AI Security Solutions In 2026: Tools To Secure AI - wiz.io

AI has become a core asset for modern businesses, on par with data: It drives workflows, customer experiences, and operational models across every sector. With self-hosted models now accounting for more than 70% of in-cloud AI workloads, organizations are taking greater control of their AI stacks—and with that comes greater security responsibility.  If you're a CISO, security architect, developer, or GRC leader, you're probably asking: How do we secure AI systems without slowing innovation down? In this guide, we'll help you navigate the rapidly evolving landscape of AI security best practices and show how AI security posture management (AI-SPM) acts as the foundation for scalable, proactive AI risk management.  25 AI Agents. 257 Real Attacks. Who Wins? From zero-day discovery to cloud privilege escalation, we tested 25 agent-model combinations on 257 real-world offensive security challenges. The results might surprise you 👀 Your work email here See which agent won AI is moving faster than security From copilots automating internal workflows to generative AI powering customer experiences, AI adoption is seemingly moving faster than previous waves of technology. But AI security? It’s struggling to keep up. Traditional security frameworks weren’t built for non-deterministic models, dynamic prompts, or AI agents making real-time decisions. As a result, new threats are emerging across the stack—shadow AI deployments, unrestricted data exposures, and adversarial manipulations of model behavior—while most organizations lack full visibility into their AI environments. Top AI challenges according to survey results from the AI Readiness Report Meanwhile, the threat landscape keeps evolving quickly (and regulatory pressure is also mounting with global regulations like the EU AI Act kicking in). The result? A widening AI risk surface that can feel unmonitored, unprotected, and largely misunderstood. The bottom line: Scaling AI securely requires an AI-first strategy, one that’s purpose-built for dynamic systems, rapid change, and evolving threats. Quick primer: What is AI security? AI security isn't just about locking down your chatbot API or encrypting a model file. It’s a full-stack discipline that protects models, data pipelines, infrastructure, interfaces, and behavior throughout the entire AI lifecycle. To build safeguards at every layer of your AI ecosystem, AI security spans multiple domains: 1. Cloud & infrastructure security  Actionable steps: Secure your compute environments from misconfigurations and unauthorized access, preventing vulnerabilities that could expose sensitive data or compromise models. 💡Example assets: GPU clusters, model deployment pipelines, inference endpoints 2. Data governance & protection Actionable steps: Safeguard training datasets and inference logs from unauthorized access and ensure compliance with privacy regulations (e.g., masking PII in prompts). 💡Example assets: Training datasets, inference logs, labeled data repositories 3. Identity & permissions for AI workloads Actionable steps: Enforce least privilege for AI workloads, including service accounts and API keys, to minimize the risk of breaches from over-permissioned systems. 💡Example assets: LLM agents, service accounts, API keys 4. Application & API security  Actionable steps: Protect AI-powered web apps and model-serving APIs from misuse, which could undermine the model's reliability and potentially expose sensitive data or create compliance issues. For example, prompt injection attacks can manipulate model behavior through adversarial prompts specifically designed to steer the AI toward unsafe actions or cause it to generate incorrect outputs. 💡Example assets: GenAI web apps, model-serving APIs, internal chatbots 5. Runtime observability & behavior monitoring Actionable steps: Monitor AI models in production for anomalies like toxic outputs or data exfiltration attempts, using logs and telemetry to ensure secure runtime behavior. 💡Example assets: LLM output logs, telemetry data, prompt histories To secure each layer—cloud, data, permissions, and behavior—consider posture-aware controls that understand the unique risks AI presents. This is the essence of modern AI security solutions: They detect, prioritize, and mitigate risks based on how AI systems function and behave within your specific environment. GenAI Security Best Practices [Cheat Sheet] Learn what makes Wiz the platform to enable your cloud security operation Your work email here Download Meet the AI threat landscape in 2026 AI threats are evolving as fast as the tech itself. A few years ago, nobody was talking about prompt injection attacks. Today they’re a top concern for any team using LLMs that have memory or interact with external tools, with attackers capable of corrupting even state-of-the-art models like Google’s Gemini.  So what other risks should you be aware of in 2026? While there are many (you can learn more in our AI Security Risks guide), here are three other critical threats you definitely need to prepare for: Model extraction: Attackers reverse-engineer your AI models to steal intellectual property or replicate their functionality. This could compromise proprietary algorithms or expose your competitive edge. Training data poisoning: By introducing malicious data into your training datasets, attackers can undermine model integrity, causing models to produce faulty or biased predictions. Over-permissioned AI agents: When AI systems are given more access than they need—whether to data, systems, or services—there’s a greater risk that an attacker could exploit this excessive access, leading to larger-scale breaches. Understanding your AI security choices As the need for AI security grows, so does the range of AI security solutions available. But AI security is not a one-size-fits-all approach. Depending on where you are on your AI journey and the scale of your operations, the tools you need will vary.  