Best GenAI Security Tools: Top 5 Options in 2026

What Are GenAI Security Tools? 

GenAI security tools are specialized platforms that help organizations govern and secure the use of generative AI. They address risks such as prompt injection, data leakage, shadow AI, unsafe model behavior, and insecure AI-generated code. Some GenAI security solutions focus on enterprise AI usage governance, while others secure AI-assisted software development and the software supply chain behind it.

These tools include capabilities for discovery, risk assessment, data protection, policy enforcement, and continuous monitoring, and they integrate with existing security infrastructure to provide a comprehensive defense against GenAI threats. 

Notable examples include Checkmarx, Aim Security, and Check Point Infinity.

Types of GenAI Security Tools

GenAI security platforms generally fall into two categories. 

The first governs how employees and enterprise systems use generative AI, focusing on shadow AI, data leakage, prompt controls, and policy enforcement. 

The second secures AI-assisted software development, focusing on AI-generated code, software supply chain risk, developer workflows, and application security.

Key functions of GenAI security tools include:

• Discovery and assessment: Tools identify and inventory all GenAI applications in use, both sanctioned and “shadow” applications, and assess their associated risks.
• Data protection: They prevent sensitive data from being leaked through prompts by using AI-powered data classification and classification.
• Policy and governance: These solutions enable the creation and enforcement of granular policies to govern GenAI usage and help meet regulatory compliance requirements.
• Threat prevention: They specifically address GenAI-related threats like prompt injection, data poisoning, and model inversion.
• Monitoring and response: They provide real-time monitoring and can integrate with existing security systems like SIEM and SOAR platforms to detect and respond to threats.
• Secure development lifecycle: Some platforms help secure the entire AI development lifecycle, from training to deployment.
• Securing AI-generated code: GenAI security tools offer in-editor code analysis and real-time feedback within development environments, flagging and remediating vulnerabilities as code is written.

This is part of a series of articles about AI cybersecurity

To learn more about each tool, click their names in the table or scroll down to see our full review.

Who Needs GenAI Security Tools? 

GenAI security tools are essential for organizations that build, deploy, or integrate generative AI into their software development lifecycle. These tools serve a wide range of technical and business stakeholders who are responsible for securing modern AI-driven applications:

• CISOs and security leaders: need these tools to gain visibility and control over AI risks across the enterprise. As generative AI introduces new vectors for data leakage, model misuse, and regulatory exposure, security leaders use GenAI tools to align risk management with broader compliance and governance objectives. Consolidating security functions into a unified platform also helps reduce tool sprawl and total cost of ownership.
• AppSec leaders and security teams: rely on GenAI security solutions to centralize policy management and prioritize risk across the AI ecosystem. With capabilities like AI-driven threat detection, policy enforcement, and correlated insights, these tools enable security teams to move from reactive triage to strategic risk reduction.
• DevOps and platform engineers: use GenAI-aware security tools to embed controls directly into the CI/CD pipeline and infrastructure-as-code processes. By integrating security into the development workflow, they can enforce guardrails at scale without disrupting delivery speed.
• Developers and development leaders: benefit from in-context security feedback and AI-generated fix recommendations. GenAI security tools surface issues within the tools developers already use such as IDEs and pull requests, allowing them to build securely without needing deep security expertise.

Key Functions of GenAI Security Tools

Because this category spans both enterprise AI governance and AI-assisted software development, the best tool depends on what you need to secure. Some platforms are strongest at discovering GenAI usage, blocking sensitive-data exposure, and enforcing employee AI policies. Others focus on securing AI-generated code, developer workflows, and software supply chain risk inside the SDLC.

Discovery and Assessment

Discovery and assessment functions allow organizations to locate and inventory all generative AI assets and interactions within their environment. These tools map out where AI models are deployed, what data they access, and how users interact with them. This visibility is crucial to identify potential points of exposure and evaluate the organization’s current AI risk posture.

Beyond simple asset tracking, assessment capabilities analyze model configuration, integration points, and historical user queries for potential vulnerabilities or non-compliance. This continuous assessment forms the foundation for targeted protection measures and compliance with frameworks like GDPR or internal governance standards. By establishing a detailed baseline, organizations can enact security controls that are tailored and proportional to their risk.

Data Protection

Data protection in the context of GenAI security centers on safeguarding sensitive prompts, responses, and any personal or regulated data that may be processed by AI models. These tools implement data loss prevention (DLP) capabilities to monitor and control the flow of information between users and AI systems, ensuring that proprietary, confidential, or customer data is not inadvertently exposed or misused.

