Agentic AI in Cybersecurity: Top 3 Use Cases and Key Considerations

What Is Agentic AI in Cybersecurity?

Agentic AI in cybersecurity refers to AI systems that can pursue security goals with a meaningful degree of autonomy. Instead of only identifying patterns or surfacing alerts, these systems can gather context, determine next steps, use connected tools, and execute tasks such as investigation, prioritization, containment, or remediation.

That makes agentic AI different from conventional security automation. Traditional automation follows fixed rules and predefined playbooks. Agentic AI can adapt its actions based on changing conditions, new evidence, and the broader context of the environment. In practice, this allows security teams to move from simple task automation toward more dynamic, context-aware execution.

Agentic AI in cybersecurity can support a wide range of functions, including incident response, threat investigation, security operations, application security, and autonomous red teaming. The common thread is not just analysis, but coordinated action.

Why Cybersecurity Needs Agentic AI

Modern software delivery moves fast, spans many environments, and generates large volumes of security data. Traditional approaches struggle to keep up with this scale and complexity. Agentic AI addresses these gaps by embedding autonomous decision-making and action across the development and security lifecycle.

• Real-time prevention and remediation: Security issues must be addressed as they appear, not after deployment. Agentic AI can detect and fix vulnerabilities during development, in pipelines, and in production workflows, reducing exposure windows and closing the gap between identifying issues and actually resolving them.
• Coverage across the entire delivery lifecycle: Security is no longer limited to a single stage. Agentic systems operate across development (IDE), integration pipelines (CI/CD), and organizational oversight, ensuring consistent protection from code creation to deployment. This continuous presence helps eliminate execution bottlenecks where findings accumulate but are not addressed in time.
• Reduction of alert noise: Security tools often overwhelm teams with alerts. Agentic AI evaluates context, prioritizes risks, and enforces policies automatically, allowing teams to focus only on meaningful issues. This directly addresses alert fatigue – a widespread challenge caused by high volumes of low-quality or redundant alerts that reduce response effectiveness.
• Consistent policy enforcement at scale: As systems grow, manually applying security policies becomes impractical. Agentic AI continuously enforces rules, thresholds, and compliance requirements across pipelines without manual intervention.
• Unified view of risk: Modern environments include code, open-source dependencies, containers, and cloud infrastructure. Agentic AI aggregates signals across these layers to provide a centralized, risk-based view of the entire system, overcoming the fragmentation caused by dozens of disconnected security tools and siloed data sources.
• Faster and safer development: By integrating directly into developer workflows and existing toolchains, agentic AI reduces friction. Teams can maintain delivery speed while improving security outcomes, without accumulating unresolved findings that slow down releases later.
• Adaptation to evolving threats: Cyber threats, including those driven by AI, evolve quickly. Agentic AI uses shared intelligence across environments to identify new risks and respond dynamically.
• Improved decision-making for leadership: Security leaders need visibility into trends, posture, and performance. Agentic systems provide continuous insights that support planning, reporting, and investment decisions. To see how these capabilities translate into business impact, read about the ROI of agentic AI in application security.
• Scalability without process overhaul: Organizations can enhance security without redesigning workflows. Agentic AI integrates into existing systems, allowing incremental adoption while delivering immediate value. At the same time, it enables a shift toward more autonomous workflows – with built-in oversight and human-in-the-loop controls – helping teams scale operations despite ongoing talent shortages and increasing system complexity.

Traditional AI vs. AI Agents vs. Agentic AI

Traditional AI, AI agents, and agentic AI are closely related, but they are not the same thing.

In cybersecurity, the differences matter because they shape how much context a system can use, how independently it can operate, and how much responsibility teams are willing to delegate to it.

The table below gives a quick comparison:

Traditional AI in cybersecurity is usually designed to classify, predict, recommend, or detect. It may score anomalies, identify suspicious behavior, or suggest likely root causes. These systems are valuable, but they are often narrow in scope and do not act on their own.

AI agents are software components that can perform defined tasks autonomously. For example, an agent might collect evidence for an alert, enrich a ticket, or query a threat intelligence source. Agents are useful building blocks, but on their own they may still operate within a tightly scoped workflow.

Agentic AI combines autonomy, reasoning, planning, and tool use. It can break a goal into steps, evaluate options, invoke multiple systems, and adapt its approach as conditions change. In cybersecurity, that makes agentic AI better suited to complex workflows that require investigation, prioritization, coordination, and follow-through rather than one-step automation.

