---
title: "AI Security: Ultimate Guide to Securing the AI Ecosystem"
date: "2026-08-25T16:49:15+00:00"
url: "https://checkmarx.com/learn/ai-security-ultimate-guide-to-securing-the-ai-ecosystem/"
description: "AI security is the practice of protecting AI systems from threats that can compromise their confidentiality, integrity, availability, safety, or intended behavior. It covers the entire AI lifecycle, including model development, training data, prompts, agents, generated outputs, deployment infrastructure, and the applications that use AI capabilities."
---

# AI Security: Ultimate Guide to Securing the AI Ecosystem

## Summary

AI security protects the entire AI ecosystem, including models, training data, prompts, agents, generated code, APIs, and infrastructure. This guide explains key AI security risks, frameworks, controls, and best practices organizations use to secure AI-powered applications and AI-assisted software development.

 ## What is AI Security?

AI security is the practice of protecting AI systems from threats that can compromise their confidentiality, integrity, availability, safety, or intended behavior. It covers the entire AI lifecycle, including model development, training data, prompts, agents, generated outputs, deployment infrastructure, and the applications that use AI capabilities.

While traditional software security focuses on securing code, networks, operating systems, and applications, AI security introduces additional attack surfaces. Attackers may target training data, manipulate model behavior through prompt injection, extract sensitive information from models, exploit AI-generated code, or abuse autonomous agents to perform unintended actions. As a result, securing an AI system requires more than applying standard application security controls.

AI security includes several interconnected components. Models must be protected against manipulation, theft, and misuse. Prompts and system instructions must be designed to resist attacks that attempt to override safeguards. Agents require controls around tool access, permissions, and action execution. Data used for training, fine-tuning, retrieval, and inference must be protected from exposure and poisoning. Generated code should be reviewed and validated before deployment. The environments where AI systems run, including cloud services, APIs, databases, and runtime infrastructure, must also be secured.

## Why AI Security Matters

AI is becoming a core component of software development, business applications, and operational workflows. As organizations adopt AI-powered assistants, applications, and autonomous agents, they must address new security risks that extend beyond traditional software security.

Effective AI security combines technical controls, governance, visibility, and monitoring to help organizations manage risk across the entire AI lifecycle:

- **AI is changing how software is built and how applications behave:** AI is increasingly embedded in development workflows, business systems, and customer-facing applications. While it accelerates software delivery and automation, it also introduces risks through AI-generated code, automated decision-making, and interactions with external systems.
- **New attack surfaces and governance challenges:** AI introduces threats such as prompt injection, data poisoning, model manipulation, sensitive data exposure, and abuse of agent permissions. Organizations also need governance processes to manage model usage, data access, accountability, compliance, and acceptable use.
- **Why organizations need visibility and control across the AI lifecycle:** Organizations require visibility into models, data, agents, and AI-generated outputs to identify risks and monitor behavior. Controls such as access management, data protection, governance policies, monitoring, logging, and approval workflows help reduce risk and support secure AI adoption.

## Key Domains of AI Security

### AI-Assisted Software Development

AI-assisted software development focuses on securing the use of AI throughout the software development lifecycle. Developers increasingly rely on AI tools to generate code, create tests, explain vulnerabilities, write infrastructure-as-code templates, and automate development tasks. While these capabilities improve productivity, they can also introduce security risks.

AI-generated code may contain vulnerabilities, insecure configurations, outdated libraries, or logic flaws. Models can also recommend unsafe patterns if they were present in training data. As a result, organizations should treat AI-generated code as untrusted until it has been reviewed, tested, and validated.

**Security practices in this domain:** Secure code review, static and dynamic analysis, dependency scanning, secret detection, and software composition analysis. Organizations should also establish policies governing how developers use AI tools, what data can be shared with them, and how generated outputs are incorporated into production systems.

### AI Applications and LLM-Powered Systems

AI applications combine large language models, retrieval systems, prompts, tools, APIs, and business logic to deliver user-facing functionality. Security in this domain focuses on protecting the application layer and preventing attackers from manipulating model behavior.