GenAI security tools also apply encryption, masking, or redaction techniques to further reduce privacy and compliance risks. They often provide audit trails and automated reporting to document data handling practices for internal reviews or external audits. This layer of control is increasingly vital as AI models are trained on sensitive datasets and produce outputs that may unintentionally reveal privileged information.

Policy and Governance

Policy and governance features define and enforce how generative AI can be used within an organization, who can access certain capabilities, and under what circumstances. These security tools establish AI usage policies—such as approved prompt templates, prohibited topics, or access roles—and apply technical enforcement to prevent deviations from established rules.

Automated governance allows organizations to transmit regulatory requirements and company standards into actionable controls within their AI infrastructure. This helps ensure consistent adherence to legal and ethical standards across teams and projects. Governance tools also enable clear documentation and easy updates as regulations and business requirements evolve.

Threat Prevention

Threat prevention is focused on detecting and blocking malicious activities targeting generative AI systems. Common threats include prompt injections that manipulate model behavior, abuse of AI-powered chat interfaces, or attempts to extract confidential training data (“model inversion”). GenAI security tools employ real-time filtering and input sanitization to mitigate these risks before they impact the underlying model or data.

These tools often integrate with threat intelligence feeds and behavioral analytics to adapt against emerging attack patterns. This approach capitalizes on both signature-based and anomaly-based methods, reducing reliance on human intervention and speeding incident mitigation. Overall, threat prevention capabilities allow organizations to safely leverage GenAI in production without amplifying their attack surface.

Monitoring and Response

Continuous monitoring is essential for detecting abnormal interactions or policy violations in generative AI environments. GenAI security tools collect and analyze logs from AI interactions, infrastructure, and supporting applications to surface suspicious patterns in usage, data flows, or access attempts. Real-time alerts are generated for incidents requiring human review, such as unexpected data exfiltration or irregular prompt submissions.

Incident response automation streamlines mitigation by triggering actions such as user lock-outs, model suspension, or engagement with security operations centers when threats are detected. Monitoring tools also support robust forensics—retaining context-rich records that facilitate root cause analysis and help organizations refine their controls and resilience against future incidents.

Secure Development Lifecycle

GenAI security tools facilitate a secure development lifecycle by embedding risk management into each phase of model creation, fine-tuning, and deployment. They provide automated code reviews, configuration analysis, and security checks throughout development to catch vulnerabilities before models are launched. This shifts security considerations leftward, ensuring issues are addressed early and consistently.

Integration with DevOps pipelines allows security tools to enforce best practices, such as dependency scanning, AI artifact verification, and continuous validation against known vulnerabilities or misconfigurations. This reduces risk from incorporating third-party libraries or pre-trained models and ensures that only secured and compliant AI systems reach production. Embedding security in the development process helps guard against both intentional and accidental weaknesses in GenAI deployments.

Securing AI-Generated Code and AI-Assisted Development

As AI becomes a co-pilot in software development, securing AI-generated code is critical to preventing the introduction of vulnerabilities. GenAI security tools offer in-editor code analysis and real-time feedback within development environments, flagging unsafe patterns as code is written. This enables early detection of insecure constructs such as command injection, improper error handling, and unsafe deserialization. These tools often support policy-driven guardrails that block the inclusion of high-risk code, enforce secure coding practices, and align outputs with compliance requirements.

Beyond static analysis, some platforms simulate runtime execution to detect hidden issues like insecure API calls or logic flaws. Integrations with CI/CD pipelines ensure that AI-assisted code is scanned automatically before being merged or deployed, helping teams avoid last-minute security incidents. By embedding these checks directly into developer workflows, GenAI security tools minimize disruption while increasing coverage and confidence in AI-generated outputs.

Related content: Read our guide to AI cybersecurity tools

Core Security Challenges in GenAI Systems and AI-Assisted Development

Prompt and Model Manipulation Attacks

Prompt and model manipulation attacks exploit the gap between what an AI agent actually plans to do and what the user believes it will do. A recent example is Lies-in-the-Loop (LITL), also called HITL Dialog Forging, a novel agentic AI attack developed and documented by Checkmarx Research. In this attack, an adversary uses indirect prompt injection to alter the Human-in-the-Loop approval dialog itself, so the prompt shown to the user looks harmless while the underlying action is malicious. In practice, this can turn a safety control into a delivery mechanism for remote code execution.