How Agentic AI Works in Cybersecurity

While the field of agentic AI is rapidly progressing, here are the primary stages and processes typically found in state of the art agentic systems built for cybersecurity use cases.

1. Context Gathering

Agentic AI begins by collecting and aggregating relevant security data from across the environment. This includes logs, alerts, threat intelligence feeds, identity signals, code repositories, and cloud telemetry. The system correlates signals across these sources to build a unified, contextual understanding of potential risks, rather than relying on isolated data points.

This stage often involves continuous ingestion and enrichment of data, where the agent adds meaning by linking events, identifying relationships, and prioritizing relevance. For example, a login anomaly might be enriched with user behavior history, asset criticality, and known attack patterns.

2. Reasoning and Planning

Once sufficient context is established, the agent applies advanced reasoning to interpret the situation and determine the appropriate course of action. Using LLM-powered cognition and decision frameworks, it translates high-level goals, such as “investigate this alert” or “reduce exposure”, into structured, multi-step plans.

These plans are not fixed. The agent dynamically breaks down objectives into smaller tasks, evaluates multiple possible approaches, and selects the most effective strategy based on current conditions. This ability to decompose problems and adapt plans is what enables agentic AI to handle novel or complex threats.

In practice, this resembles a continuous reasoning loop: the system analyzes inputs, forms hypotheses, tests them, and refines its approach. This allows it to move beyond simple rule execution into true problem-solving, even in unpredictable environments. 

3. Tool Use and Orchestration

After defining a plan, agentic AI orchestrates actions across the security ecosystem by selecting and invoking the appropriate tools, APIs, and services. This might include vulnerability scanners, SIEM platforms, endpoint detection tools, cloud security systems, and internal databases.

Rather than executing a single action, the agent coordinates multiple tools as part of a broader workflow. It maintains context between steps, ensuring that outputs from one tool inform the next action. For example, it may query threat intelligence, trigger a scan, correlate results, and update a ticketing system, all within a single coordinated process.

4. Action Execution

With a plan in place and tools selected, the agent executes actions to remediate, contain, or prevent threats. These actions can include blocking malicious IP addresses, isolating compromised endpoints, revoking credentials, patching vulnerabilities, or enforcing security policies.

Unlike traditional automation, which relies on predefined scripts, agentic AI generates and executes actions dynamically based on real-time analysis. This enables more precise and context-aware responses, especially in situations where static playbooks would fail.

Execution is often iterative rather than one-time. The agent may take an initial action, observe the outcome, and then decide on follow-up steps. This continuous execution model allows for faster containment of threats and reduces the time between detection and response.

5. Feedback Loops and Human Approval

After executing actions, agentic AI evaluates the outcomes and feeds this information back into its decision-making process. This feedback loop enables the system to learn from experience, refine its strategies, and improve performance over time.

The agent continuously updates its internal state and memory, allowing it to recognize patterns, avoid repeated mistakes, and adapt to evolving threats. This creates a cycle of observation, reasoning, action, and learning, often referred to as an iterative or agentic loop.

In parallel, human oversight can be integrated as a governance layer. For high-risk or sensitive actions, the system can request approval, provide explanations, or escalate decisions to security teams. This human-in-the-loop approach ensures accountability and control while still benefiting from automation and speed.

3 Use Cases of Agentic AI in Cybersecurity

1. Application Security

Agentic AI enhances application security by embedding autonomous protection directly into the software development lifecycle. Instead of relying on periodic scans or manual reviews, agentic systems continuously monitor code, dependencies, and configurations for vulnerabilities as they are introduced. This enables earlier detection of issues such as insecure code patterns, misconfigurations, or supply chain risks before they reach production.

These systems can also take corrective actions automatically, such as suggesting or applying fixes, enforcing secure coding policies, and blocking risky deployments in CI/CD pipelines. By integrating with developer tools and workflows, agentic AI reduces friction while maintaining consistent security coverage across environments.

Additionally, agentic AI can adapt to evolving risks in modern application architectures, including microservices, APIs, and cloud-native systems. Its ability to reason about context and dependencies allows it to identify complex, multi-layer vulnerabilities that traditional AppSec tools often miss, improving overall resilience without slowing development velocity.

