Why is AI security important?
AI security incidents are escalating rapidly – Forbes reports a 690% increase from 2017 to 2023, with acceleration expected to continue. This surge affects organizations across all industries and sizes, from startups to Fortune 500 companies.
The scope of AI risks extends far beyond what's publicly reported. Current incident data primarily captures breaches at major organizations like Facebook, Tesla, and OpenAI. However, countless unknown risks exist across smaller deployments and private AI implementations. The AI Incident Database is a great resource for anyone who wants to familiarize themselves with common and known types of AI incidents, but more known unknowns and unknown unknowns permeate the AI security ecosystem.
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A quick look at AI’s challenges
AI security complexity stems from the technology's dynamic and interconnected nature, with research from NIST noting that many AI systems contain billions or even trillions of decision points. Unlike traditional software, AI systems involve constantly evolving algorithms, massive datasets, and real-world applications that change behavior over time.
Key complexity factors include:
Technical challenges: Managing sophisticated algorithms and big data processing
Threat landscape: Navigating largely uncharted security territory
Risk diversity: Data breaches, adversarial attacks, ethical implications, and vulnerability management
Employee usage creates additional exposure. Even well-intentioned productivity improvements can lead to data leaks—employees using ChatGPT without updating default privacy settings may inadvertently share proprietary information.
To manage these and more risks associated with AI, organizations need a strategic and well-coordinated security approach that extends traditional cybersecurity measures to the unique needs of AI.
The top 8 AI security best practices
Effective AI security requires cross-functional collaboration between SecOps, DevOps, and GRC teams. This unified approach establishes strong security posture while maintaining AI's transformative business potential.
Team objectives:
SecOps: Lead security framework development and threat monitoring
DevOps: Maintain agile deployment processes for data science teams
GRC: Ensure compliance and governance across AI implementations
The goal is balancing security controls with productivity – enabling safe access to external AI technologies while keeping development processes efficient.
Let’s look at eight best practices for achieving these objectives:
1. Embrace an agile, cross-functional mindset
Agile security frameworks adapt to AI's rapid evolution while providing immediate protection for existing AI deployments. Most organizations already have employees using AI tools and established use cases requiring immediate security coverage.
Implementation approach:
Rapid initial deployment: Create a foundational AI security framework quickly to cover existing processes
Iterative refinement: Use short update cycles to specialize controls for your specific AI requirements
Priority-based evolution: Define mechanisms to address the most critical risks first
This approach ensures immediate security coverage while maintaining flexibility for future AI adoption.
In support of this evolving AI framework definition, establish a culture of open communication around AI security from the very beginning. Encouraging dialogue and collaboration ensures that potential risks are identified and mitigated efficiently while providing a way for security teams to communicate and enforce AI security requirements.
Our research shows that AI is rapidly gaining ground in cloud environments, with over 70% of organizations now using managed AI services. At that percentage, the adoption of AI technology rivals the popularity of managed Kubernetes services, which we see in over 80% of organizations!
2. Understand the threat landscape for AI
AI is a complex subject that requires subject-matter expertise. Collaboration with data science teams or other AI specialists is ideal, yet security teams still need to develop a foundational understanding of the AI threat landscape.
A great starting point is the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework, which defines tactics and techniques threat actors use to compromise AI. When reviewing generalized threats, consider the Department of Homeland Security's three primary categories of vulnerabilities—attacks using AI, attacks targeting AI, and design failures—and select those relevant to your AI ecosystem.
By combing through the MITRE ATLAS framework, security teams can learn from past AI security breaches that affected the AI applications you leverage and incidents that occurred at companies with similar workflows. Remember, the stakes are high, as shown by the security breach caused by Microsoft AI researchers exposing 38 TB of data and by Hugging Face API's exposed tokens.
And to be sure you're completely up to date, track known vulnerabilities of popular AI models and adopted AI technologies through tailor-made online searches and alerts. Wiz’s Cloud Attack Retrospective documented leaked Bedrock credentials being abused within 7 hours for unauthorized LLM use.
State of AI in the Cloud [2025]
Based on the sample size of hundreds of thousands of public cloud accounts, our second annual State of AI in the Cloud report highlights where AI is growing, which new players are emerging, and just how quickly the landscape is shifting.
