What Is AI Security? 

AI security focuses on protecting artificial intelligence systems, their data, and their outputs from threats and misuse. This field covers risks unique to AI, such as excessive agent permissions, unapproved AI-generated code, prompt injection, and data poisoning. It also adapts traditional security methods to the specific architectures and workflows of AI systems, accounting for how models are trained, deployed, and integrated into applications.

Unlike conventional cybersecurity, AI security addresses the dynamic, data-driven, and often autonomous nature of AI. As AI becomes more embedded in critical business operations, the importance of tailored security controls grows, making AI security an essential discipline for organizations leveraging advanced machine learning and generative AI.

AI security risks generally fall into two categories. Some risks emerge when organizations use AI during software development, while others arise when AI models, agents, and applications are deployed into production:

• Risks in development workflows: Security issues introduced through AI-assisted coding, package recommendations, data handling, automated changes, and inadequate oversight of AI-generated outputs.
• Risks in AI applications: Threats that target deployed AI systems, including prompt injection, knowledge-base exposure, excessive permissions, model manipulation, and unsafe autonomous behavior.

AI security risks at a glance:

The following table summarizes key AI security risks. We explore these and other aspects of AI security in more detail below.

Category	Risk	Potential Impact	Primary Mitigations
Development Workflows	Insecure AI-generated code	Introduces exploitable vulnerabilities such as SQL injection, XSS, insecure authentication, and hardcoded secrets into production	Human code review, SAST/DAST, dependency scanning, security testing, treat AI output as untrusted
Development Workflows	AI-suggested vulnerable or malicious packages	Software supply chain compromise through vulnerable, abandoned, hallucinated, or malicious dependencies	Verify packages, use approved registries, review package reputation, pin versions, continuous CVE monitoring
Development Workflows	Sensitive data leakage through prompts, logs, or model training	Exposure of source code, credentials, customer data, intellectual property, and regulated information	Prevent secrets in prompts, enterprise AI services, DLP controls, developer training, data classification
Development Workflows	Unreviewed AI-generated changes	Security flaws, business logic errors, and insecure configurations reaching production	Mandatory peer review, small pull requests, automated testing, prohibit direct AI commits to protected branches
Development Workflows	Weak auditability and accountability	Reduced traceability, compliance challenges, and difficult incident investigations	Record AI usage, maintain audit trails, assign ownership, integrate AI into SDLC governance
AI Applications	Over-permissioned AI agents	Unauthorized access to systems, secrets, cloud resources, and production environments if an agent is compromised or manipulated	Least-privilege access, scoped credentials, permission separation, approval for high-risk actions
AI Applications	Unsafe or unverified autonomous actions	Unintended infrastructure changes, service disruption, data loss, or attacker-controlled actions	Human approval, policy enforcement, dry-run modes, comprehensive logging
AI Applications	AI-generated remediation errors	Incorrect fixes, regressions, new vulnerabilities, and false confidence that issues have been resolved	Validate fixes with testing, expert review, remediation verification, do not auto-close findings
AI Applications	Exposure of cloud vulnerability context	Disclosure of cloud assets, identities, permissions, attack paths, and exploitable weaknesses	Restrict access, treat findings as sensitive, redact unnecessary details, protect prompts and logs
AI Applications	Prompt injection and indirect prompt injection	Manipulated model behavior, unauthorized tool use, data disclosure, and compromised AI decisions	Treat external content as untrusted, isolate system prompts, restrict tool access, validate outputs
AI Applications	RAG and knowledge-base exposure	Leakage of confidential documents, source code, customer information, and regulated data	Document-level access controls, permission-aware retrieval, avoid indexing sensitive data, monitor outputs
AI Applications	Model and data poisoning	Reduced model accuracy, targeted manipulation, unsafe recommendations, and compromised AI behavior	Validate training and retrieval data, track provenance, monitor model behavior, restrict data modification

In this article:

• Traditional AppSec vs. AI Security vs. Agentic AI Security
• Why AI Security Matters
• The Main AI Security Risks in Development Workflows
• The Main AI Security Risks in AI Applications
• AI Security Tools and Technologies
• AI Security Best Practices

Traditional AppSec vs. AI Security vs. Agentic AI Security 

Traditional application security (AppSec) protects software by identifying vulnerabilities in source code, dependencies, APIs, infrastructure, and runtime environments. Its goal is to prevent attackers from exploiting software flaws through practices such as secure coding, vulnerability scanning, penetration testing, and runtime protection. While these controls remain essential for AI-enabled applications, they do not address threats that are unique to machine learning models or autonomous AI systems.