Prompt injection is one of the most significant risks. Attackers can craft inputs that attempt to override instructions, bypass safeguards, access restricted data, or influence agent decisions. Retrieval systems can also be targeted through poisoned or malicious content designed to manipulate responses.

**Security practices in this domain:** Organizations must secure every component of the application architecture, not just the model itself. This includes prompt management, API security, authentication, authorization, data access controls, and validation of model outputs. Security testing should evaluate how the application behaves under adversarial conditions. This includes testing for prompt injection, sensitive data leakage, jailbreak attempts, insecure tool usage, and excessive permissions.

### AI Model and Supply Chain Security

AI systems depend on complex supply chains that include foundation models, training datasets, open-source components, model weights, plugins, APIs, and third-party services. Each dependency introduces potential security risks.

Attackers may attempt to poison training data, distribute malicious models, tamper with model artifacts, or compromise components used during development and deployment. Organizations that consume external models must understand where those models originated, how they were trained, and whether they can be trusted.

Model theft is another important concern. Trained models often represent significant investments and may contain proprietary capabilities. Attackers may attempt to extract model parameters, replicate model behavior, or abuse APIs to reconstruct valuable intellectual property.

**Security practices in this domain include:** Artifact integrity checks, dependency management, provenance tracking, access controls, vulnerability monitoring, and continuous validation of third-party components. These practices help ensure that organizations can trust the models and services they deploy.

### AI Runtime Behavior and AI Guardrails

AI systems must be monitored and controlled after deployment because risks do not end once a model is placed into production. Runtime security focuses on how models, agents, and applications behave during real-world operation.

AI guardrails are mechanisms that restrict unsafe or unauthorized behavior. They may prevent harmful content generation, block sensitive data disclosure, enforce business policies, validate outputs, or limit which actions an agent can perform. Guardrails provide an additional layer of protection beyond model training.

Agentic systems require especially strong runtime controls. Agents may interact with databases, cloud environments, messaging platforms, financial systems, or development tools. Security controls should enforce least-privilege access, action validation, approval workflows, and comprehensive logging.

**Security practices in this domain:** Continuous monitoring helps identify abnormal behavior, policy violations, attempted attacks, and unexpected model outputs. Runtime visibility enables organizations to detect incidents quickly and respond before they lead to security, operational, or compliance problems.

### Governance, Privacy, and Compliance

Governance provides the policies, processes, and oversight needed to manage AI risks at scale. It ensures that AI systems are used responsibly, securely, and in accordance with legal and business requirements.

Organizations need clear visibility into which AI systems are being used, what data they access, who owns them, and how risks are managed. Governance programs often define acceptable use policies, model approval processes, risk assessments, documentation requirements, and accountability structures.

Privacy is a major component of AI security because models frequently process sensitive information. Organizations must understand how data is collected, stored, shared, retained, and used during training and inference. Controls such as data minimization, encryption, access management, and anonymization help reduce exposure.

**AI governance and compliance practices include:** AI programs must adhere to compliance requirements, which vary by industry and region but increasingly apply to AI systems. Organizations may need to demonstrate transparency, maintain audit trails, perform risk assessments, monitor model performance, and document security controls. Strong governance helps ensure that AI deployments remain compliant while supporting innovation and business objectives.

## AI Security Risks in Modern Application Development

### 1. Vulnerabilities Introduced by AI-Generated Code

AI coding assistants can accelerate development by generating large amounts of code in seconds, but they do not guarantee secure implementations. Because models learn from publicly available code, documentation, and examples, they can reproduce insecure patterns, outdated practices, or incomplete security controls. Common weaknesses include flawed authentication logic, insufficient input validation, hardcoded credentials, weak cryptography, and injection vulnerabilities. The risk increases when developers accept generated code without thorough review.

**Impact:** AI-generated vulnerabilities can introduce security flaws directly into production systems, increasing the likelihood of unauthorized access, data breaches, application compromise, and regulatory violations. Because generated code often appears authoritative, insecure implementations may bypass normal scrutiny and spread across projects.

