities.

On September 30, 2026, Snyk made Govern Agent Behavior generally available, starting with MCP Governance. The feature is designed to discover agent tooling, observe out-of-policy usage, and control MCP use at runtime.

That makes Snyk relevant when the security problem includes both what the coding agent generates and what the agent itself is allowed to use or do.

GitHub: repository-native code, dependency, and secret controls

For teams whose development workflow is already centered on GitHub, native repository controls cover much of the conventional baseline.

GitHub Code Security includes code scanning and dependency-oriented controls, while Secret Protection adds secret scanning and push protection.

GitHub also applies AI in targeted security workflows.

Copilot Autofix generates proposed fixes for CodeQL alerts, while generic secret detection uses AI to identify unstructured credentials that deterministic patterns may miss.

The main advantage is where these controls sit. They are close to pull requests, repository rules, dependency changes, and merge workflows.

Teams that need deep multi-cloud IaC policy, pre-install malicious-package controls, or agent-time scanning across several external coding environments will typically need additional capabilities around the GitHub layer.

SonarQube: code quality and security gates

SonarQube combines code quality and security analysis.

Core security capabilities include SAST, taint analysis, secrets detection, and IaC scanning. SonarQube Advanced Security adds SCA and deeper analysis of interactions between application code and third-party dependencies.

For projects containing AI-generated code, teams can mark the project accordingly and apply the quality standards associated with AI Code Assurance. Automatic AI-code detection was deprecated in SonarQube Server 2026.1, so it should not be treated as the core mechanism for deciding which projects contain generated code.

AI CodeFix is a separate capability. It uses an LLM to propose fixes for a supported subset of issues that SonarQube has already detected.

This separation matters when evaluating the product: Sonar's analyzers produce the finding, while AI CodeFix assists with remediation.

Checkmarx One: centralized enterprise AppSec with AI-assistant access

Checkmarx One covers multiple security domains within one enterprise AppSec platform, including SAST, SCA, IaC, and Secrets Detection.

In 2026, Checkmarx added a native MCP Server that allows AI assistants and IDE chat environments to trigger and inspect security scans while using the platform's authentication and role-based access model.

That matters for organizations that want coding agents to call an existing governed AppSec platform rather than introducing a separate security path specifically for AI-generated code.

The value is less about creating a new scanner category and more about exposing established enterprise controls to a new development interface.

Veracode: policy-driven application scanning and remediation

Veracode remains closer to the established application-security model.

Its workflows support static analysis and software composition analysis across developer and CI processes, while Veracode Fix can propose and apply code changes for supported SAST findings.

This is another example of AI-assisted remediation sitting on top of conventional detection.

The distinction becomes important when reviewing generated fixes. Veracode explicitly recommends rebuilding and rescanning after applying a fix because the generated change can break the application even when it resolves the reported security flaw.

Its documentation gives a concrete example: Fix can add an 'import' that requires a new library but does not update the relevant package-manager file, such as 'pom.xml'. The original vulnerability may be fixed while the application no longer builds.

That is a useful model for AI remediation generally. A security fix is still a code change. It needs compilation, functional testing, and a new security scan.

Socket: package and software supply-chain risk

Socket addresses a narrower part of the problem than a full AppSec platform.

Its focus is software supply-chain risk, including package inspection, threat intelligence, malicious-package detection, and dependency analysis. That specialization can matter when AI coding tools are allowed to select or install dependencies.

Socket MCP allows AI assistants to score dependencies before they enter a codebase, inspect package artifacts, review threat information, and investigate package-level risk.

Socket should therefore be treated as a complementary supply-chain control rather than a replacement for general SAST, secrets detection, or IaC scanning.

Enji Guard: continuous AI code and repository auditing

Enji Guard is developed by Enji.ai, a Mad Devs product company. Unlike the tools above that primarily center on a specific AppSec scanner or security category, Guard evaluates a broader set of repository and engineering risks around AI-assisted development.

Its audits cover areas including security, dependency hygiene, test quality, CI/CD, configuration hygiene, codebase health, and AI readiness. The AI readiness audit looks at whether a coding agent can understand the project, make a safe change, verify it, and leave usable context for the next engineer or agent.

The security audit analyzes repository evidence around trust boundaries, authentication, secrets, unsafe data flows, workflow risks, and related application-security concerns. It can use static scanners and other read-only tools where appropriate, but it is deliberately bounded: it does not exploit findings, brute-force endpoints, run arbitrary repository commands, or treat static evidence as proof of a production compromise.

Dependency hygiene is handled as a separate audit. Guard inspects manifests, lockfiles, registries, acquisition paths, lifecycle scripts, vulnerability evidence, and reproducibility rather than reducing dependency health to a single CVE lookup. It also evaluates whether tests provide meaningful regression protection and whether CI/CD actually blocks unverified changes rather than simply existing as configuration.

Enji Guard adds a continuous audit layer across AI-written code and repository health, bringing security, dependency hygiene, test quality, CI/CD, configuration, and AI readiness into one review process.

AI code security tools comparison: SAST, SCA, secrets, IaC, and agent workflows

The table below focuses on documented product capabilities rather than assuming that every function available through custom rules or third-party integrations is native.

Product packaging and editions change frequently. Before procurement, verify the exact language coverage, deployment model, workflow integration, and licensing required for your environment. For Enji Guard specifically, the comparison should be read as audit coverage rather than one-to-one equivalence with specialized SAST, SCA, or IaC products.

When should AI-generated code be scanned?