AI security expands AppSec by protecting the models, data, prompts, and inference pipelines that power AI applications. In addition to securing the surrounding application, organizations must defend against attacks such as prompt injection, training data poisoning, model extraction, jailbreaks, insecure retrieval pipelines, and unauthorized disclosure of sensitive information through model outputs. AI security also introduces governance controls for model usage, data access, and AI-generated content.

Agentic AI security extends these protections further by focusing on AI systems that can plan, make decisions, invoke tools, and perform multi-step actions with limited human involvement. Because AI agents often receive broad permissions across business systems, they introduce risks that go beyond incorrect model outputs. Security must ensure agents operate within defined boundaries, verify requests before taking actions, restrict tool permissions, maintain complete audit trails, and continuously monitor agent behavior for misuse or compromise.

Aspect	Traditional AppSec	AI Security	Agentic AI Security
Primary focus	Securing applications and infrastructure	Securing AI models, data, and AI-enabled applications	Securing autonomous AI agents and their actions
Protected assets	Source code, APIs, servers, containers, dependencies	Models, prompts, embeddings, training data, inference pipelines	Agents, tool integrations, workflows, permissions, memory
Common threats	SQL injection, XSS, RCE, vulnerable libraries, API abuse	Prompt injection, model theft, data poisoning, jailbreaks, RAG attacks	Excessive permissions, malicious tool use, unauthorized actions, agent hijacking
Typical controls	SAST, DAST, dependency scanning, WAF, IAM	Prompt filtering, model monitoring, data validation, output guardrails, model access controls	Least-privilege permissions, action approval, tool restrictions, policy enforcement, continuous agent monitoring
Key challenge	Preventing software exploitation	Ensuring AI systems behave securely and protect sensitive data	Ensuring autonomous systems act safely, predictably, and within organizational policy

Why AI Security Matters 

1. AI Tools Are Becoming Part of Business-Critical Workflows

AI is now integrated into core business processes such as customer support, fraud detection, and supply chain optimization. These tools automate decision-making, analyze sensitive data, and interact with customers, making them integral to daily operations. As businesses rely more on AI, the impact of a compromise, whether through data leakage, model manipulation, or unauthorized actions, becomes more severe.

The integration of AI into critical workflows also means that security incidents can disrupt operations on a larger scale than traditional software breaches. For example, an attack on an AI-powered recommendation engine or chatbot could lead to reputational damage, regulatory violations, or financial losses. These risks highlight the need to embed security into every phase of AI adoption and lifecycle management.

2. AI Expands the Attack Surface

The adoption of AI introduces components such as model endpoints, data pipelines, and prompt interfaces, which increase the attack surface. Each of these components can be targeted in ways that traditional software is not, such as by manipulating inputs to trigger unintended behaviors or by accessing sensitive model parameters.

AI systems often rely on third-party models, datasets, and APIs, which can introduce vulnerabilities outside the organization’s direct control. The complexity and interconnectedness of AI architectures make it harder to map and defend all potential entry points, requiring continuous monitoring and specialized defenses.

3. Attackers Are Also Using AI

Threat actors increasingly use AI to automate attacks, evade detection, and craft convincing phishing or social engineering campaigns. For example, generative AI can produce realistic spear-phishing emails, deepfake audio, or synthetic identities at scale, lowering the barrier to entry for sophisticated attacks.

AI also enables attackers to discover vulnerabilities in software and systems more efficiently and to adapt their tactics in real time based on defensive measures. This arms race means that defenders must secure their own AI assets and anticipate AI-powered threats, making proactive security strategies necessary.

The Main AI Security Risks in Development Workflows 

Insecure AI-Generated Code

AI coding assistants can generate working code in seconds, but they do not understand the security requirements of your application. They produce code by predicting likely patterns from training data rather than reasoning about trust boundaries, threat models, or business-specific security rules. 