2. Incident Response

Agentic AI transforms incident response by enabling autonomous detection, investigation, and remediation of threats in real time. Instead of requiring analysts to manually triage alerts, agentic systems can assess the severity of incidents, correlate signals across environments, and determine the most appropriate response.

These agents can execute immediate containment actions, such as isolating compromised endpoints, blocking malicious activity, or revoking credentials, reducing the time between detection and response. This is especially critical in modern environments where attacks evolve rapidly and dwell time must be minimized.

Beyond immediate response, agentic AI supports full incident lifecycle management. It can gather evidence, document actions, and continuously reassess the situation as new data becomes available. By automating repetitive and time-sensitive tasks, it allows security teams to focus on complex investigations and strategic decisions, effectively augmenting human expertise in Security Operations Centers (SOCs).

3. Autonomous Red Teaming

Agentic AI enables continuous and scalable red teaming by simulating adversarial behavior against systems, applications, and AI models. Unlike traditional red teaming, which is periodic and resource-intensive, agentic systems can autonomously probe for vulnerabilities, test attack paths, and evaluate system defenses on an ongoing basis.

These agents can mimic real-world attackers by chaining actions together, such as reconnaissance, exploitation, and lateral movement, while adapting their strategies based on system responses. This allows organizations to uncover weaknesses that may not be identified through static testing or predefined scenarios.

In the context of AI systems, agentic red teaming is particularly important. It helps identify risks such as prompt injection, data leakage, permission escalation, and orchestration flaws across agent workflows. By continuously testing both individual components and full agent interactions, organizations can validate the security and reliability of their systems before and after deployment.

How Agentic AI Works in Application Security

Application security deserves special attention because it is one of the most practical areas for agentic AI adoption.
Secure Coding in the IDE

Agentic AI can be embedded directly into the developer’s integrated development environment (IDE) to assist with writing secure code from the start. These agents analyze code as it’s written, identify insecure patterns, suggest remediations, and enforce secure coding standards in real time, without interrupting development flow. They can also correlate findings with project context (frameworks, libraries, past commits) to reduce false positives and highlight the most relevant risks.

Beyond static checks, the agent can reason about intent. For example, it can detect when authentication logic is incomplete, when input validation is inconsistent across endpoints, or when secrets are handled incorrectly. It can then propose concrete fixes, generate secure code snippets, or refactor vulnerable sections. This shifts security left by preventing issues before they propagate downstream.

Agentic systems also learn from team behavior and past incidents. If certain vulnerability classes repeatedly appear, the agent can proactively flag similar patterns earlier and enforce stricter checks. Over time, this creates a feedback loop where secure coding practices improve without requiring constant manual training or reviews.

How Checkmarx helps:

Checkmarx One embeds agentic capabilities directly into the IDE, enabling real-time, context-aware security feedback as developers write code. It leverages a deep understanding of project structures, frameworks, and historical data to reduce false positives and recommend accurate, actionable fixes. The platform’s agentic approach adapts to coding patterns and enforces secure practices without disrupting developer productivity.

Policy Enforcement in CI/CD Pipelines

In the CI/CD pipeline, agentic AI continuously evaluates builds, configurations, and dependencies against organizational policies. It can automatically block risky changes, quarantine artifacts, or trigger compensating controls based on real-time context. Unlike static gate checks, agentic enforcement adapts to the evolving environment, factoring in exploitability, asset exposure, and runtime usage.

These agents can orchestrate multiple tools in the pipeline. For example, they may combine results from static analysis, software composition analysis, and container scanning, then make a unified decision about release readiness. Instead of failing a build on every issue, the agent can apply risk-based thresholds, allowing low-risk issues while stopping high-impact vulnerabilities.

Agentic AI can also manage exceptions intelligently. Rather than relying on manual approvals, it can track temporary policy overrides, enforce expiration, and re-evaluate risk as conditions change. This prevents “exception sprawl” and ensures that security debt does not silently accumulate over time.

How Checkmarx helps:

In CI/CD environments, Checkmarx One’s agentic enforcement engine evaluates builds using correlated insights from SAST, SCA, and other scans. It applies risk-based policies dynamically, blocking only those changes that present unacceptable exposure while allowing low-risk issues to proceed. The platform tracks policy exceptions, enforces expiration timelines, and ensures security controls evolve with the application.