3. Define the AI security requirements for your organization
Different organizations have different security requirements, and no one-size-fits-all framework exists for AI security.
Organization-centric AI governance policies establish security baselines across data privacy, asset management, ethical guidelines, and compliance standards. These comprehensive policies address AI's unique risk profile and open-source dependencies.
Core governance areas:
Data privacy: Protect sensitive information in training and inference processes
Asset management: Maintain inventory and control over AI models and datasets
Third-party risk: Manage security for open-source AI components and vendor solutions
Compliance standards: Align with regulatory requirements and industry frameworks
Proactive risk management requires continuous evaluation. Essential security controls include ongoing system behavior monitoring, regular penetration testing, and resilient incident response plans.
By ensuring that AI governance policies are regularly revisited and updated, security teams not only ensure compliance but also enable your organization to stay ahead of emerging and evolving security challenges.
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4. Ensure comprehensive visibility
Security can only be achieved for processes that are known and visible.
An AI bill of materials (AI-BOM) provides comprehensive inventory of all AI components and dependencies across your organization's systems. This includes in-house, third-party, and open-source elements that power your AI applications.
AI-BOM implementation process:
Component cataloging: Document all AI models, datasets, frameworks, and libraries
Dependency mapping: Track relationships between AI system components
AI-model cards: Create standardized documentation for each AI application before AI-BOM inclusion
AI-model cards serve as security blueprints, clearly documenting model details, security requirements adherence, and stakeholder responsibilities for each AI system.
Also, keep in mind that AI pipelines hosted in-house should be pushable to production only within established CI/CD processes. This production pattern enables the automated integration of security measures while also minimizing manual errors and accelerating the model's deployment process.
Last but not least, governance processes aimed at visibility should address the risks associated with shadow AI, or the AI that employees are using without the security team's knowledge. Promoting transparency and accountability across your organization and providing a seamless path to introducing new AI technology are the only ways to safeguard against shadow AI.
5. Allow only safe models and vendors
As we’ve seen, AI I is a community-driven discipline. Given the requirements for specialized (big) data, organizations often decide to adopt open-source and third-party AI solutions to unlock the business potential of AI applications. Putting these external AI models in production demands a delicate balance between performance and safety, given the limited security controls available for external technologies.
As part of your AI framework, security teams should establish a rigorous vetting process to evaluate any external AI models and vendors against predefined security requirements. External AI solutions to be vetted include frameworks, libraries, model weights, and datasets. At a minimum, your security requirements should encompass data encryption and data handling, access control, and adherence to industry standards, including certifications. Any external AI solution that successfully passes this process is expected to be trustworthy and secure.
By applying the same rigorous standards to all components, security teams can confidently ensure that your entire AI ecosystem adheres to the highest security protocols, mitigating potential risks and fortifying your organization's defense against emerging threats.
6. Implement automated security testing
Unexpected behavior of AI models in production can lead to unwanted consequences, ranging from degraded user experience to brand damage and legal liabilities. While AI models are non-deterministic in nature and impossible to completely control, comprehensive testing can reduce the risks associated with AI (mis-)behaviors.
Regularly scanning AI models and applications allows security teams to proactively identify vulnerabilities. These checks may include classic tests such as scanning for container security and dependencies or fuzz testing, as well as AI-specific scans via tools such as Alibi Detect or the Adversarial Robustness Toolbox. Make sure your teams test AI applications against misconfigurations or configuration mismatches, which could serve as easy entry points for security breaches. Being able to detect attack paths throughout the AI pipelines, from sensitive training data and exposed secrets to identities and network exposures, before they become threats in production is your goal.
Finally, functional testing is also a necessity. To ensure the safety of core functionalities, functional testing must include AI-specific testing for ethicality, such as bias and fairness analysis, which can help manage what NIST has identified as three major categories of AI bias: systemic, computational and statistical, and human-cognitive.
Incorporating AI security testing within your CI/CD pipeline is the key to reliably identifying and addressing vulnerabilities early in the software development life cycle, and regular testing is the only way to maintain a continuous and proactive security posture.