As a result, generated code may include common vulnerabilities such as SQL injection, command injection, cross-site scripting (XSS), insecure authentication, hardcoded credentials, weak cryptography, or missing authorization checks. It can also introduce business logic flaws, where the code runs correctly but does not enforce the rules the application depends on, such as skipping an ownership check before returning a record or allowing a workflow step to be bypassed. These are harder to spot than injection bugs because nothing in the code looks wrong on its own.. Because the code is often well-formatted and syntactically correct, these issues can be difficult to spot during a quick review.

Impact:

• Introduces exploitable vulnerabilities into production applications
• Increases the likelihood of security incidents and costly remediation
• Creates false confidence because generated code often appears polished and correct
• Expands the security review workload for development teams

Mitigations:

• Require human review for all AI-generated code before merging
• Run static analysis, dependency scanning, and security testing on generated code
• Include secure coding requirements directly in prompts where appropriate
• Treat AI output as untrusted until it has been validated through normal development processes

AI-Suggested Vulnerable or Malicious Packages

Modern AI assistants frequently recommend third-party libraries alongside code examples. While this can speed up development, the recommendations are not guaranteed to be safe, actively maintained, or even real. 

Models may suggest packages with known vulnerabilities, outdated dependencies, abandoned projects, or hallucinated package names. Attackers have demonstrated that they can exploit this behavior by publishing malicious packages under names likely to be suggested by AI, turning package recommendations into a software supply chain attack vector.

Impact:

• Introduces software supply chain vulnerabilities
• Increases exposure to malicious or compromised packages
• Adds unsupported or vulnerable dependencies to projects
• Creates long-term maintenance and patching challenges

Mitigations:

• Verify all AI-recommended packages before installation
• Use approved registries and internal package allowlists
• Review package reputation, maintenance history, and known vulnerabilities
• Pin dependency versions and continuously monitor them for new CVEs

Sensitive Data Leakage Through Prompts, Logs, or Model Training

AI assistants rely on prompts to generate useful responses, but those prompts often contain valuable information about an organization’s systems. Developers may inadvertently paste source code, API keys, customer records, internal documentation, architecture diagrams, or production logs into external AI services. 

Depending on the deployment model and configuration, this information may be stored in service logs, retained for operational purposes, or handled according to the provider’s data policies, creating potential confidentiality and compliance risks.

Impact:

• Exposes confidential source code, credentials, or customer data
• Creates privacy and regulatory compliance risks
• Increases the likelihood of intellectual property leakage
• Complicates incident response and forensic investigations

Mitigations:

• Prevent secrets and sensitive information from being included in prompts
• Use enterprise AI services with appropriate data handling controls
• Apply data loss prevention (DLP) policies to AI interactions
• Train developers on safe AI usage and data classification requirements

Unreviewed AI-Generated Changes

AI tools can generate large volumes of code, documentation, tests, and configuration changes in a matter of seconds. While this accelerates development, it also creates the risk that changes are accepted with minimal review because they appear complete and professionally written. 

AI-generated code may contain subtle security flaws, incorrect assumptions, or unintended logic changes that are easy to overlook, especially when large pull requests combine dozens of generated modifications. 

Impact:

• Allows security vulnerabilities to enter production unnoticed
• Increases the risk of functional and business logic defects
• Reduces confidence in the integrity of code reviews
• Makes large AI-generated pull requests more difficult to evaluate

Mitigations:

• Require peer review for all AI-generated changes
• Keep generated pull requests small and focused
• Validate generated functionality with automated and manual testing
• Prevent direct AI-generated commits to protected branches

Weak Auditability and Accountability

Many organizations are required to demonstrate how software changes were developed, reviewed, tested, and approved. AI-assisted development can make this more difficult if teams do not record when AI tools were used, what prompts generated code, or who ultimately approved the final implementation. 

Without adequate traceability, security investigations, compliance audits, and post-incident reviews become significantly more challenging.

Impact:

• Reduces traceability for security investigations
• Makes compliance and audit activities more difficult
• Creates uncertainty around ownership of generated code
• Weakens governance over AI-assisted development

Mitigations:

• Record when AI tools contribute to development activities
• Maintain audit trails for code reviews and approvals
• Assign clear ownership for all generated code before merging
• Incorporate AI usage into existing secure development lifecycle policies

The Main AI Security Risks in AI Applications 

Over-Permissioned AI Agents

AI agents often need access to tools, APIs, repositories, tickets, cloud accounts, or internal systems to complete tasks. The risk starts when these agents receive broader permissions than they need. An agent that can read, write, deploy, delete, or modify security settings across many systems can cause serious damage if it behaves incorrectly or is manipulated.