**Mitigations:**

- Treat AI-generated code as untrusted until reviewed
- Perform peer reviews and security-focused code reviews
- Use static application security testing (SAST)
- Scan dependencies for known vulnerabilities
- Enforce secure coding standards
- Validate authentication, authorization, and input handling logic
- Include security testing in CI/CD pipelines

### 2. Prompt Injection and Unsafe AI Application Behavior

Prompt injection attacks manipulate the inputs, context, or retrieved content provided to an AI system in order to alter its behavior. Rather than exploiting software vulnerabilities, attackers target the model’s instructions and decision-making process. Malicious prompts or poisoned content can be used to bypass safeguards, extract sensitive information, override system instructions, or influence application behavior, particularly in retrieval-augmented and agent-based systems.

**Impact:** Successful prompt injection can cause AI applications to disclose confidential information, perform unauthorized actions, misuse connected tools, or violate business policies. The risk becomes significantly greater when models have access to APIs, databases, workflows, or external services.

**Mitigations:**

- Validate and sanitize user-controlled inputs
- Isolate system prompts from user content
- Implement output filtering and policy enforcement
- Restrict tool and data access using least privilege
- Apply approval workflows for sensitive actions
- Continuously test applications for prompt injection weaknesses
- Monitor model behavior and tool usage

### 3. Excessive Agency in Coding Agents and Autonomous Workflows

Modern AI agents can write code, modify repositories, deploy infrastructure, execute commands, and interact with business systems. While these capabilities improve productivity, they also create security risks when agents are granted broad permissions. Agents can act as highly privileged users, and mistakes, manipulated inputs, or prompt injection attacks may lead to unintended actions across critical environments.

**Impact:** Excessive agent permissions can result in unauthorized changes, infrastructure misconfigurations, service outages, data loss, or exposure of sensitive systems. A compromised or misdirected agent can affect multiple environments faster than a human operator.

**Mitigations:**

- Apply least-privilege access controls
- Limit agent access to required tools and resources
- Require human approval for high-risk actions
- Segregate development, staging, and production permissions
- Maintain detailed audit logs of agent activity
- Validate agent outputs before execution
- Monitor for anomalous behavior and actions

### 4. Secrets Exposed Through Prompts, Repositories, and Generated Code

AI-assisted development increases the risk of exposing credentials, API keys, proprietary code, customer information, and internal documentation. Sensitive data may be shared in prompts, embedded in generated code, committed to repositories, or stored in logs and collaboration systems. Repository-connected AI tools can further expand access to information that should remain restricted.

**Impact:** Exposed secrets can enable unauthorized access to applications, cloud environments, databases, and third-party services. Credential leaks may lead to account compromise, data breaches, financial losses, and compliance violations.

**Mitigations:**

- Implement automated secret scanning
- Use centralized secret management systems
- Prevent credentials from being stored in source code
- Apply data loss prevention (DLP) controls
- Restrict repository and AI tool permissions
- Establish policies for acceptable prompt content
- Regularly rotate exposed credentials

### 5. API Security Gaps in AI-Built Applications

AI-generated applications frequently rely on APIs, but generated implementations may omit critical security controls. Authentication, authorization, rate limiting, input validation, and error handling are often incomplete or incorrectly implemented. AI-powered systems also create additional integrations between models, agents, tools, retrieval systems, and third-party services, increasing overall complexity.

**Impact:** API security weaknesses can enable unauthorized access, privilege escalation, excessive data exposure, abuse of business logic, and disruption of services. As the number of integrations grows, the attack surface expands accordingly.

**Mitigations:**

- Enforce strong authentication mechanisms
- Implement granular authorization controls
- Validate and sanitize all inputs
- Apply rate limiting and abuse protections
- Encrypt sensitive data in transit
- Perform API security testing and code reviews
- Continuously monitor API activity and logs

### 6. Infrastructure-as-Code Misconfigurations Created During Rapid Development

AI tools can generate infrastructure-as-code templates for cloud resources, networking, containers, and deployment pipelines. These configurations often prioritize functionality over security and may include overly permissive access controls, exposed services, weak encryption settings, disabled logging, or insecure defaults. Because templates are frequently reused, a single mistake can affect many environments.