Broadly, AI security tools are evolving in three primary categories: Comprehensive AI security platforms for full-lifecycle visibility, risk management, and governance across your AI environment AI lifecycle-specific tools for deeper security at specific stages of the AI lifecycle, in development and production AI use case–specific solutions for particular types of AI workloads—especially LLMs, autonomous agents, or third-party AI supply chains Let’s break down the categories (and main sub-categories) to understand how these solutions fit your organization's needs. Layer 1: Comprehensive AI security platforms The heart of a strong AI security strategy is a comprehensive platform that provides centralized visibility across all your AI systems. These platforms complement specialized tools by providing an overarching view of your AI environment: Real-time risk insights, prioritized security actions, and governance across all teams and environments. For many organizations, this is the first step in scaling secure AI at an enterprise level. AI security posture management (AI-SPM) tools AI-SPM forms the foundation of an AI security strategy. It acts as your control plane, providing visibility and enforcement across development, deployment, and runtime. Leading AI-SPM Solution: Wiz AI-SPM: A comprehensive AI security solution that builds a dynamic inventory of your AI estate, detecting Shadow AI, deployed agents, and unmanaged resources. It prioritizes risks by analyzing infrastructure context, identifying architectural flaws like exposed inference endpoints or insecure model configurations before they can be exploited. Other AI-SPM Solutions Microsoft Defender for Cloud: CSPM with AI and ML asset support Palo Alto Networks Prisma Cloud AI-SPM: CSPM with AI/ML visibility and threat detection Key capabilities: Continuous discovery and inventory of AI assets across your environment Risk assessment for AI-specific misconfigurations and vulnerabilities—including model-serving endpoints, over-permissioned agents, and prompt injection risks Identity, access, and permissions management for AI workloads  Code-to-cloud correlation to trace AI model exposure back to the originating code, pipeline, or misconfiguration for remediation prioritization Integration with broader cloud security frameworks AI lifecycle coverage: Development ➔ deployment ➔ production operations Risks addressed: Shadow AI, exposed endpoints, misconfigured services, over-permissioned AI agents, compliance violations Best for: Organizations at any AI maturity level seeking a foundation for their AI security strategy, especially those with diverse AI initiatives spanning multiple teams and projects Layer 2: AI lifecycle–specific tools Once you've established foundational visibility with AI-SPM, lifecycle-specific solutions allow you to address security challenges at each stage of your AI journey.  These specialized tools focus on securing specific phases—from development to data preparation to production—providing deeper controls for specific aspects of your AI security strategy.  Organizations typically add these solutions as they mature their AI practice and develop more sophisticated use cases. AI development security tools Development-phase security tools protect your AI at its source, addressing vulnerabilities before they reach production. Key capabilities: Secure coding practices for AI development environments Static and dynamic scanning of AI code and notebooks Dependency scanning for ML frameworks and libraries Model robustness, fairness, and explainability testing AI lifecycle coverage: Design ➔ development ➔ testing Risks addressed: Vulnerable or malicious dependencies, model bugs, unsafe development practices, adversarial vulnerabilities in models Vendors:  Protect AI: AI security platform with advanced model scanning via ModelScan and notebook security with open-source NB Defense Robust Intelligence: AI stress-testing platform with automatic adversarial and robustness testing for models Weights & Biases: AI experiment tracking tool with built-in security Best for: Data science and ML engineering teams building custom models or fine-tuning foundation models, especially for high-risk use cases Watch 10-min AI Guided Tour Interactive walkthrough of how Wiz helps security teams secure AI workloads across the cloud with full visibility. Your work email here Start Tour AI data security solutions Data is the bedrock of AI, making specialized data security tools essential for protecting sensitive information throughout the AI lifecycle. Key capabilities: Sensitive data discovery and classification in AI datasets Redaction and masking tools for protecting PII Encryption and tokenization of training data AI lifecycle coverage: Data collection ➔ data transformation ➔ model training Risks addressed: PII in training data, data leakage through model outputs, compliance violations, unauthorized access to sensitive datasets Vendors: Wiz AI-SPM: An AI data security tool designed to safeguard model inputs and training material. It actively scans datasets and vector stores for sensitive information (PII, secrets, and IP) and enforces strict governance by mapping exactly which models can access this data, alerting you to unauthorized flows or public exposure risks. Sentra: Cloud-native DSP with AI-specific context Immuta: Data access control platform with dynamic data-policy enforcement for AI training data BigID: Data privacy and governance platform with sensitive-data discovery and compliance automation Best for: Organizations working with sensitive or regulated data, particularly in healthcare, financial services, or government sectors AI runtime security monitoring Once AI systems are in production, runtime security tools provide the continuous monitoring needed to detect and respond to threats, anomalies, and misuse in real time. Key capabilities: Real-time telemetry and anomaly detection Output monitoring and behavioral baselining Integration with SOC/SIEM pipelines for unified security operations AI lifecycle coverage: Deployment ➔ production monitoring ➔ maintenance Risks addressed: Malicious usage