Impact:

• Allows a compromised or misled agent to access sensitive systems
• Increases blast radius from prompt injection or tool misuse
• May expose secrets, customer data, source code, or cloud resources
• Can lead to unauthorized changes in production environments

Mitigation:

• Apply least-privilege access to every agent and connected tool
• Use scoped, temporary credentials instead of long-lived broad access
• Separate read-only tasks from write or deploy actions
• Require approval for high-risk actions such as deletion, deployment, or permission changes

Unsafe or Unverified Autonomous Actions

Autonomous AI systems can take actions without direct human review, such as creating tickets, changing configurations, modifying code, sending messages, or calling APIs. This becomes risky when the system acts on incomplete, incorrect, or maliciously influenced information.

Impact:

• Causes unintended changes to applications, infrastructure, or data
• Triggers false remediation, service disruption, or data loss
• Executes attacker-controlled instructions through connected tools
• Makes failures harder to detect before damage occurs

Mitigation:

• Require human approval for sensitive or irreversible actions
• Add policy checks before agents can execute tool calls
• Use dry-run modes and previews for proposed changes
• Log all autonomous actions with inputs, outputs, and decision context

AI-generated remediation errors

AI systems may suggest or apply fixes for vulnerabilities, misconfigurations, or code issues. These fixes can be incomplete, incorrect, or insecure. A remediation may remove a warning without fixing the root cause, break application logic, or introduce a new vulnerability.

Impact:

• Creates a false sense that a security issue has been resolved
• Introduces regressions, outages, or new vulnerabilities
• Wastes engineering time on incorrect fixes
• Makes risk tracking inaccurate if issues are closed too early

Mitigation:

• Validate AI-generated fixes with tests and security scans
• Require expert review for high-severity findings
• Link each fix to the original vulnerability and acceptance criteria
• Avoid auto-closing issues until remediation is verified

Exposure of cloud vulnerability context

AI applications may process cloud security data such as misconfigurations, exposed assets, identities, network paths, vulnerabilities, and attack graphs. This context is highly sensitive because it can show attackers where the organization is weak and how systems are connected.

Impact:

• Reveals exploitable weaknesses in cloud environments
• Exposes asset names, identities, permissions, and network details
• Helps attackers prioritize paths to sensitive systems
• Increases risk if prompts, logs, or AI outputs are leaked

Mitigation:

• Treat cloud vulnerability context as sensitive security data
• Limit which users and agents can access cloud findings
• Redact secrets, account details, and unnecessary identifiers
• Store prompts, outputs, and logs in protected environments with retention controls

Prompt Injection and Indirect Prompt Injection

Prompt injection occurs when an attacker places instructions that manipulate the AI system into a prompt or data source. Indirect prompt injection is especially risky because the malicious instruction may come from a web page, email, document, ticket, repository, or knowledge base that the AI application reads.

Impact:

• Causes the model to ignore system instructions or safety rules
• Can trigger unauthorized tool use or data disclosure
• Allows attackers to manipulate summaries, decisions, or recommendations
• Undermines trust in AI-generated outputs

Mitigation:

• Treat all external content as untrusted input
• Separate system instructions from retrieved or user-provided content
• Restrict tool access based on task, user, and data sensitivity
• Add output validation and approval steps for sensitive actions

RAG and Knowledge-Base Exposure

Retrieval-augmented generation systems connect models to documents, tickets, code, chat logs, and internal knowledge bases. If access controls are weak, the AI application may retrieve and expose information the user should not see.

Impact:

• Leaks confidential documents, source code, or customer data
• Bypasses normal access controls through AI-generated answers
• Exposes sensitive internal discussions or security findings
• Creates compliance risk when regulated data is retrieved incorrectly

Mitigation:

• Enforce document-level access controls during retrieval
• Filter retrieved content based on the user’s permissions
• Avoid indexing secrets or highly sensitive data unless required
• Monitor queries and outputs for sensitive data exposure

Model and Data Poisoning

Model and data poisoning occur when attackers manipulate training data, fine-tuning data, feedback loops, embeddings, or knowledge-base content. The goal is to influence model behavior, hide malicious content, or make the system produce incorrect outputs in specific situations.

