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.
State of AI in the Cloud [2025]
AI data security is critical, but staying ahead of emerging threats requires up-to-date insights. Wiz’s State of AI Security Report 2025 reveals how organizations are managing data exposure risks in AI systems, including vulnerabilities in AI-as-a-service providers.
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AI is moving faster than security
By 2025, almost 80% of enterprises will have adopted AI in some form—up from just 55% in 2023. From copilots automating internal workflows to generative AI powering customer experiences, AI adoption is moving faster than any previous wave 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.
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’s unmonitored, unprotected, and largely misunderstood.
Over 75% of CISOs report growing concerns about emerging AI security risks, but few feel equipped with the right tools or frameworks to tackle the problem at scale.
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—you need 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.
Meet the AI threat landscape in 2025
AI threats are evolving as fast as the tech itself. Two 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 2025? 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
At the heart of any robust 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: You get real-time risk insights, prioritized security actions, and governance across all teams and environments.
For most organizations, this is the first step in scaling secure AI at an enterprise level.
AI security posture management (AI-SPM)
AI-SPM forms the foundation of your AI security strategy. It acts as your control plane, providing visibility and enforcement across development, deployment, and runtime.
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
Top vendors:
Wiz AI-SPM: AI-SPM integrated with CNAPP for unified AI and cloud risk management
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
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
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: Protects AI data by identifying exposure risks to training datasets, models, and pipelines across your cloud environment.
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
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: Secures your AI supply chain by detecting risks in third-party models, open-source packages, and infrastructure components powering your AI workloads.
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.
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.
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!
What’s next? Towards proactive AI security
Your organization can’t afford blind spots in the AI layer. Whether you are fine-tuning LLMs, deploying agents, or integrating AI into customer workflows, every model, API, and identity adds to your expanding attack surface.
A modern AI security posture management (AI-SPM) platform unifies your efforts by providing:
Full lifecycle coverage—from prompt to pipeline, ensuring end-to-end protection
Graph-based risk prioritization—across apps, data, and identities to focus on what matters most
Unified alerts that cut through the noise and pinpoint real risks
Security shouldn’t slow down your AI roadmap—it should accelerate it.
👉 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.