These attacks are especially serious in agentic AI tools with high privileges, such as coding assistants that can run shell commands or modify files. HITL dialogs are often treated as the final safeguard against prompt injection and excessive agency, but LITL shows that this safeguard can itself be manipulated. Once the approval interface is no longer trustworthy, users may authorize harmful actions because they are only able to judge what the system displays, not what it actually executes.

Vulnerable or Hallucinated Code

A significant risk with GenAI adoption in coding environments is the generation of vulnerable or hallucinated code snippets, outputs that may be syntactically correct but insecure or functionally erroneous. Developers using AI-assisted code tools can unwittingly introduce flaws, such as SQL injection, buffer overflows, or logic bugs, especially when outputs are accepted without thorough review. Hallucination further compounds this risk by generating plausible-looking but non-functional code.

Security for AI-generated code necessitates a multilayered approach. Automated static and dynamic analysis can detect obvious vulnerabilities, while specialized agentic AI security tools can flag or block the incorporation of vulnerable or hallucinated code as it is written. Continuous education on AI’s limitations, coupled with human-in-the-loop review, is also essential to ensure that generated code maintains organizational security standards and does not introduce new risk vectors.

Supply-Chain Risks in AI-Assisted Development

AI-assisted development leverages pre-trained models, third-party libraries, and open-source data, all of which introduce supply-chain vulnerabilities. Attackers may compromise these building blocks to insert backdoors, trojans, or manipulated weights into downstream projects using them. Additionally, dependence on opaque model providers complicates verifying provenance and integrity, increasing the risk of hidden or inherited vulnerabilities.

Mitigating supply-chain risk requires dedicated tooling for dependency tracking, provenance verification, and tamper detection. GenAI security solutions often incorporate software bills of materials (SBOMs), model signing, and automated provenance analysis to identify untrusted or altered components. Security teams must continuously vet the entire AI development supply chain—ensuring that every third-party component is safe and that model updates do not inadvertently introduce unseen exposures.

Runtime Vulnerabilities in AI-Enabled Apps

AI-enabled applications often interact with unpredictable user inputs and external APIs at runtime, multiplying traditional attack surfaces. Vulnerabilities can arise from insecure integration between generative models and application logic, leading to privilege escalation, data leakage, or code execution risks. Attackers probing runtime behaviors can exploit weak authentication, error handling, or data validation in real time.

Addressing runtime vulnerabilities involves thorough testing of both AI models and their operational environments. GenAI security platforms conduct runtime monitoring, sandboxing, and exception analysis to immediately detect anomalies or attempted exploitations. Regular “red teaming” exercises and ongoing patch management further reduce exposure to newly discovered threats, ensuring that applications maintain security throughout continual updates and evolving user demands.

Notable GenAI Security Tools 

1. Checkmarx One Assist

Best for: Organizations that want a unified AI AppSec platform to secure code + supply chain at high velocity, with workflow-native support for developers and AppSec leaders.

Key strengths: Correlated risk across multiple testing signals (code, dependencies, APIs, IaC, containers) plus agentic assistance across IDE, CI/CD, and portfolio reporting to prioritize and accelerate fixes.

Things to consider: Plan a phased rollout (repos/pipelines/apps) and define governance guardrails early to ensure consistent policy enforcement and auditability.

Checkmarx One Assist is a family of agentic AI AppSec agents, Developer Assist, Policy Assist, and Insights Assist, which span the inner, middle, and outer loops of modern software delivery.  Powered by the Checkmarx One platform and its unified telemetry, these agents live where teams work: the IDE, CI/CD pipelines, and executive dashboards. 

Together, these agents prevent and remediate vulnerabilities in real time, standardize security policies at scale, and give leadership a live, risk-based view of the entire application portfolio so enterprises can ship AI-era software faster without losing control. 

Key features include:

• Inner loop: Secure coding in the IDE. Developer Assist prevents and fixes vulnerabilities as code is written, including AI-generated code, across SAST, SCA, IaC, containers, and secrets. 
• Middle loop: Policy enforcement in CI/CD. Policy Assist continuously evaluates code, configurations, and dependencies in pipelines, automatically enforcing AppSec policies, SLAs, and risk thresholds while reducing alert noise. 
• Outer loop: Portfolio-level insights and governance. Insights Assist aggregates signals from Checkmarx One to surface posture, trends, and exceptions for leadership, enabling risk-based planning, reporting, and investment decisions. 
• End-to-end AI threat coverage: The agents use shared intelligence from Checkmarx One, spanning applications, open-source packages, containers, cloud, and malicious package telemetry, to protect against AI-driven threats and software supply chain risk. 
• Faster adoption and less friction: Role-specific agents fit naturally into developer, AppSec, and leadership workflows, accelerating value realization and helping organizations scale secure development practices without large process overhauls. 