patterns, model exfiltration attempts, over-permissioned AI services, data drift affecting security Vendors:  HiddenLayer: ML threat detection platform with real-time protection against model theft and adversarial attacks Protect AI: AI security platform with real-time model behavior monitoring and drift detection via Layer Fiddler: AI observability platform with security-centric anomaly detection and bias monitoring Best for: Organizations with customer-facing or business-critical AI systems that need active protection against exploitation and abuse Layer 3: AI use case–specific solutions The outer layer of your AI security strategy consists of use case–specific solutions that address specialized AI applications and components. These tools are necessary to target the risks that emerge within AI areas like LLMs, autonomous agents, and AI supply chains. LLM security solutions Protect large language models and LLM-powered applications from emerging attack techniques like prompt injection and output manipulation. Key capabilities: Prompt filtering and jailbreak detection Output scanning for sensitive information Customizable guardrails and safety policies Monitoring of LLM usage patterns AI lifecycle coverage: Deployment ➔ production Risks addressed: Prompt injection, jailbreaking attempts, data leakage through responses, memory manipulation Vendors:  Prompt Security: GenAI security platform focused on prompt risk management and attack detection Lakera: AI-native security platform specialized in adversarial prompt protection and secure LLM operations Protect AI: AI security platform with a specialized toolkit—LLM Guard—for securing GenAI apps against attacks Best for: Organizations deploying customer-facing chatbots, internal productivity tools using LLMs, or any application built on foundation models WIZ ACADEMY LLM Security for Enterprises: Risks and Best Practices Read more AI supply chain security Supply chain security tools help secure the components and models you source from third parties, open-source repositories, or external vendors. Key capabilities: AI-specific software bill of materials (SBOM) Model lineage and provenance tracking Risk scoring of external model components Continuous monitoring for vulnerabilities in AI dependencies AI lifecycle coverage: Design ➔ development ➔ deployment Risks addressed: Model tampering, unsafe open-source models, lack of provenance information, licensing compliance issues, insecure external datasets Vendors:  Wiz AI-SPM: An AI supply chain security solution that validates the integrity of your external building blocks. It detects malicious code and vulnerabilities in third-party models (e.g., from Hugging Face) and open-source libraries, ensuring that the external assets powering your AI applications don't introduce hidden backdoors or critical flaws. Protect AI: AI security platform with fast-tracked automated remediation for vulnerabilities discovered in their huntr GenAI bounty platform AI Risk Repository: Living database of AI risks co-created by MITRE ATLAS and Robust Intelligence Anchore: Software supply chain security platform with support for AI/ML artifacts Best for: Organizations building applications on third-party foundation models or using pre-trained components from various sources Finding your AI security strategy Choosing the right mix of AI security best practices and tools isn’t about buying the flashiest solution; it’s about aligning security with where you are on your AI journey. Here’s how to think about it: 1. Assess your AI maturity Ask: “Where am I on my AI journey?” Just starting? Focus on an AI-SPM solution for visibility and wide coverage. Building GenAI apps? Add runtime monitoring, LLM security, and API protections. Reached enterprise-wide AI adoption? You’ll need the full stack: from development security and data governance to compliance tooling and runtime observability. Pro tip Map your AI systems to business impact. Prioritize securing systems that interact with sensitive data, drive customer decisions, or automate high-risk operations. 2. Evaluate tools against your risk profile Don’t get caught in feature checklist hell. When choosing a security solution or tool, focus on: Cloud-native, developer-friendly integrations Cross-team usability (security + ML + compliance) Vendor roadmap agility (AI threats are moving fast!) When it comes to the platform vs. point solutions debate, remember that in most cases, you’ll need a combination: a comprehensive platform for visibility and enforcement, plus targeted tools for specific use cases. Pro tip Use a weighted scorecard to objectively rank tools against your must-haves. Bonus if you get input from GRC, data science, and engineering teams early on! Wiz AI Security starts with visibility Wiz is the unified AI security solution that empowers teams to secure innovation without slowing it down. Instead of relying on disjointed point tools, Wiz embeds AI-SPM directly into your cloud security posture. This delivers the specialized visibility required to manage unique AI risks (like model theft, shadow AI, and prompt injection) alongside your broader cloud infrastructure. Powered by the Wiz Security Graph, Wiz connects model usage, data flows, and permissions into a single, correlated view. This context allows you to validate AI-BOMs, detect critical attack paths, and neutralize risks before they impact production. Wiz transforms how you manage risk with built-in AI capabilities: Mika AI: Investigate complex environment risks using plain language—simply ask, "Which of my LLMs have access to production databases?" to get instant answers. Wiz SecOps AI Agent: Automate the heavy lifting of threat investigation and triage, dramatically reducing response times from hours to minutes. Wiz Defend: Ensure continuous runtime protection that catches AI-specific threats, like prompt injection attempts or data exfiltration, the moment they happen. 👉 Ready to scale AI securely? Schedule a demo and see how AI-SPM can give your team full visibility and control across your entire AI estate.