**Impact:** Infrastructure misconfigurations can expose cloud resources directly to attackers, increase the risk of unauthorized access, weaken monitoring capabilities, and create widespread security vulnerabilities across multiple systems.

**Mitigations:**

- Review all AI-generated infrastructure definitions
- Use infrastructure-as-code scanning tools
- Enforce policy-as-code requirements
- Validate cloud security configurations before deployment
- Apply least-privilege identity and access controls
- Enable logging, monitoring, and encryption by default
- Conduct regular cloud security assessments

### 7. Open-Source and Software Supply Chain Risks in AI-Assisted Development

AI coding assistants frequently recommend open-source libraries, frameworks, packages, and code snippets. Some recommendations may include vulnerable, outdated, poorly maintained, or malicious components. AI-generated build processes, deployment scripts, and automation workflows can also introduce insecure practices that affect the broader software supply chain.

**Impact:** Supply chain weaknesses can expose applications to known vulnerabilities, malicious code execution, dependency confusion attacks, and operational disruptions. A single compromised dependency can affect multiple applications and environments.

**Mitigations:**

- Maintain an inventory of software dependencies
- Generate and track Software Bills of Materials (SBOMs)
- Use software composition analysis (SCA) tools
- Verify dependency provenance and integrity
- Continuously monitor for newly disclosed vulnerabilities
- Restrict use of unapproved packages and repositories
- Review AI-generated build and deployment configurations

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## AI Security Frameworks and Standards

### NIST AI Risk Management Framework

The [NIST AI Risk Management Framework (AI RMF)](https://nist.gov/itl/ai-risk-management-framework) provides a structured approach for identifying, assessing, and managing risks associated with AI systems. It supports organizations that design, develop, deploy, or use AI, helping them improve trustworthiness while managing risk. The framework includes four core functions: Govern, Map, Measure, and Manage, with governance informing the others.

For AppSec and security teams, the NIST AI RMF translates AI risk into a repeatable process. Teams can define ownership, map AI usage across the SDLC, measure technical and organizational risks, and manage them through controls, monitoring, and remediation. This is important as AI-generated code, machine learning models, and autonomous agents become embedded in production systems.

### EU AI Act

[The EU AI Act ](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng)is a comprehensive regulatory framework for AI systems in the European Union. It establishes a risk-based approach to AI governance, with different requirements depending on whether an AI system is prohibited, high-risk, limited-risk, or minimal-risk. The regulation includes obligations related to risk management, data governance, technical documentation, transparency, human oversight, accuracy, robustness, cybersecurity, and post-market monitoring. It also introduces requirements for general-purpose AI models, including additional obligations for models that may pose systemic risk.

For AppSec and security teams, the EU AI Act is important because it connects AI security with legal, compliance, and governance requirements. Teams may need to support AI system classification, document technical controls, assess cybersecurity and robustness, review third-party AI components, and provide evidence that AI systems are monitored and maintained after deployment. For high-risk AI systems, security activities such as threat modeling, access control, logging, vulnerability management, incident response, and supplier assurance become part of a broader compliance and risk management process.

### OWASP Top 10 for LLM Applications

The [OWASP Top 10 for LLM Applications](https://owasp.org/www-project-top-10-for-large-language-model-applications/assets/PDF/OWASP-Top-10-for-LLMs-v2025.pdf) is a framework focused on risks in applications that use large language models and generative AI. It was created as a community-driven project to address AI-specific security issues not fully covered by traditional web application frameworks. The 2025 version highlights risks such as prompt injection, sensitive information disclosure, supply chain vulnerabilities, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption.

