Why AI Security Tools Are Different and 9 Tools to Know in 2026

As enterprises embed AI into business-critical workflows, security tools built for traditional applications fall short. AI systems introduce new attack surfaces that demand dedicated AI model security, from prompt injection to model theft and data poisoning. In this article, we explore why AI security tools must differ from legacy tools, and we present nine that are rising to meet the challenge. This article is part of a series of articles on AI Security.

Editor’s note: Updated the article to cover recent market trends and to reflect features and capabilities in 2026.

What are AI security tools?

As companies embed AI models into their applications, they face risks that traditional security tools weren’t designed to catch, such as prompt injection, data leakage, model poisoning, and shadow AI. Addressing these threats requires a new class of security tools built specifically for AI risk.

These AI security tools typically focus on three areas: securing the data that trains and powers models, hardening the models themselves against theft and manipulation, and protecting the runtime environment where models interact with users. By covering these dimensions, they help defend against attacks like adversarial inputs, model extraction, and data poisoning.

AI security tools can be applied both pre-deployment, to identify weaknesses early, and in production, to monitor behavior and enforce protections. Many also integrate across the AI development lifecycle, bringing continuous testing, compliance, and governance into the process. The result is greater assurance that AI-powered applications remain secure, resilient, and trustworthy.
AI security tools don’t exist in a vacuum. They sit within a larger landscape of risks and defenses that every CISO and AppSec leader needs to consider. For example, retrieval-augmented generation pipelines introduce unique risks, which we explore in our guide on RAG security. Unauthorized AI connectivity, such as hidden Model Context Protocol integrations, can also expose critical blind spots, as detailed in our post on MCP security.

These risks fall under the broader discipline of generative AI security, which is quickly becoming a board-level priority. As adoption accelerates — see our roundup of generative AI statistics — organizations are realizing that model-level threats like extraction and theft must be addressed head-on. Our guide to protecting your AI models walks through practical strategies.

Beyond the threats, there are solutions. From AI guardrails that constrain model behavior, to AI security solutions that clarify the role of AI in security vs. securing AI itself, and AI security posture management tools that provide visibility across the AI estate — the ecosystem is evolving rapidly.

Why traditional security isn’t enough for AI

AI security requires a different approach because the attack surface of AI applications is different from traditional applications. Models are dynamic and non-deterministic systems whose behavior changes based on input prompts, fine-tuning, and continuous learning. They are also often built on top of third-party APIs or pretrained weights, introducing risks from external sources that may be opaque to security teams.

Once deployed, AI systems can degrade or be manipulated over time without any obvious changes to the codebase, making post-deployment monitoring as critical as pre-deployment testing. Effective protection must address data integrity, model integrity, and inference-time defenses in addition to conventional infrastructure security.

Why AI security tools are different:

• Must inspect and filter inputs for malicious prompt injection
• Require monitoring for output anomalies and hallucinations in real time
• Need controls to prevent sensitive data leakage from training sets or inference logs
• Must track and govern model versions to detect unauthorized changes or “shadow models”
• Require integration of security at the model lifecycle level, not just the network or application layer

Related content: Read our guide to AI security solutions

AI security tools market trends 

Market size and growth

According to recent market research, the AI security platforms market is expanding rapidly as organizations invest in technologies that can defend against increasingly complex cyber threats. The market is projected to grow from USD 3,506.2 million currently to USD 25,611.2 million by 2035, representing a compound annual growth rate (CAGR) of 22%.

This growth reflects a broader shift toward automated and intelligence-driven security systems. AI platforms can process large volumes of security data, identify patterns, and react to threats faster than traditional tools. As a result, many organizations are adopting AI-based security platforms as a core part of their cybersecurity infrastructure.

Key drivers behind market expansion

One of the main drivers of market growth is the increasing sophistication and frequency of cyberattacks. Organizations face threats such as zero-day exploits, ransomware, phishing campaigns, and data breaches. AI-powered security tools help address these threats by detecting anomalies and responding to attacks in real time.

The growing complexity of digital systems also contributes to demand. As companies adopt cloud platforms, distributed applications, and large-scale digital services, traditional security tools struggle to keep up. AI security platforms provide automated threat detection and behavioral analysis, helping security teams monitor large environments more effectively.

Industry adoption and use cases

AI security platforms are being adopted across many industries that handle sensitive data or operate complex digital systems. Financial services and banking represent the largest application sector, accounting for around 42% of demand.

Financial institutions rely on these platforms to detect fraud, monitor transaction anomalies, and maintain regulatory compliance. AI tools can analyze large transaction volumes and identify suspicious behavior even under changing market conditions. Other sectors increasingly adopting AI security technologies include healthcare, government, retail, and enterprise IT services.

Types of AI security tools

AI security tools are divided into three main types:

Pre-deployment AI security tools

These tools secure AI systems before they are exposed to real users. They identify weaknesses in models, pipelines, and dependencies to reduce the attack surface before launch. Capabilities include model risk assessment, adversarial testing, prompt injection simulation, dependency scanning, and alignment verification. They are often integrated into the CI/CD pipeline to enforce security gates and ensure that only vetted models reach production.