Portfolio-Level Risk Visibility

At the organizational level, agentic AI aggregates risk signals across all assets, from application code and APIs to infrastructure and cloud services. It autonomously correlates vulnerabilities, misconfigurations, identity risks, and threat intelligence to construct a unified risk model. This moves teams away from siloed dashboards toward a consistent, system-wide view.

The agent can prioritize risks based on real impact rather than raw severity. For example, a critical vulnerability in an unused service may be deprioritized, while a medium issue in an internet-facing system with sensitive data is escalated. This context-aware prioritization helps security teams focus on what actually matters.

Agentic systems can also provide continuous reporting and forecasting. They track trends in vulnerability density, remediation time, and policy compliance, then surface insights for leadership. Some systems can simulate “what-if” scenarios, such as how a new dependency or architecture change would affect overall risk posture.

How Checkmarx helps:

Checkmarx One provides a unified, agentic risk dashboard that continuously aggregates and prioritizes risks across codebases, dependencies, APIs, and infrastructure. It integrates with cloud platforms and developer tools to provide a live, organization-wide view of security posture. By simulating impact scenarios and offering contextual prioritization, it helps teams and leadership align remediation with actual business risk.

End-to-End Threat Coverage

Agentic AI spans the entire software delivery lifecycle, enabling threat detection, response, and prevention across all stages. It can monitor runtime environments, detect indicators of compromise, and autonomously initiate remediation actions, such as isolating services, rotating credentials, or applying patches based on predefined goals.

These agents integrate telemetry from logs, network traffic, endpoint signals, and cloud APIs to build a continuous understanding of system behavior. When anomalies occur, they do not just alert, they investigate. They can trace attack paths, identify affected assets, and determine the most effective containment strategy.

Over time, the system improves through feedback and shared intelligence. Lessons from one incident can inform detection and response in other environments. This creates a continuously evolving defense layer that adapts to new attack techniques, reducing dwell time and limiting the blast radius of breaches without relying solely on human intervention.

How Checkmarx helps:

Checkmarx One leverages agentic intelligence to provide end-to-end threat coverage, automatically detecting, analyzing, and remediating threats across the SDLC. Its agents integrate telemetry from runtime, cloud, and application layers, enabling fast, autonomous containment actions based on real-time goals. Continuous learning and feedback loops ensure each remediated threat strengthens future defenses.

How Agentic AI Works in Incident Response

Agentic AI transforms incident response by enabling autonomous detection, triage, and containment in near real-time. The system continuously ingests telemetry from endpoints, networks, identity systems, and cloud environments to detect anomalies and indicators of compromise. Upon identifying a potential incident, the agent correlates related signals, assesses severity, and determines the scope of impact using contextual reasoning.

The agent then formulates and executes a response plan, such as isolating affected assets, revoking access credentials, or blocking malicious domains. It documents each action taken, updates the threat model, and escalates to human analysts when oversight is required. This real-time, autonomous response reduces dwell time and limits the blast radius of attacks, improving resilience and reducing manual burden on SOC teams.

Key benefits of agentic AI for incident response:

• Enables autonomous, real-time detection, triage, and containment of threats.
• Reduces security incident “dwell time” by immediately executing containment actions like isolating compromised assets or revoking credentials.
• Improves resilience and reduces the manual burden on SOC teams.
• Continuously correlates signals and assesses severity using contextual reasoning.
• Documents actions taken, updates the threat model, and ensures human oversight is maintained by escalating high-risk decisions.

How Agentic AI Works in Autonomous Red Teaming

In autonomous red teaming, agentic AI simulates adversarial behavior continuously to uncover vulnerabilities across applications and infrastructure. These agents emulate tactics like reconnaissance, privilege escalation, and lateral movement—adapting strategies based on defenses encountered. Unlike scripted tests, agentic red teams plan and reason like real attackers, enabling them to discover complex, multi-step attack paths.

They can also target AI systems and agent workflows themselves, probing for weaknesses like prompt injection or orchestration flaws. By automating this testing at scale and across environments, organizations gain ongoing validation of their defenses. Agentic red teaming helps security teams identify blind spots proactively, test incident response readiness, and harden systems before attackers can exploit them.