Impact:

• Degrades model accuracy and reliability
• Causes targeted incorrect answers or unsafe recommendations
• Can hide malicious instructions inside trusted data sources
• Reduces confidence in AI-assisted decisions

Mitigation:

• Validate and review data before training, fine-tuning, or indexing
• Track data provenance and changes to knowledge sources
• Monitor for unusual model behavior or sudden output changes
• Restrict who can modify training data, embeddings, and source documents

AI Security Tools and Technologies 

Agentic AI Vulnerability Management

Agentic AI vulnerability management focuses on securing AI agents that can plan tasks, call tools, access data, and take actions. These systems create new risks because a model output can trigger operations such as sending messages, changing files, querying databases, or using APIs.

Vulnerability management for agents includes mapping agent permissions, testing tool-use boundaries, reviewing memory and context handling, and detecting unsafe action chains. Security teams should look for excessive privileges, missing approvals, weak isolation, and poor audit trails.

As agentic systems become more autonomous, organizations need processes to evaluate their behavior and security posture. Regular assessments, permission reviews, and runtime monitoring help ensure that agents operate within approved boundaries and cannot be easily abused or manipulated through malicious inputs.

The strongest tools in this category judge a vulnerability by whether it is actually exploitable, not by whether a CVE exists. That distinction needs to be precise. Exploitable means exploitable in your specific environment given your configuration, the prerequisites the vulnerability depends on, and the controls already in place. It does not mean a known exploit exists somewhere in the world, which is a different question answered by threat intelligence.

How Maze helps secure agentic workflows:

Where Maze fits: Maze sits in this category and not the others below. It does not do AI security posture management, data loss prevention for AI, red teaming, or runtime protection for LLM applications. Maze investigates vulnerabilities in code, dependencies, and cloud environments, including the code AI assistants generate and the packages they recommend, and determines which of those are exploitable.

• Investigates vulnerabilities using AI agents. Maze uses specialized AI agents to investigate vulnerabilities across code, dependencies, and cloud environments, helping security teams focus on the issues that present real risk.
• Provides code and cloud context. Maze combines context from source code, runtime, cloud infrastructure, and security controls so investigations reflect how vulnerabilities can actually be exploited.
• Prioritizes exploitable vulnerabilities. Rather than treating every finding equally, Maze checks whether the conditions a vulnerability depends on are actually present on the affected asset, closes the ones that cannot be exploited, and ranks the rest by impact and likelihood in that environment.
• Finds vulnerabilities traditional scanners miss. AI agents can identify business logic vulnerabilities, analyze zero-day threats, and detect issues that conventional SAST and vulnerability scanners may overlook.
• Delivers fixes for human review. Maze traces vulnerabilities to their root cause, verifies proposed fixes, and delivers them as recommendations or pull requests to the developer who owns the code, or into their coding agent. A person still reviews and merges, so nothing is patched autonomously.
• Validates AI-generated results. Multiple validation layers help detect and correct AI hallucinations before results are presented, improving confidence in investigations and remediation recommendations.
• Supports continuous vulnerability management. By continuously analyzing code, dependencies, containers, virtual machines, and cloud assets, Maze helps organizations maintain visibility into evolving security risks across agentic AI environments.

AI Asset Discovery and Inventory

AI asset discovery tools identify where AI is used across an organization. This includes models, prompts, datasets, embeddings, vector databases, AI agents, third-party APIs, and AI-enabled applications. The goal is to create an inventory of AI systems before applying controls.

These tools help security teams understand ownership, data flows, access permissions, and exposure points. Without an inventory, organizations may miss shadow AI usage, unmanaged models, or risky integrations. Asset discovery is often the first step in building an AI security program.

Platforms can continuously monitor environments to detect newly deployed AI assets and changes to existing systems. This visibility helps organizations maintain accurate records and ensure that security policies are applied consistently across AI-related resources.