Post-deployment AI security tools

These tools protect AI systems in live environments. They operate at runtime to detect and block malicious inputs, prevent model abuse, and enforce compliance with policies. Common functions include real-time prompt filtering, output moderation, abuse detection, and anomaly monitoring. They may also include response throttling, automated model rollback, and integration with security information and event management (SIEM) platforms.

AI data protection tools

These tools focus on protecting the data used by AI models during training, fine-tuning, and inference. They implement privacy-preserving techniques such as differential privacy, data masking, and synthetic data generation. In live systems, they scrub sensitive information from retrieval-augmented generation (RAG) pipelines, redact confidential content from logs, and ensure compliance with regulations like GDPR or HIPAA. When AI tools reference external corpora (as in RAG systems), new risks emerge. See our guide to RAG security for securing that architecture. They help prevent both inadvertent leaks and targeted exfiltration attacks.

The following table summarizes the differences between these types:

Pre-deployment AI security tools 

Main focus: Harden AI applications before they go live

From identifying risky AI components to red teaming and AI security testing, these tools help secure models and pipelines before production.

1. Mend.io

Mend.io is a security platform built for every risk, across application security and AI security — securing the code layer, the AI layer, and the attack surface between them.  The platform includes SCA and SAST, as well as Mend AI to secure AI components. Mend AI automatically inventories AI components like AI models, agents, MCPs, and RAGs, enriches all components with compliance and security information, applies policies, and simulates adversarial attacks with AI red teaming. Integrated into CI/CD pipelines, Mend AI brings continuous AI risk management into existing pre-development stages.

Key features:

• AI component inventory: Discovers and catalogs models, datasets, and APIs
• Prompt hardening: Enforces guardrails against injection and manipulation
• AI red teaming: Tests models against adversarial and jailbreak attacks
• Policy governance: Maps risks to compliance and security standards
• End-to-end coverage: Extends AppSec protection across code, open source, and AI

2. Cisco AI Defense

Cisco AI Defense is an enterprise AI security platform that helps organizations secure both internally developed and third-party AI systems. The platform focuses on identifying AI assets across distributed environments, assessing models for vulnerabilities, and applying guardrails that mitigate risks such as prompt injection, denial-of-service attacks, and data leakage. 

Key features:

• AI asset discovery: Identifies AI workloads, models, applications, data sources, and users across distributed cloud environments to provide visibility into AI usage.
• Model and application validation: Uses automated testing and red-teaming techniques to detect vulnerabilities and safety risks in AI models at scale.
• Runtime threat protection: Applies guardrails that block adversarial inputs, harmful responses, and AI-specific attacks during production use.
• AI access governance: Monitors and controls employee interactions with third-party AI tools to reduce data exposure and enforce organizational policies.
• AI supply chain risk management: Provides governance and security controls for AI models and related files used across development and deployment pipelines. 

Source: Cisco AI Defense

3. HiddenLayer

HiddenLayer provides automated red teaming capabilities to test AI systems for vulnerabilities before deployment. The platform focuses on continuously stress-testing models, agents, and other AI systems to identify weaknesses such as prompt injection, jailbreaks, and data poisoning. Automated testing allows security teams to scale vulnerability assessments across multiple models and environments.

Key features:

• Automated AI red teaming: Continuously tests models and AI applications to identify vulnerabilities before attackers exploit them.
• Prompt injection and jailbreak testing: Simulates attacks that manipulate model behavior through adversarial inputs.
• Scalable model testing: Runs automated tests across large numbers of models, agents, and AI systems.
• Vulnerability scoring and tracking: Provides metrics and trend analysis to measure security posture and track improvements over time.
• Scheduled and on-demand scans: Enables continuous security testing as well as targeted scans after model updates or system changes.

Source: HiddenLayer

Post-deployment AI security tools 

Main focus: Monitor, control, and defend live AI systems

These tools enforce guardrails at runtime to prevent abuse, model exploitation, or policy violations.

4. Prompt Security

Prompt Security is an AI security platform that protects organizations from risks associated with generative AI usage. It provides runtime protections for AI applications, employee use of AI tools, and AI development workflows. The platform focuses on identifying threats such as prompt injection, shadow AI usage, data leakage, and harmful model outputs. It can be deployed as a cloud service or on-premises and integrates with existing enterprise systems.

Key features:

• Prompt injection protection: Detects and blocks malicious prompts that attempt to manipulate model behavior.
• Shadow AI detection: Identifies unauthorized use of AI tools within the organization to reduce unmanaged risk.
• Data leak prevention: Prevents exposure of sensitive information through prompts, responses, or AI code assistants.
• Runtime policy enforcement: Applies governance controls to AI applications, agents, and model interactions.
• AI red teaming capabilities: Tests generative AI applications for vulnerabilities through simulated attacks. 