Key benefits of agentic AI for red teaming:

• Provides continuous and scalable simulation of adversarial behavior against applications and infrastructure.
• Uncovers complex, multi-step attack paths by reasoning and planning like a real attacker, going beyond scripted tests.
• Proactively identifies system blind spots and tests incident response readiness on an ongoing basis.
• Validates the security of AI systems and agent workflows by probing for vulnerabilities like prompt injection or orchestration flaws.
• Hardens systems and defenses before they can be exploited by real-world threats.

Risks and Challenges of Agentic AI in Cybersecurity

Agentic AI has tremendous potential for improving cybersecurity productivity and risk posture, but also raises significant risks. Let’s review the primary risks and challenges organizations must face as they adopt agentic AI into their security strategy.

Setting and Enforcing Enterprise-Wide Policies

Agentic AI introduces new complexity when it comes to defining and enforcing consistent security policies across an organization. Unlike traditional systems, agents operate dynamically across tools, environments, and data sources, making it harder to apply static rules or centralized controls.

Organizations must translate high-level security, compliance, and operational policies into enforceable constraints that govern agent behavior at runtime. This includes defining what data agents can access, which actions they are allowed to take, and under what conditions those actions are permitted. These controls must be continuously enforced, not just configured once, as agents adapt and interact with new systems.

At scale, maintaining policy consistency becomes a challenge. Different teams may deploy agents with varying permissions, integrations, and objectives, leading to fragmented enforcement and potential gaps. Without a unified policy framework, organizations risk inconsistent security postures and unintended behavior across environments. 

Governance and accountability for Agentic AI

Governance for agentic AI extends beyond traditional AI oversight by addressing systems that can independently plan and execute actions. It involves defining clear boundaries of authority, accountability, and control for autonomous agents operating within enterprise environments.

Effective governance frameworks establish who is responsible for agent decisions, how those decisions are monitored, and what safeguards are in place to prevent misuse or unintended consequences. This includes identity management for agents, auditability of actions, and transparency into how decisions are made. Without these controls, agents can behave like “digital insiders,” introducing risks similar to privileged users with broad system access.

A key challenge is that traditional governance models, designed for static systems or human-driven processes, do not fully account for autonomous, adaptive behavior. Organizations must evolve their governance approaches to include continuous monitoring, runtime policy enforcement, and human-in-the-loop oversight for high-impact actions.

Over-permissioned agents

If agents have broad access to code, infrastructure, security tools, or identity systems, a design flaw or compromised input can have an outsized impact. Least privilege matters just as much for agents as it does for people and services.

Unsafe or low-quality actions

Agentic systems can still make poor decisions if they are working with weak context, misleading inputs, or poorly designed policies. Fast action is only valuable when the underlying reasoning and controls are reliable.

Potential for New Attack Surfaces

Agentic AI introduces new integration points that can expand the attack surface. For example, the agent connects to external services such as an MCP server to retrieve remediation instructions and may interact with generative AI tools to modify code or replace dependencies. These interactions create additional trust boundaries that must be secured.

Because the agent can automatically apply changes, any compromised input, such as poisoned remediation guidance or unsafe package recommendations, can quickly affect multiple parts of the codebase. The risk is not just incorrect fixes, but widespread propagation of those fixes. This makes validation, source integrity, and strict control over integrations essential when deploying agentic systems.

Balancing Speed with Security Assurance

Agentic AI enables vulnerabilities to be fixed immediately, even before code reaches later stages of the pipeline. This reduces exposure windows and keeps development moving, but it also introduces pressure to trust automated outcomes.

To address this, the system performs multiple layers of validation, including syntax checks, build validation, functional testing, and security confirmation. If any step fails, the agent reworks the fix until it passes all checks. This structured verification helps ensure that rapid remediation does not break functionality or introduce new risks, but organizations still need confidence that these checks align with their quality and security standards.

Integration Challenges with Existing Workflows

Although agentic AI is designed to operate within existing tools like IDEs, integrating it fully still requires process alignment. Teams must adjust how they handle code reviews, approvals, and exception management when fixes can be generated and applied automatically.

Developers also need to understand how the agent plans and executes changes, especially when it limits modifications to only what is necessary to fix a vulnerability. While this reduces disruption and avoids unnecessary changes, it can differ from traditional refactoring approaches. Adopting agentic AI therefore involves not just technical integration, but also updating team practices to incorporate automated decision-making while maintaining control and accountability.

How to Choose Agentic AI Cybersecurity Tools

Selecting an agentic AI solution requires more than evaluating model quality. The key is how well the system fits into real workflows, uses context, and drives measurable security outcomes across the software lifecycle.