AI-generated code should be scanned at several stages: during generation or in the IDE for fast feedback, before commit or push for secrets and other high-confidence issues, at pull request for independent enforcement, and in CI for deeper analysis.

During generation or in the IDE

Fast scanning can catch obvious source issues, suspicious dependencies, and secrets while the implementation is still being created.

This is where agent-integrated products can reduce the distance between introducing a problem and fixing it.

The control should remain lightweight enough that developers do not learn to bypass it because every edit triggers a slow full-project scan.

Before commit or push

Pre-commit and push-time controls are particularly valuable for high-confidence findings such as secrets.

Keeping a credential out of shared Git history is much easier than responding after it has been committed, synchronized, logged, or copied into another system.

At pull request

The pull request is an important independent control point.

A practical PR security workflow can include:

• diff-aware SAST;
• dependency review;
• secrets scanning;
• IaC policy;
• required security checks;
• human review for sensitive areas.

This is also where branch protection can prevent a coding agent or developer from merging a change that has not passed the organization's required controls.

In CI and before release

Deeper analysis can run later when it is too expensive for every edit.

Depending on the product and threat model, this may include:

• full-project taint analysis;
• broader policy checks;
• container or artifact scanning;
• DAST;
• API testing;
• integration testing;
• environment-specific security checks.

The important property is independence.

If a coding agent can write code and invoke security tooling, the same agent should not be the only authority deciding that its own work is safe enough to merge.

What AI code security tools cannot detect reliably

Automated code security tools are less reliable when a vulnerability depends on business rules, authorization context, runtime behavior, or architectural assumptions that cannot be inferred from code or configuration alone.

Business-logic and authorization errors

A scanner may understand that a database operation is syntactically safe and still miss that the operation violates a product rule.

For example, it may not know that:

• only the invoice owner can download a PDF;
• an administrator in one tenant must never view another tenant's records;
• a refund above a particular threshold requires a second approval.

These rules live partly in product requirements and architecture, not just in syntax.

Threat modeling, code review, API testing, and targeted security testing remain important for them.

When the risk extends beyond findings that automated scanners can verify, a broader AI-built product audit can examine authentication, secrets, configuration, dependencies, tests, documentation, and architecture together. Mad Devs' current audit scope explicitly includes products built with AI coding tools or mixed AI-generated code.

Generated remediation can introduce new defects

AI-generated fixes should be treated as proposed patches.

The Veracode pom.xml example shows the failure mode clearly: a remediation can fix the vulnerability, add an import for a new library, and still break the build because the package manifest was not updated.

Compilation and functional testing therefore remain part of security remediation.

The security scanner should also run again after the change. A patch that appears plausible in the diff is not evidence that the original finding is gone.

Unknown supply-chain threats

CVE-based SCA only knows about vulnerabilities represented in the intelligence it consumes.

Malicious-package detection and threat intelligence extend that coverage, but no supply-chain scanner can establish that every dependency will remain safe under every future condition.

Package selection is therefore still a trust decision.

Agent permissions are a separate security layer

Code scanners evaluate what the agent produces.

They do not automatically control what the agent itself can access or execute.

An agent that can read production secrets, run shell commands, access cloud environments, or invoke powerful MCP tools introduces a separate security problem.

That layer needs its own identity, permissions, tool governance, audit, and runtime controls. For a deeper treatment of that problem, see the Mad Devs guide to AI agent security.

How to choose an AI code security tool

Start with the security gap you actually have rather than the number of AI features in a product.

Your current AppSec stack already covers SAST, SCA, secrets, and IaC

Do not replace those controls simply because more code is now AI-generated.

Measure whether they still work at the new development pace:

• Are scan queues becoming slower?
• Are PRs becoming larger?
• Do developers receive findings after they have moved on?
• Are agents able to change files that existing scans do not cover?

If coverage is still sound, the missing capability may only be earlier developer or agent feedback.

Coding agents can add dependencies or edit infrastructure

Prioritize supply-chain and configuration controls.

An agent that can install packages, modify Terraform, or change a CI workflow can alter the risk profile before a conventional SAST scan sees anything important.

Useful additions include:

• pre-install dependency checks;
• malicious-package detection;
• SCA;
• IaC scanning;
• workflow security checks;
• independent PR policies.

Security findings arrive too late

Move part of the existing detection earlier instead of replacing it with a weaker scanner.

Products that can expose established SAST, SCA, or secrets engines in the IDE or agent loop are useful here. Then keep the deeper or policy-enforced scan at PR or CI.

The application itself uses LLMs or agents

Add controls for AI-specific application risks.

Review:

• prompt injection;
• tool permissions;
• sensitive data exposure;
• output handling;
• excessive agency;
• authorization across tool calls;
• external content entering agent context.

These checks sit alongside ordinary AppSec controls because the application still contains source code, dependencies, credentials, and infrastructure.

Governance is the main problem

If dozens or hundreds of repositories are involved, scanner accuracy is only part of the decision.

Check whether the platform can answer:

• Which repositories are covered?
• Which scans are mandatory?
• Who dismissed a finding?
• Who can bypass a block?
• Are exceptions time-limited?
• Can agents disable or avoid a control?
• Which MCP servers and tools are approved?
• Are changes and policy decisions auditable?

This is where repository-native controls, centralized enterprise AppSec platforms, and emerging agent-governance products differ most.

AI code security tool selection checklist

The selection decision should ultimately map each risk to a control and each control to a point in the development workflow. A product with more AI features is not necessarily a better security control if it leaves dependencies, secrets, configuration, or independent enforcement uncovered.