Fast and fast in the age of artificial intelligence (AI) and cloud technology, organizations are increasingly implementing security measures to protect sensitive data and ensure regulatory compliance. Among these measurements, AI-SPM (AI Security Astute Management) solutions have gained the traction to secure AI pipelines, sensitive data assets, and the entire AI ecosystem. These solutions help organizations identify risks, control security policies, and secure operational crucial data and algorithms.
However, not all AI-SPM tools are created equally. When evaluating potential solutions, organizations struggle to identify which questions to ask them to make an informed decision. To navigate this complex space, there are five important questions that every organization needs to ask when choosing an AI-SPM solution.
1: Does the solution provide comprehensive visibility and control over AI and related data risks?
As AI models across the enterprise surge, maintaining visibility and control over AI models, datasets, and infrastructure is essential. This gives you a clear understanding of what needs to be protected. Visibility or control gaps can expose an organization to a security or non-compliance violation.
AI-SPM solutions need to enable seamless AI models discovery, creating centralized inventory for full visibility into deployed models and associated resources. This helps organizations monitor model usage, ensure policy compliance, and actively address potential security vulnerabilities. By maintaining a detailed overview of the model across the environment, businesses can actively mitigate risk, protect sensitive data, and optimize AI operations.
2: Can a solution identify and fix AI-specific risks in the context of enterprise data?
Integrating AI into business processes introduces new and unique security challenges beyond traditional IT systems. for example:
Is your AI model vulnerable to hostile attacks and exposure? Are AI training datasets sufficiently anonymized to prevent leakage of personal or unique information? Are you monitoring predictive model bias and tampering?
Effective AI-SPM solutions must tackle the risks inherent in AI systems. For example, you should protect the training data used in machine learning workflows, ensure that the dataset remains compliant under privacy regulations, and identify abnormal or malicious activities that could undermine the integrity of your AI model. Ask if your solution includes built-in features to protect every stage of the AI lifecycle. From ingesting data to unfolding.
3: Does the solution match regulatory compliance requirements?
Regulatory compliance is the biggest concern for businesses around the world, given the growing complexity of data protection laws such as GDPR (General Data Protection Regulation), NIST AI, and HIPAA (Health Insurance Portability and Accountability Act). AI systems scale this challenge by rapidly processing sensitive data in ways that could increase the risk of accidental or non-compliance.
When evaluating your AI-SPM solution, automatically map your data and AI workflows to your governance and compliance requirements. To enable audit preparation, it should be able to detect non-compliant data and provide robust reporting capabilities. Additionally, features such as automated policy enforcement and real-time compliance monitoring are important to respond to regulatory changes and to prevent heavy fines and reputational damage.
4: How well do dynamic cloud-native and multi-cloud architecture solutions scale?
Modern cloud-native infrastructure is dynamic, with workloads scaled up and down as demand is needed. In a multi-cloud environment, this flexibility poses challenges. Maintain consistent security policies across a variety of providers (AWS, Azure, Google Cloud, etc.) and services. Adding AI and ML tools to your mix will increase variability.
AI-SPM solutions must be designed for scalability. Ask if the solution can handle dynamic environments, continually adapt to changes in the AI pipeline, and manage the security of distributed cloud infrastructure. The best tools provide centralized policy management while ensuring that each property adheres to the organization’s security requirements, regardless of location or state.
5: Will the solution be integrated with existing security tools and workflows?
A common mistake organisations make when adopting new technologies is failing to consider how well these technologies integrate with existing systems. AI-SPM is no exception. Without seamless integration, organizations could face operational disruptions, data silos, or gaps in security attitudes.
Before choosing an AI-SPM solution, check whether you want to integrate with existing data security tools such as DSPM, DLP, identity governance platforms, and DevOps toolchains. Equally important is the ability of solutions to integrate with AI/ML platforms such as Amazon Bedrock and Azure AI. Powerful integration ensures consistency and enables security, DevOps, and AI teams to collaborate effectively.
Key Takeout: Aggressive AI Security and Not Reactive
Remember that AI-SPM is not just about protecting your data, but about protecting the future of your business. As AI continues to rebuild its industry, installing the right tools and technology will help organizations innovate confidently ahead of new threats.
For more information, please visit zscaler.com/security
About the company
Zscaler (NASDAQ:ZS) accelerates digital conversions to make customers more agile, efficient, resilient and safe. The Zscaler Zero Trust Exchange™ platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices and applications everywhere. Distributed globally across more than 150 data centers, SSE-based Zero Trust Exchange™ is the world’s largest inline cloud security platform. Find out more at Zscaler.com.
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