Key capabilities:

• Comprehensive AI Asset Discovery: Automatically identify AI models, LLM applications, AI agents, prompts, datasets, embeddings, vector databases, model endpoints, and AI-enabled applications across cloud, on-premises, and SaaS environments.
• Shadow AI Detection: Discover unauthorized or unmanaged AI services, personal AI assistants, and third-party AI APIs that employees use outside approved governance processes.
• AI Inventory Management: Maintain a centralized inventory of AI assets with information such as ownership, business purpose, model type, deployment location, lifecycle stage, and associated risks.
• Data Flow Mapping: Map how data moves between AI applications, models, databases, external APIs, and enterprise systems to identify potential exposure points.
• Relationship and Dependency Analysis: Identify connections between AI components, including models, agents, knowledge bases, tools, and supporting infrastructure.
• Continuous Asset Monitoring: Continuously detect newly deployed AI assets, configuration changes, and retired resources to keep inventories accurate.
• Risk-Based Asset Classification: Classify AI assets according to sensitivity, regulatory requirements, business criticality, and data exposure.
• Governance and Reporting: Generate inventory reports that support security audits, compliance initiatives, and AI governance programs.

AI Security Posture Management

AI security posture management tools assess how AI systems are configured and whether they follow security policies. They can detect issues such as exposed model endpoints, weak access controls, unsafe prompt handling, missing logging, or excessive agent permissions.

These platforms help teams prioritize risks across AI applications and development workflows. They often integrate with cloud environments, model registries, code repositories, and CI/CD pipelines, giving security teams a central view of AI-related risk.

Many solutions provide continuous monitoring and remediation recommendations. By identifying misconfigurations early and tracking security posture over time, organizations can reduce exposure and improve governance across the AI lifecycle.

Key capabilities:

• AI Configuration Assessment: Continuously evaluate AI models, applications, and infrastructure for security misconfigurations, insecure defaults, and policy violations.
• Access Control Analysis: Review user permissions, API authentication, service accounts, agent privileges, and identity configurations to identify excessive access.
• Model Endpoint Security: Detect publicly exposed model APIs, weak authentication, insecure network configurations, and missing encryption controls.
• Prompt and Application Security Reviews: Identify unsafe prompt templates, insecure system prompts, and application configurations that increase attack surface.
• Integration Visibility: Assess security risks introduced by integrations with cloud services, model registries, code repositories, CI/CD pipelines, and external AI providers.
• Continuous Risk Monitoring: Track changes in AI security posture over time and alert security teams when new risks or policy violations appear.
• Prioritized Risk Scoring: Rank findings by business impact and asset criticality, and where the tool supports it, by whether the finding is exploitable in the environment rather than by severity alone.
• Remediation Guidance: Provide actionable recommendations for resolving security issues and improving AI security posture.

Data Loss Prevention for AI

Data loss prevention for AI focuses on stopping sensitive information from entering or leaving AI systems in unsafe ways. These tools can inspect prompts, responses, logs, training data, and retrieved documents for secrets, personal data, customer records, or proprietary information.

DLP controls may redact sensitive fields, block unsafe requests, or alert security teams when confidential data is exposed. For AI applications, DLP must work in real time and understand natural language, not just fixed patterns. This is important for chatbots, coding assistants, and RAG systems connected to internal data.

AI-focused DLP solutions can also classify information based on context and apply different policies depending on user roles or data sensitivity. This helps organizations reduce the risk of accidental disclosures while allowing employees to use AI capabilities.

Key capabilities:

• Prompt Inspection: Analyze user prompts before they reach AI models to detect sensitive information such as customer records, credentials, regulated data, or proprietary content.
• Response Inspection: Review AI-generated responses for confidential information before they are delivered to users or downstream applications.
• Sensitive Data Detection: Identify personally identifiable information (PII), protected health information (PHI), payment card data, API keys, passwords, source code, and intellectual property.
• Real-Time Data Protection: Block, redact, tokenize, or mask sensitive information during AI interactions without disrupting legitimate workflows.
• Context-Aware Classification: Classify information based on context and meaning rather than relying only on predefined keywords or regular expressions.
• Policy Enforcement: Apply different DLP policies based on user identity, department, application type, data classification, or regulatory requirements.
• RAG Data Protection: Monitor retrieved documents and knowledge base content to prevent unauthorized disclosure of sensitive internal information.
• Alerting and Audit Logging: Generate alerts and maintain detailed logs for policy violations, attempted data exfiltration, and compliance reporting.