Source: Prompt Security

5. HiddenLayer

HiddenLayer also provides runtime security capabilities that monitor and defend AI applications once deployed. Its platform combines asset discovery, supply chain security, adversarial attack simulation, and runtime monitoring to protect AI systems across the lifecycle. The runtime protection layer acts as a security control that detects and responds to adversarial attacks against generative, predictive, and agentic AI systems.

Key features:

• AI asset discovery: Creates an inventory of AI models, applications, and related assets across environments.
• AI supply chain security: Identifies risks in proprietary and third-party models used in AI systems.
• AI attack simulation: Continuously tests defenses to identify weaknesses and validate protections.
• Runtime security controls: Detects and responds to adversarial attacks targeting live AI applications.
• AI guardrails: Enforces policies that prevent misuse, data leakage, and model manipulation.

Source: HiddenLayer

6. AIM Security

AIM Security provides an enterprise AI security platform that protects AI applications, agents, and employee interactions with AI systems. The platform focuses on identifying shadow AI usage, enforcing governance policies, and protecting AI applications from runtime attacks. It also includes capabilities for monitoring AI interactions and managing security posture throughout the AI development lifecycle.

Key features:

• AI firewall protection: Monitors and secures interactions between users, AI agents, and models to prevent runtime attacks.
• Shadow AI discovery: Detects employee use of public AI tools and provides visibility into organizational AI adoption.
• AI interaction monitoring: Analyzes prompts, responses, and AI workflows to identify risky or anomalous activity.
• AI security posture management: Continuously identifies vulnerabilities and misconfigurations in AI systems during development.
• Lifecycle risk detection: Scans AI models and agents for security and compliance risks before they reach production. 

Source: AIM

AI data protection tools

Main focus: Secure the data that trains, feeds, and interacts with AI

From privacy in training datasets to runtime RAG scrubbing, these tools protect data against leakage, misuse, and compliance failures.

7. Protect AI

Protect AI provides a platform that secures AI systems across the machine learning lifecycle. The platform integrates multiple tools that protect AI models from selection and testing through deployment and runtime monitoring. Its architecture combines red teaming, threat detection, and model monitoring capabilities to address vulnerabilities in AI models, pipelines, and runtime environments.

Key features:

• Model security and threat detection: Identifies vulnerabilities and threats affecting AI models during development and deployment.
• AI red teaming: Provides testing capabilities that simulate attacks against AI applications to identify weaknesses.
• Runtime protection: Detects and blocks AI threats during live system operation.
• Lifecycle security coverage: Secures AI systems from model import and testing through deployment and monitoring.
• Threat intelligence integration: Uses research from a large security community to identify emerging vulnerabilities and attack techniques. 

Source: Protect AI

8. Arthur.ai

Arthur.ai provides an AI reliability and governance platform focused on monitoring and evaluating AI systems across their lifecycle. The platform enables organizations to track model performance, monitor AI interactions, and enforce governance policies for AI applications. It supports multiple AI system types, including generative AI, traditional machine learning, and agentic systems, and provides tools for continuous evaluation and oversight.

Key features:

• AI lifecycle evaluation: Continuously evaluates AI models to monitor performance and reliability across development and production.
• Agent discovery and governance: Identifies AI agents and enforces governance policies across deployed systems.
• AI guardrails: Prevents misuse and problematic outputs through policy-based controls.
• Model-agnostic support: Works with multiple AI frameworks, models, and deployment environments.
• Real-time monitoring dashboards: Provides visibility into AI system performance and interactions through customizable analytics tools. 

Source: Arthur.ai

9. Relyance AI

Relyance AI provides a platform focused on protecting sensitive data used in modern applications, including AI systems. The platform uses dynamic data security posture management to track how data flows across code, cloud environments, and AI models. By continuously monitoring these data flows, the platform helps detect policy violations, identify sensitive information exposure, and enforce compliance requirements.

Key features:

• Data flow visibility: Tracks how data moves from source code through cloud infrastructure and AI systems.
• AI data governance: Provides visibility into how AI models use training data, prompts, and outputs.
• Real-time risk detection: Identifies sensitive data exposure, unauthorized access, and policy violations.
• Shadow AI detection: Discovers unapproved AI services and tools operating within the organization.
• Compliance automation: Supports regulatory requirements through automated data mapping, monitoring, and privacy controls. 

Source: Relyance AI

AI security with Mend AI

AI systems don’t just run models — they ingest sensitive data, evolve through user interaction, and introduce unpredictable behavior into your applications.

Effective AI security requires full lifecycle coverage: visibility into what’s in your AI supply chain, hardened system prompts, behavioral testing, and enforced governance policies.

Mend AI automates discovery and risk assessment of AI components across your codebase, tests your conversational AI for threats like prompt injection and data exfiltration, and enforces compliance policies throughout the software development lifecycle so you stay protected from build time to runtime.