For AppSec teams, the OWASP LLM Top 10 provides a threat model for building and testing AI-powered applications. It helps teams evaluate how user input, model responses, plugins, APIs, retrieval systems, and autonomous actions can be abused. Incorporating it into design reviews, code reviews, penetration testing, and runtime monitoring helps identify AI-specific weaknesses before they lead to production incidents.

### OWASP Top 10 for Agentic Applications

[The OWASP Top 10 for Agentic Applications](https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/) is a security framework focused on risks introduced by autonomous and semi-autonomous AI agents. It addresses threats that arise when AI systems can plan, make decisions, call tools, interact with APIs, access data, execute workflows, or perform actions on behalf of users. These risks extend beyond traditional LLM application security because agentic systems may combine reasoning, memory, permissions, external tools, identity, and automation in ways that increase business and security impact.

For AppSec teams, the OWASP Agentic Top 10 provides a practical threat model for evaluating AI agents before they are deployed into production environments. It helps teams assess whether agents have excessive permissions, unsafe tool access, weak identity boundaries, insufficient human approval gates, poor logging, uncontrolled memory, insecure integrations, or the ability to take harmful actions. Incorporating this framework into architecture reviews, agent design, red teaming, permission reviews, and runtime monitoring helps reduce the risk of AI agents becoming uncontrolled actors inside enterprise systems.

### Google Secure AI Framework (SAIF)

[Google’s Secure AI Framework (SAIF)](https://safety.google/intl/en_us/safety/saif/) is a framework for securing AI systems across their lifecycle. It adapts established security practices to risks introduced by AI, including model behavior, training data, AI infrastructure, and application-layer threats. SAIF includes six elements: expanding strong security foundations to the AI ecosystem, extending detection and response, automating defenses, harmonizing platform-level controls, adapting controls based on risk, and contextualizing AI system risks across business processes.

SAIF emphasizes that AI security should not be treated as a separate discipline. AI systems should be secured using the same practices applied to software, cloud, identity, infrastructure, and data protection, with added controls for AI-specific risks such as prompt injection, model theft, data poisoning, and confidential data leakage. This helps teams align AI security with enterprise security programs and apply controls across data, infrastructure, models, and applications.

### Cloud Security Alliance AI Controls Matrix

[The Cloud Security Alliance AI Controls Matrix](https://cloudsecurityalliance.org/artifacts/ai-controls-matrix) is a vendor-neutral control framework for secure and responsible AI systems, especially in cloud environments. It is designed to help organizations manage AI risks through practical control objectives and aligns with established frameworks such as the CSA Cloud Controls Matrix, ISO/IEC 42001, and NIST AI guidance. It focuses on translating AI governance and risk concepts into actionable controls that can be assessed and implemented.

For AppSec and cloud security teams, the AI Controls Matrix helps connect AI security requirements to cloud, SaaS, identity, data, and infrastructure controls. It can be used to evaluate AI services, third-party AI vendors, internal AI platforms, and cloud-hosted AI workloads. This is valuable for teams that need measurable controls for areas such as access management, logging, data protection, model governance, secure deployment, monitoring, and supplier assurance.

### MITRE ATLAS

[MITRE ATLAS](https://atlas.mitre.org/) is a knowledge base of adversary tactics, techniques, and case studies for attacks against AI-enabled systems. It is modeled in a similar style to MITRE ATT&CK, but focuses on AI-specific attack paths such as model theft, data poisoning, prompt injection, evasion, model manipulation, AI supply chain compromise, exfiltration, and abuse of AI agents or tools. ATLAS includes tactics and techniques across predictive AI, generative AI, agentic AI, and enterprise AI environments.

For AppSec and security teams, MITRE ATLAS provides a practical threat-modeling and detection framework for AI systems. Teams can use it to identify how attackers may target models, datasets, pipelines, APIs, RAG systems, plugins, and AI agents. It is especially useful for red teaming, adversarial testing, security monitoring, control mapping, and incident response planning because it describes AI threats in a way that aligns with existing security operations and threat intelligence practices.