Key differentiators include:

• Agentic AppSec built for AI-assisted development: Checkmarx secures software at the moment risk is introduced, inside AI-assisted coding workflows, rather than waiting for downstream scans alone.
• Continuous assurance across AI-generated, human-written, and legacy code: The platform correlates risk across source code, open-source dependencies, IaC, APIs, containers, and supply-chain signals so teams can secure mixed codebases without relying on isolated point tools.
• Unified control from IDE to CI/CD to leadership oversight: Developer Assist, Policy Assist, and Insights Assist connect secure coding, automated policy enforcement, and portfolio-level visibility in one workflow-native system.
• Policy-aware actions with enterprise guardrails: Checkmarx agents operate using shared platform context, policy rules, and business priorities so remediation and enforcement stay auditable, tunable, and aligned with enterprise standards.
• Built to reduce friction, not add another scanner: Checkmarx differentiates from AI-boosted scanners by combining prevention, prioritization, and remediation into a unified AppSec platform that supports secure velocity at scale.

Secure AI-Assisted Development

Checkmarx One Assist

Explore how Checkmarx secures AI-generated code from IDE to CI/CD

See it in Action

2. Aim Security

Best for: Organizations deploying AI models and agents that need lifecycle security and governance across training, testing, and inference environments.

Key strengths: Comprehensive AI security posture management with dynamic model scanning, asset inventory, and supply-chain protection for AI systems.

Things to consider: Integration breadth and operational complexity may require specialized expertise, and pricing may be higher for smaller teams.

Aim Security provides AI security posture management and runtime protections across models, agents, datasets, and environments. It offers model scanning, asset inventory, compliance testing, and lifecycle safeguards across AI platforms.

Key features include:

• Dynamic model scanning: Traces live model operations in a sandbox to detect backdoors, rogue behaviors, and vulnerabilities that static scanners can miss.
• AI asset inventory and lineage: Discovers models, agents, and datasets, tracking provenance end-to-end so teams know what is running, where data originated, and whether configurations remain compliant.
• Compliance testing: Audits against regulations and frameworks, including the EU AI Act, ISO 42001, MITRE ATLAS, and NIST RMF, producing reports for governance and oversight needs.
• Supply-chain defense: Scans third-party components to block backdoors, tampered weights, and unlicensed code before they are integrated into training, fine-tuning, or production environments.
• Lifecycle protection: Applies continuous discovery, red-teaming, and policy enforcement across training, testing, and inference to maintain consistent controls from first commit to production agents.

Limitations as reported by users on Gartner:

• High cost: Some users report that pricing can be a concern, particularly when compared to publicly available AI tools.
• Limited integrations: Reviewers mention that integration options are limited, which can restrict connectivity with existing systems and workflows.
• Limited public information: Users note that there is not a lot of publicly available information, making evaluation more difficult.
• Need for specialized expertise: Effective use may require specialized knowledge, increasing the operational burden for teams without in-house AI security expertise.

3. Check Point Infinity GenAI Protect

Best for: Enterprises that need visibility and governance over employee use of generative AI tools and AI-enabled workflows.

Key strengths: Strong GenAI discovery capabilities with AI-powered data protection and governance insights for policy enforcement and compliance.

Things to consider: Initial deployment and configuration can be complex, and pricing may be high for organizations with limited security resources.

Infinity GenAI Protect discovers generative AI services, assesses associated risks, and applies AI-powered data protection controls. It emphasizes visibility, governance insights, data loss prevention, and regulatory reporting.

Key features include:

• GenAI app discovery: Identifies shadow and sanctioned GenAI applications in use across the organization, establishing a baseline of services, users, and risk exposure.
• Risk assessment: Evaluates GenAI tools and integrations to determine their risk profiles, informing decisions on permitted usage, access conditions, and compensating technical controls.
• AI-powered data classification: Uses contextual analysis of conversational data to reduce leakage risks, supporting data loss prevention without relying solely on predefined keywords or patterns.
• Governance insights: Surfaces visibility and insights that help define policies, prioritize investments, and standardize acceptable use across teams and services.
• Regulatory reporting: Maintains unified audit trails and details of risky activity to support compliance reporting and demonstrate adherence to applicable regulations.