AI Red Teaming Platforms

AI red teaming platforms test AI systems by simulating attacks against models, prompts, agents, and connected tools. They can check for prompt injection, jailbreaks, data leakage, unsafe outputs, excessive permissions, and policy bypasses.

These platforms help teams find weaknesses before attackers do. Some support automated testing across many prompts and scenarios, while others help human testers run deeper assessments. The results can guide fixes and validate whether security controls work as expected.

As AI applications become more complex, red teaming plays an important role in security validation. Regular testing helps organizations measure the effectiveness of defenses and identify new attack paths introduced by model updates, integrations, or changing business requirements.

Key capabilities:

• Prompt Injection Testing: Simulate prompt injection attacks to determine whether attackers can manipulate model behavior or override system instructions.
• Jailbreak Evaluation: Test models against known and custom jailbreak techniques to identify weaknesses in safety controls and guardrails.
• Data Leakage Testing: Evaluate whether models expose confidential training data, sensitive prompts, internal documents, or user information.
• AI Agent Security Testing: Assess AI agents for excessive permissions, insecure tool usage, unauthorized actions, and privilege escalation risks.
• RAG Security Assessment: Test retrieval pipelines for document poisoning, retrieval manipulation, and malicious knowledge base content.
• Automated Attack Campaigns: Execute large-scale testing across thousands of prompts and attack scenarios to improve assessment coverage.
• Custom Attack Scenario Development: Create organization-specific test cases that reflect internal applications, business workflows, and threat models.
• Security Validation Reporting: Produce detailed reports with identified vulnerabilities, attack success rates, remediation guidance, and security metrics.

Runtime Protection for LLM Applications

Runtime protection tools monitor AI applications while they are in use. They inspect inputs, model outputs, tool calls, and user behavior to detect attacks or unsafe actions. This is important because many AI risks appear during live interactions.

These tools can block prompt injection attempts, filter harmful outputs, prevent sensitive data exposure, and stop risky tool use. They may also enforce policies based on user identity, data sensitivity, or action type. Runtime protection acts as a control layer between the model, the user, and connected systems.

Many platforms provide detailed telemetry and threat detection capabilities. This visibility allows security teams to investigate incidents, identify attack patterns, and improve defenses based on real-world usage and emerging threats.

Key capabilities:

• Real-Time Prompt Monitoring: Inspect prompts as users interact with AI applications to detect malicious inputs, prompt injection attempts, and policy violations.
• Output Security Filtering: Analyze model responses for harmful content, confidential information, policy violations, and unsafe recommendations before delivery.
• Tool and Agent Protection: Monitor AI agent actions, function calls, and external tool usage to prevent unauthorized operations and excessive privileges.
• Adaptive Policy Enforcement: Apply runtime security policies based on user identity, application context, data sensitivity, and requested actions.
• Threat Detection: Identify attacks such as prompt injection, indirect prompt injection, jailbreak attempts, abuse, automated misuse, and suspicious user behavior.
• Sensitive Data Protection: Prevent confidential information from being exposed during AI interactions through real-time filtering and enforcement.
• Security Telemetry: Capture detailed logs of prompts, responses, model decisions, tool calls, and security events for investigation and threat hunting.
• Incident Response Support: Generate alerts, provide forensic data, and integrate with SIEM, SOAR, and other security operations platforms.

Secure Development Tools

Secure development tools help developers build AI features without avoidable risks. They can scan AI-generated code, detect vulnerable dependencies, identify hardcoded secrets, and review infrastructure or configuration changes suggested by AI tools.

These tools also help enforce secure coding standards in AI-assisted workflows. For example, they can require review of AI-generated pull requests, check package reputation, and flag unsafe use of model APIs.

Many organizations integrate these controls into CI/CD pipelines and development environments. By embedding security checks into workflows, teams can identify issues earlier and reduce the cost and complexity of remediation.

Key capabilities:

• AI-Generated Code Scanning: Analyze code produced by AI coding assistants for security vulnerabilities, insecure patterns, and coding errors.
• Secret Detection: Identify hardcoded passwords, API keys, certificates, access tokens, and other sensitive credentials before deployment.
• Dependency Security Analysis: Detect vulnerable, outdated, or malicious open-source packages suggested by AI coding tools.
• Infrastructure Security Validation: Review infrastructure-as-code templates, deployment configurations, and cloud resources generated with AI assistance.
• Secure Pull Request Reviews: Automatically inspect AI-generated pull requests for security issues, compliance violations, and risky code changes.
• Policy Enforcement: Ensure AI-assisted development follows secure coding standards, organizational policies, and regulatory requirements.
• CI/CD Pipeline Integration: Embed automated security testing into build pipelines so AI-generated code is validated before release.
• Developer Guidance and Remediation: Provide developers with actionable recommendations, secure coding alternatives, and remediation advice directly within development workflows.