7. Focus on continuous monitoring
Beyond testing, the dynamic and inherently non-deterministic nature of AI systems requires ongoing vigilance. Focus on continuous monitoring to sustain a secure and reliable AI ecosystem that can successfully address unexpected AI behavior and misuse.
Establish a robust system for monitoring both AI applications and infrastructure to detect anomalies and potential issues in real-time. Real-time monitoring processes track key performance indicators, model outputs, data distribution shifts, model performance fluctuations, and other system behaviors.
By integrating automated alerts and response mechanisms triggered by these real-time threat detection mechanisms, you can promptly identify and respond to security incidents, mitigating risks and minimizing the impact of any adversarial activity.
8. Raise staff awareness of threats and risks
As the AI framework for your organization matures in tandem with advancements in the field of SecOps for AI, security teams need to dedicate time to educating staff about threats and risks so that each individual AI user adheres to basic security guidelines.
First, it’s best practice for security teams to collaborate with data science teams to provide clear and concise security guidelines. The design of these security guidelines should promote experimentation for data science teams as much as possible. This way, you minimize the risk of data science teams neglecting or bypassing security controls to unlock the potential of AI.
After the first security guidelines are in place, you should offer comprehensive training to all employees to equip the entire workforce with the knowledge to use AI safely. Collaborative awareness not only mitigates the risk of involuntary security breaches but also allows employees to directly contribute to the organization's security posture.
AI compliance and regulatory frameworks
The rapid adoption of AI has been followed by a wave of new regulations and compliance frameworks. Organizations must navigate this landscape to avoid legal penalties and build trust with customers. A proactive approach to compliance is essential for responsible AI deployment.
Key regulations and frameworks
Several key frameworks are shaping AI governance globally. The EU AI Act categorizes AI systems by risk level and imposes strict requirements on high-risk applications, with rules for general-purpose AI models slated to apply 12 months after the Act's expected publication.
In the United States, the NIST AI Risk Management Framework (RMF) provides voluntary guidance for managing risks associated with AI, and its goal is to help organizations promote trustworthy and responsible development and use of AI systems. Other industry-specific regulations, such as HIPAA in healthcare, also have implications for how AI is used with sensitive data.
Core principles for compliance
Most AI regulations are built on a set of core principles, including transparency, fairness, accountability, and privacy. Your organization must be able to demonstrate that its AI systems operate according to these principles. This includes maintaining clear documentation (like model cards), auditing for bias, and ensuring data handling practices comply with privacy laws.
Achieving and demonstrating compliance
Use security tools that can map your controls to specific regulatory requirements. This simplifies the process of demonstrating compliance to auditors and stakeholders. Wiz helps organizations streamline AI compliance by mapping your AI security posture against major frameworks like NIST. With automated evidence gathering and out-of-the-box compliance checks, Wiz simplifies the process of proving your AI systems are built and operated securely, helping you prepare for audits and meet regulatory requirements.
Next steps for establishing robust AI security
The eight best practices presented in this article aim to empower teams to secure existing AI pipelines quickly—and swiftly adopt new AI solutions too. The focus on adaptability and agility is critical for organizations seeking to integrate AI successfully and securely in the evolving landscape of AI and the emerging field of AI security.
To establish this agile standardized security framework, explore solutions that prioritize process enhancement over infrastructure maintenance. As a cloud-native application protection platform with AI security posture management (AI-SPM) capabilities, Wiz is a cornerstone of reliable security across IT and AI applications. With extended visibility and streamlined governance, our AI-SPM tool offers built-in support for best-practice AI security management.
Considering an AI-SPM solution? Here are the four most important questions every security organization should be asking itself:
->Does my organization know what AI services and technologies are running in my environment?
->Do I know the AI risks in my environment?
->Can I prioritize the critical AI risks?
->Can I detect a misuse in my AI Pipelines?
Need automated detection of AI misconfigurations, management of your AI-BOM, and proactive discovery and removal of attack paths for AI applications in the cloud? Wiz has you covered.
Wiz is a founding member of the Coalition for Secure AI. As a founding member, Wiz joins other industry leaders in contributing to the development of standardized approaches to AI cybersecurity, sharing best practices, and collaborating on AI security research and product development.
You can learn more by visiting the Wiz for AI webpage. If you prefer a live demo, we would love to connect with you.
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