Lifecycle and data strategy

• Coverage across development loops: Choose tools that operate across the inner loop (IDE), middle loop (CI/CD), and outer loop (organizational oversight). Solutions limited to a single stage create gaps in visibility and control. End-to-end coverage ensures consistent security from code creation to deployment and beyond.
• Unified data and telemetry: Look for platforms that aggregate signals from multiple sources such as code, dependencies, containers, and cloud environments. A unified data layer allows agents to make informed, context-aware decisions instead of acting on isolated findings.
• Real-time prevention and remediation: Tools should not only detect issues but also fix them as they appear. Evaluate whether the agent can generate and apply remediations during development and in pipelines, reducing exposure time without slowing delivery.

Integration and decision framework

• Policy-aware decision making: Ensure the system enforces organizational policies, SLAs, and risk thresholds automatically. Agents should align actions with business rules and allow policies to be audited, tuned, and consistently applied at scale.
• Integration with existing toolchains: Strong solutions integrate into IDEs, CI/CD pipelines, repositories, and cloud platforms already in use. This reduces friction and avoids the need for major process changes while enabling incremental adoption.
• Role-specific capabilities: Evaluate whether the platform supports different stakeholders. Developers need in-IDE guidance and fixes, security teams need policy orchestration, and leadership needs risk visibility and reporting. Generic tools often fail to address these distinct needs.
• Context-rich prioritization: The system should correlate findings across domains (SAST, SCA, IaC, APIs, containers) and prioritize based on real risk. This reduces alert noise and ensures teams focus on high-impact issues.

Scalability and intelligence

• End-to-end threat intelligence: Prefer solutions that incorporate intelligence across the software supply chain, including open-source risks and malicious package data. This improves detection of modern, AI-driven threats.
• Scalability without workflow disruption: Assess how easily the tool scales across teams and projects. The best solutions enhance existing processes rather than requiring a full redesign of development or security workflows.
• Visibility and reporting for leadership: Look for capabilities that provide portfolio-level insights, including trends, policy adherence, and remediation performance. Continuous visibility supports better planning and investment decisions.
• Proven platform approach: Tools built on a unified, cloud-native platform tend to provide better consistency across scanning, prioritization, and remediation. This reduces fragmentation and improves overall security posture.

Conclusion

Agentic AI is a force multiplier for cybersecurity, augmenting human teams with autonomous capabilities that improve speed, precision, and coverage. It doesn’t replace human expertise but enhances it, handling repetitive, time-sensitive tasks while allowing humans to focus on complex decisions and strategy. This partnership enables security teams to operate at a scale and velocity that traditional approaches cannot match.

Checkmarx One is uniquely positioned to deliver agentic AI for application security. Its unified platform integrates SAST, SCA, and other capabilities with real-time context and autonomous workflows. With native support for secure coding in IDEs, adaptive policy enforcement in CI/CD, and portfolio-wide visibility, Checkmarx One enables organizations to implement agentic AI across the software lifecycle, securing applications without slowing innovation.

Checkmarx: Transforming AppSec with Agentic AI

Checkmarx is an agentic AI AppSec platform to deliver full-lifecycle application security that keeps pace with modern development and AI-driven threats. The Checkmarx One Assist platform includes intelligent agents purpose-built for key DevSecOps roles, enabling proactive prevention, automated remediation, and continuous risk visibility from code to cloud.

• Developer Assist Agent operates inside the IDE, preventing and fixing vulnerabilities in real time. It detects issues across SAST, SCA, IaC, and secrets as code is written, and provides secure, context-aware fixes. It reduces mean time to remediate (MTTR) and helps developers stay secure without leaving their workflow.
• Policy Assist Agent enforces security policies continuously in CI/CD pipelines. It evaluates code, package, and configuration changes, reducing noise by prioritizing only meaningful risks. This ensures compliance and helps prevent vulnerable code from progressing through the delivery process.
• Insights Assist Agent provides real-time visibility into application security posture, trends, and SLAs. It correlates data across tools and environments, delivering actionable intelligence for AppSec teams to make better decisions and manage risk across large portfolios.

Checkmarx’s agentic AI architecture scales across the development lifecycle and enterprise environments, helping teams reduce risk, ship faster, and align AppSec with business priorities.

Learn more about Checkmarx One Assist