### ISO/IEC 42001 AI Management System

[ISO/IEC 42001](https://iso.org/standard/42001) is an international standard for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System. It provides a management-system approach for organizations that develop, provide, or use AI systems, helping them define responsibilities, governance processes, risk management practices, policies, and continuous improvement mechanisms for AI.

For AppSec and security teams, ISO/IEC 42001 provides a governance layer around technical AI security activities. It helps ensure that AI security is not handled only as a project-level concern, but as part of an organizational management system with ownership, documentation, accountability, audits, and improvement cycles. Security teams can use it to align AI risk assessments, control implementation, supplier reviews, incident processes, and compliance evidence with a broader AI governance program.

## AI Security Tools and Controls

### Static Application Security Testing for AI-Generated Code

Static application security testing (SAST) helps organizations identify vulnerabilities in AI-generated code before it reaches production. As developers increasingly rely on AI coding assistants, security teams must account for the fact that generated code can contain insecure patterns, flawed business logic, weak authentication controls, and improper data handling. SAST automates source code analysis and provides early visibility into security issues across the development lifecycle. By integrating SAST into AI-assisted development workflows, organizations can reduce risk without slowing software delivery.

**Key capabilities:**

- Detects common vulnerabilities in AI-generated code
- Identifies insecure authentication and authorization logic
- Finds input validation and data handling weaknesses
- Integrates with IDEs, pull requests, and CI/CD pipelines
- Provides remediation guidance to developers
- Prevents high-risk code from being merged or deployed

### Software Composition Analysis for AI-Suggested Dependencies

Software composition analysis (SCA) helps organizations manage open source dependencies introduced through AI-assisted development. AI coding tools frequently recommend libraries and packages that developers may adopt without reviewing their security posture, maintenance history, licensing requirements, or supply chain risks. SCA continuously evaluates these dependencies and helps teams understand the risks they introduce across applications and development environments.

**Key capabilities:**

- Identifies known vulnerabilities in open source packages
- Detects risky, abandoned, or malicious dependencies
- Analyzes transitive dependency chains
- Monitors licensing and compliance risks
- Prioritizes remediation based on severity and exploitability
- Continuously scans repositories and build pipelines

### Secrets Detection Across Prompts, Repositories, and Generated Code

Secrets detection helps prevent sensitive credentials and access tokens from being exposed during AI-assisted development. Developers may unintentionally include secrets in prompts, generated code, source repositories, configuration files, or collaboration tools. Automated detection reduces the likelihood of credential exposure and helps organizations respond quickly when sensitive information is discovered.

**Key capabilities:**

- Detects API keys, passwords, tokens, and certificates
- Scans prompts, source code, repositories, and configuration files
- Monitors commit history and pull requests
- Blocks commits containing exposed secrets
- Alerts security teams and asset owners
- Supports credential rotation and remediation workflows

### API Security Testing for AI-Built Applications

API security testing evaluates APIs created, modified, or integrated through AI-assisted development. AI-generated applications often introduce new endpoints, services, and integrations that may lack proper authentication, authorization, input validation, or rate limiting. Security testing helps identify weaknesses before they can be exploited in production environments.

**Key capabilities:**

- Tests authentication and authorization controls
- Detects excessive data exposure and access control flaws
- Validates schema enforcement and input validation
- Evaluates business logic security
- Assesses API integrations and third-party connections
- Supports testing during development and runtime

### Infrastructure-as-Code Scanning for AI-Generated Cloud Configurations

Infrastructure-as-code (IaC) scanning identifies security issues in cloud and deployment configurations generated by AI tools. AI-generated Terraform, Kubernetes, Docker, and CloudFormation templates can contain insecure defaults that expose cloud resources or weaken security controls. Early scanning helps prevent these issues from propagating across environments.