AI Security Best Practices 

Identify and Inventory All AI Usage

Organizations cannot secure AI systems they do not know about. The first step in any AI security program is creating an inventory of AI assets, including models, agents, datasets, vector databases, AI-powered applications, third-party AI services, and development tools used across the organization. Special attention should be given to shadow AI, where employees use AI tools without approval or oversight.

Maintaining an accurate inventory provides visibility into where sensitive data is processed, which systems have AI capabilities, and how AI components interact with business processes. This visibility supports risk assessments, compliance efforts, and security monitoring. The inventory should be updated as new AI tools and services are introduced.

Apply Least Privilege to AI Tools and Agents

AI applications and agents should have access only to the resources necessary to perform their intended functions. Excessive permissions increase the potential impact of prompt injection attacks, compromised accounts, or unexpected model behavior. Access to databases, APIs, files, and administrative functions should be scoped and reviewed.

For AI agents that can perform actions autonomously, permission controls are especially important. Organizations should separate high-risk actions from routine tasks, require approvals for sensitive operations, and monitor agent activities. Applying least privilege reduces the impact of malicious attacks and accidental mistakes.

Protect Sensitive Data From AI Exposure

Sensitive information should be protected throughout the AI lifecycle, including data collection, model training, inference, logging, and storage. Organizations should establish clear policies defining what information can be shared with AI systems and under what circumstances. Personal data, financial records, intellectual property, and credentials require protection.

Technical controls such as encryption, data masking, tokenization, and DLP solutions can reduce the risk of exposure. Teams should review prompts, logs, and training datasets for sensitive content and ensure that third-party AI providers meet security and privacy requirements. Data protection helps prevent accidental disclosures and exfiltration attempts.

Secure RAG Systems and Knowledge Bases

RAG architectures introduce additional security considerations because language models can access external information repositories. Knowledge bases often contain internal documents, customer information, technical documentation, or proprietary business data that must be protected from unauthorized access.

Organizations should implement access controls at the retrieval layer, ensuring users can access only information they are authorized to view. Retrieved content should be filtered and validated before inclusion in model responses. Regular reviews of knowledge-base content, query patterns, and system outputs can help identify exposure risks and prevent sensitive information from being leaked through AI-generated responses.

Align AI Governance With Enterprise Frameworks

AI governance should not operate separately from existing security, risk management, and compliance programs. Organizations should incorporate AI systems into established governance frameworks, policies, and control processes. This includes risk assessments, change management, incident response, vendor management, and regulatory compliance activities.

Aligning AI governance with enterprise frameworks creates consistency and reduces operational complexity. Security teams can use existing controls while adapting them to address AI-specific risks. This approach also helps organizations demonstrate compliance with emerging AI regulations and industry standards.

Keep Remediation Actions Controlled and Reversible

When remediating AI vulnerabilities, organizations should avoid allowing AI systems to make direct changes to production environments without oversight. Human review provides an opportunity to validate proposed changes, assess their impact, and ensure they align with security and business requirements.

Using pull requests (PRs) for remediation helps maintain control, transparency, and auditability. PR workflows create a documented record of proposed changes, approvals, testing results, and deployment decisions. They also make it easier to roll back changes if a remediation introduces unexpected behavior. By keeping security fixes reviewable and reversible, organizations can improve accountability while reducing the risk of unintended consequences.

How Maze Helps You Manage AI Security Risks with Agent-Driven Investigation

How Maze Helps You Manage AI Security Risks with Agent-Driven Investigation

AI is changing the problem from both directions. Coding agents ship more code with less review, and attackers find and exploit flaws faster. Maze uses AI agents to investigate every vulnerability across your code and cloud, prove which ones are genuinely exploitable, and deliver verified fixes to the developers who own them.

To see how AI agents investigate and resolve the vulnerabilities that matter across your environment, explore the Maze platform.