Limitations as reported by users on G2:

• Steep learning curve: Users describe the platform as complex, requiring time and effort to learn and manage effectively.
• Challenging initial configuration: Setup and configuration can be difficult, especially during early deployment.
• Limited documentation: Some reviewers report gaps in documentation, which can slow onboarding and troubleshooting.
• Support delays: Users mention delays in support resolution.
• High cost: Pricing is viewed as burdensome, particularly for smaller organizations seeking comprehensive protection.
• Cloud dependency challenges: Some users report issues related to reliance on cloud-based components.

Source: Check Point

4. Microsoft Security Copilot

Best for: Security teams using Microsoft’s security ecosystem that want AI-assisted investigation, threat hunting, and SOC automation.

Key strengths: Deep integration with Microsoft Defender, Sentinel, and Entra combined with natural-language workflows and threat intelligence enrichment.

Things to consider: Effectiveness depends heavily on integration with Microsoft security products, and the platform can require training and budget investment.

Microsoft Security Copilot is a generative AI–powered security assistant that augments defenders across investigation, hunting, intelligence, posture management, and daily operations. It is available as a standalone experience and as embedded capabilities within Microsoft security products and selected third-party tools.

Key features include:

• Natural language copilot: Provides a conversational interface for incident response, threat hunting, intelligence gathering, posture reviews, troubleshooting, and policy tasks, converting complex security workflows into step-by-step guidance and summaries.
• Standalone and embedded experiences: Offers a standalone workspace and integrated prompts within products like Microsoft Defender XDR, Microsoft Sentinel, Microsoft Intune, and Microsoft Entra for in-context assistance during operational tasks.
• Plugin ecosystem: Extends functionality through Microsoft and third-party plugins that ingest events, alerts, incidents, and policies from services such as Red Canary, Jamf, and ServiceNow to broaden context and automate actions.
• Grounding and orchestration: Preprocesses prompts using grounding to refine specificity, sends enriched queries to the model, and post-processes results with plugins.
• Threat intelligence access: Searches authoritative sources including Defender Threat Intelligence articles, intel profiles, Defender XDR threat analytics, and vulnerability disclosures to add context to investigations and recommendations.

Limitations as reported by users on G2:

• Platform complexity: Users find the solution complex, especially those unfamiliar with AI-driven security tools.
• Steep learning curve: Teams may require additional training before using the platform effectively.
• High cost: Some reviewers consider the product expensive compared to alternatives.
• False positives: Reports of false positives can require additional manual verification and disrupt workflows.
• Limited access control: A small number of users note restricted access, limiting broader team usage.

Source: Microsoft

5. Prompt Security

Best for: Organizations building or using generative AI applications that need protections against prompt injection, data leakage, and unsafe model outputs.

Key strengths: Dedicated security controls for GenAI applications, AI code assistants, and agentic AI workflows with built-in red-teaming capabilities.

Things to consider: Configuration and ongoing updates may require security expertise, and some deployments may introduce minor performance overhead.

Prompt Security provides controls for employee GenAI usage, homegrown AI applications, AI code assistants, and agentic AI. It emphasizes prevention of prompt injection, data leakage, and unsafe model responses, alongside testing capabilities.

Key features include:

• Controls for employees: Adds visibility, security, and governance over employee use of AI tools, addressing shadow AI and data-privacy concerns with guardrails for acceptable use.
• Protection for homegrown apps: Blocks prompt injections, data leaks, and harmful LLM responses to reduce exploitation risks in custom applications that integrate generative models.
• AI code assistant safeguards: Integrates with developer workflows to prevent exposure of secrets and intellectual property when using AI-based code assistants like GitHub Copilot.
• Agentic AI security: Monitors, governs, and secures AI agents to maintain control over autonomous behaviors and interactions across connected systems and tools.
• AI red teaming: Provides testing capabilities to identify vulnerabilities in homegrown GenAI applications, informing remediation and ongoing hardening strategies.

Limitations as reported by Futurepedia:

• Management complexity: Users report a learning curve in understanding and managing configuration options and security settings.
• Dependence on continuous updates: The platform requires regular updates to address the evolving GenAI threat landscape.
• Potential performance overhead: Some reviewers note minor latency or overhead depending on how security controls are configured.