**Key capabilities:**

- Detects insecure cloud configurations
- Identifies overly permissive IAM policies
- Flags exposed services and administrative ports
- Validates encryption and network security settings
- Enforces policy-as-code requirements
- Integrates into pull requests and CI/CD workflows

### Application Security Posture Management for Unified AI Risk Visibility

Application security posture management (ASPM) provides centralized visibility across security findings generated throughout the software development lifecycle. As organizations adopt AI-assisted development, risk can emerge from generated code, dependencies, APIs, infrastructure, secrets, and runtime environments. ASPM helps security teams correlate findings and focus on the issues that matter most.

**Key capabilities:**

- Aggregates findings from multiple security tools
- Correlates risks across code, infrastructure, APIs, and dependencies
- Prioritizes issues based on exploitability and exposure
- Provides centralized risk visibility for AppSec teams
- Supports governance and compliance initiatives
- Tracks remediation progress across development environments

### AI Runtime Security for Live Prompts, Responses, and Agent Behavior

AI runtime security protects AI-powered applications after deployment by monitoring how models, agents, and connected tools behave in production. Many AI-specific risks only emerge during real-world interactions, making runtime controls essential for detecting abuse, policy violations, and unsafe actions.

**Key capabilities:**

- Monitors prompts, responses, and tool interactions
- Detects prompt injection attempts and policy violations
- Identifies sensitive data exposure
- Monitors agent behavior and tool usage
- Enforces approval workflows for high-risk actions
- Supports blocking, redaction, and policy enforcement

### AI Data Loss Prevention for Prompts, Outputs, and Generated Code

AI data loss prevention (AI DLP) helps organizations prevent sensitive information from being exposed through AI systems. Employees may inadvertently submit confidential information to AI tools, while AI applications may return sensitive content in outputs, logs, or generated code. AI DLP applies controls to reduce the risk of data leakage and compliance violations.

**Key capabilities:**

- Inspects prompts and AI-generated outputs
- Detects proprietary, regulated, and sensitive data
- Supports redaction, masking, and tokenization
- Monitors file uploads and collaboration workflows
- Enforces data protection policies
- Generates alerts for potential data exposure events

### AI Bill of Materials (AIBOM) for AI Component and Supply Chain Visibility

An [AI Bill of Materials (AI-BOM)](https://checkmarx.com/learn/ai-cybersecurity/what-is-an-aibom/) provides an inventory of AI-related components used across applications and development environments. As organizations adopt models, datasets, agent frameworks, AI SDKs, and third-party AI services, maintaining visibility becomes critical for governance, security, and incident response. AI-BOMs help teams understand what AI assets are deployed, where they are used, and the risks they introduce.

**Key capabilities:**

- Inventories models, datasets, prompts, and AI frameworks
- Tracks AI dependencies and providers
- Supports AI supply chain visibility and governance
- Enables impact analysis for vulnerable AI components
- Improves auditing and compliance reporting
- Maintains traceability across the AI development lifecycle

## Key AI Security Best Practices

### 1. Treat AI-Generated Code as Untrusted Until Proven Secure

AI-generated code should be treated with the same caution as code copied from an unknown external source. Even when it appears functional and aligned with intent, it may contain insecure patterns, flawed assumptions, missing validation, weak authentication logic, or unsafe dependencies. Because models generate code based on patterns learned from large datasets, they may reproduce vulnerable or outdated practices.

Organizations should require AI-generated code to pass the same security checks as human-written code before merging or deployment. This includes static analysis, dependency scanning, secrets detection, peer review, unit testing, and security validation. Developers should review generated code line by line and verify that it meets secure coding expectations rather than assuming it is safe.

### 2. Human Review Gates for High-Risk AI-Generated Changes

Human review is critical when AI-generated changes affect sensitive or high-impact areas. Code that modifies authentication, authorization, encryption, payment processing, data access, infrastructure, APIs, or deployment logic should not be accepted without qualified review. Small mistakes in these areas can expose data or weaken controls.

Organizations should define review gates that require security, engineering, or platform owners to approve high-risk AI-generated changes. These gates should be enforced in pull requests, CI/CD workflows, and change management processes. Reviewers should assess whether code follows security requirements, avoids excessive permissions, protects sensitive data, and avoids unsafe dependencies or behavior.

### 3. Define Approved Use Cases for AI Coding Assistants

Security teams should define where and how AI coding assistants may be used. Without clear guidance, developers may use AI tools in ways that expose sensitive data or generate risky code. Approved use cases may include generating boilerplate code, writing test cases, explaining existing code, creating documentation, or suggesting refactors under controlled conditions.

Organizations should also define restricted uses. Developers should avoid entering secrets, customer data, proprietary source code, regulated information, or internal architecture details into unapproved AI tools. Policies should clarify whether AI may generate production code, modify infrastructure, create authentication flows, or interact with internal repositories. Clear guidance reduces shadow usage and unmanaged risk.

### 4. Maintain Secure Coding Standards for AI-Assisted Teams

AI-assisted teams still require secure coding standards that define how software is written, reviewed, tested, and deployed. These standards apply to both human-written and AI-generated code. Developers should follow established practices for input validation, output encoding, access control, error handling, cryptography, logging, dependency management, API design, and secrets handling.

Standards should also address AI-assisted workflows. Teams should document how to review generated code, prompt for secure implementations, validate AI-suggested dependencies, and reject insecure output. Training should reinforce that AI supports development but does not replace developer accountability.

### 5. Build Security Requirements Into AI-Assisted Development Workflows

Security requirements should be embedded directly into AI-assisted workflows rather than added later. Developers should receive security feedback in the IDE, pull requests, and CI/CD pipelines. This helps identify vulnerable code, exposed secrets, risky dependencies, API weaknesses, and IaC misconfigurations early.

AI-assisted workflows should include automated checks for source code, open source components, secrets, APIs, containers, and infrastructure as code. Policies should define which issues block merges or deployments, which require review, and which can be addressed later based on severity and business impact. Integrating security into existing workflows helps maintain control over risk as development accelerates.

### 6. Conduct AI Red Teaming and Pentesting Before Deployment

AI-powered applications and AI-assisted code should be tested adversarially before deployment. Functional testing confirms intended behavior, but AI security testing should evaluate system behavior under malicious or manipulative input. A thorough AI security assessment includes testing for prompt injection, unsafe output handling, excessive agency, insecure API behavior, broken authorization, dependency risk, and abuse of connected tools.

Red teaming and penetration testing uncover risks that automated scanning may miss, especially in applications that use large language models, agents, plugins, retrieval systems, or autonomous actions. Testers should attempt to bypass controls, manipulate model behavior, extract confidential information, trigger unauthorized actions, and exploit surrounding application logic. Findings should feed back into design, guardrails, monitoring, and incident response processes before release.

## Conclusion

AI security extends far beyond protecting models from theft, manipulation, or misuse. Organizations must secure every layer of the AI ecosystem, including training and inference data, prompts, agents, AI-generated code, third-party models, open-source dependencies, APIs, cloud infrastructure, and runtime interactions. As AI becomes embedded throughout software development and business operations, attackers increasingly target these interconnected components, making AI security a cross-functional discipline that combines application security, cloud security, data protection, governance, and operational controls.

Organizations that build security into the entire AI lifecycle will be best positioned to adopt AI safely at scale. This requires visibility and control across development workflows, AI supply chains, deployment environments, and production systems, supported by frameworks such as NIST AI RMF, OWASP guidance, and secure-by-design practices. By combining governance, secure development processes, runtime monitoring, and continuous risk management, organizations can reduce AI-related threats while enabling teams to innovate and deploy AI-powered capabilities with greater confidence.

## Securing the AI Ecosystem with Checkmarx One Assist

Securing the agentic AI development lifecycle and AI applications requires security solutions that move at the speed of AI. Checkmarx One Assist is a family of agentic AI AppSec agents: Developer Assist, Policy Assist, and Insights Assist. 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, Checkmarx One Assist 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 capabilities of Checkmarx One Assist:**

- **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.

[Learn more about Checkmarx One Assist](https://checkmarx.com/product/application-security-platform/)