Source: Prompt Security 

How to Choose GenAI Security Tools

The right GenAI security tool depends on which part of the risk surface you need to control most. If your priority is enterprise AI governance, focus on discovery, shadow AI visibility, data protection, and policy controls for employee and business-system AI usage. If your priority is AI-assisted software development, look for support across IDE workflows, AI-generated code review, CI/CD guardrails, software supply chain context, and risk prioritization across the SDLC.

When comparing vendors, prioritize five areas:

Coverage: Does the platform secure GenAI usage, GenAI applications, or AI-assisted software development?
Workflow fit: Does it integrate into IDEs, CI/CD, and developer workflows, or sit mainly in security operations?
Policy and governance: Can it enforce usage policies, remediation rules, and audit requirements?
Context and prioritization: Does it correlate code, dependencies, prompts, and runtime signals, or treat each risk in isolation?
Scalability: Can it support enterprise teams, multiple repositories, and broad toolchains without adding major friction?

Where GenAI Application Security Fits in Your Dev Tech Stack

GenAI security tools do not replace traditional AppSec, but they do address a gap that became more urgent when generative AI moved software creation upstream into the IDE.

In practice, organizations now need to compare three layers: GenAI-specific controls for prompts and model interactions, traditional AppSec tools for post-commit analysis, and unified platforms that secure AI-assisted development from code creation through CI/CD and portfolio governance

GenAI-Specific Tools: Securing The Creation Layer

GenAI security tools operate at the earliest point in the software lifecycle, when code is generated from prompts. This is where new risks originate. AI assistants can introduce insecure logic, unsafe dependencies, or policy violations before code is ever committed.

These tools focus on real-time controls. They analyze prompts, generated code, and model behavior as it happens. This includes detecting prompt injection, preventing sensitive data exposure, and flagging insecure patterns in AI-generated code.

Their strength is visibility into how code is created, not just what the code contains. This allows organizations to address risks that traditional tools cannot see, such as hallucinated dependencies or prompt-driven logic flaws.

Traditional AppSec Tools: Securing The Downstream Pipeline

Traditional AppSec tools, such as SAST and SCA, are designed for a post-commit workflow. They scan code after it is written and committed to a repository. This model assumes that developers are the primary authors and that security checks can happen later in the pipeline.

This approach breaks down with AI-assisted development. By the time a scan runs, vulnerable code may already be merged or deployed. Fixing issues at this stage is slower and more expensive. These tools also lack context about how the code was generated, making it harder to detect AI-specific risks.

They remain essential for broad coverage across repositories, dependencies, and builds. However, on their own, they leave a blind spot at the point of code creation.

Unified Platforms: Bridging The Gap Across The SDLC

Unified platforms like Checkmarx One extend traditional AppSec into the AI era by combining upstream and downstream coverage. They integrate security directly into developer workflows while maintaining visibility across the full software supply chain.

These platforms embed controls in the IDE, CI/CD pipelines, and governance layers. For example, they can analyze AI-generated code as it is written, enforce policies during builds, and correlate risks across code, dependencies, and runtime environments.

This approach addresses the core issue highlighted in the source: security must shift left to the moment vulnerabilities are introduced. By covering both human-written and AI-generated code, unified platforms reduce fragmentation and enable consistent policy enforcement.

As AI becomes a standard part of development, relying on post-commit scanning alone is no longer sufficient. Organizations need security that starts in the IDE, understands AI-generated inputs, and continues through the entire lifecycle.

Conclusion

Generative AI introduces new risks that traditional security tools are not built to handle, ranging from prompt injection and model misuse to unsafe AI-generated code and opaque supply chains. GenAI security tools are purpose-built to address these gaps, offering discovery, protection, policy enforcement, and runtime controls that integrate into modern AppSec workflows. As organizations accelerate adoption of AI across development and operations, these tools become essential for maintaining visibility, compliance, and trust.

Checkmarx is uniquely positioned to help organizations meet GenAI security challenges because of its agentic, workflow-native approach to AppSec. With Developer Assist, Policy Assist, and Insights Assist, Checkmarx One Assist secures AI-generated code, enforces AI usage policies, and provides leadership with risk-based visibility; all from a unified platform. Its deep integration into developer and CI/CD workflows, combined with code-to-cloud telemetry, allows security teams to detect and remediate GenAI-driven threats without disrupting delivery speed or requiring significant retooling.