AI Security Best Practices: Why a Data-Centric Approach Is the Foundation for Secure AI Innovation

Artificial intelligence has moved from experimentation to everyday use. Across industries, AI models, copilots, and generative tools are now part of how teams work and make decisions. Yet, according to Cyera’s 2025 State of AI Data Security Report, 83% of enterprises already use AI, while only 13% report strong visibility into how it touches their data.

Securing AI isn’t exactly as simple as just access controls for your staff because even your AI agents can access data. Seventy-six percent of organizations surveyed for our report said autonomous AI agents are the hardest to secure. These agents often make decisions and access information without direct human oversight, increasing the need for continuous monitoring and control.

This gap highlights the core challenge of AI adoption: the speed of innovation has outpaced the security controls that protect sensitive data. As a result, many organizations face new questions. Where does AI interact with regulated data? How do you enforce policies across models? What controls are needed to prevent overexposure or misuse?

Enterprises can close these gaps by grounding their AI strategy in data security. A data-centric approach establishes the visibility, control, and governance needed to enable AI safely and responsibly.

Key takeaways: 

• AI adoption has outpaced security controls. 83% of enterprises use AI, but only 12% have strong visibility into how it touches their data.
• Autonomous AI agents access data and make decisions without direct human oversight, making them the hardest AI systems to secure.
• Securing the data that powers AI, rather than each model independently, is the most effective foundation for AI security.
• AI-SPM is becoming a core enterprise capability, continuously monitoring AI behavior and policy compliance in real time, where periodic audits fall short.

Why AI Security Best Practices Matter More Than Ever

AI has fundamentally changed the risk calculus for enterprise security. As adoption accelerates, so do threats and compliance demands. This makes a deliberate, structured approach to AI security mission-critical. Let’s look at the most common threats and compliance requirements that drive your need for AI security.

Evolving Attack Surface

Your company’s attack surface expands each time you add a new AI tool or model to your tech stack. Unlike traditional software, AI systems interact with vast amounts of data, often across multiple environments (SaaS, cloud, and on-premise) simultaneously.

Each of these touchpoints is a potential entry point for bad actors. As AI becomes more deeply embedded in business workflows, you must stay prepared to tackle a threat landscape that’s broader and more dynamic.

AI-Powered Threats

Remember that attackers can use AI too. From highly personalized phishing campaigns to automated vulnerability scans, adversaries now have tools that are faster and harder to detect. In fact, 82.6% of phishing emails are now AI-crafted.

Clearly, this isn’t a concern for the future. It’s happening now. You need defenses that can match the speed and sophistication of AI-powered threats, not just respond to them after an attack.

Data Integrity and Bias Risks

AI systems are only as reliable as the data behind them. When training data is incomplete or poorly governed, models can produce biased or potentially harmful outputs. These aren’t just technical issues but a real source of business risk.

Consider a fraud detection model trained on historical transaction data that over-represents certain customer segments. Over time, it may flag legitimate transactions from underrepresented groups at a higher rate and create a compliance liability. The model isn’t broken in an obvious way, but the damage is real.

Risk doesn’t just come from loss of trust. There’s also a penalty that will materially impact your financial position. Under the EU AI Act, companies deploying biased high-risk AI systems face penalties of up to €35 million or 7% of global turnover. That’s a significant financial exposure for something that often starts as a data governance oversight.

Regulatory and Legal Compliance

Regulators are catching up to AI faster than most businesses expected. The EU AI Act is already introducing tiered obligations based on risk level. GDPR continues to apply strict rules around how personal data is processed, including by AI systems. And in the U.S., state-level privacy laws are adding new layers of complexity for companies operating across jurisdictions.

Your goal, then, should be to know more than just where data is stored. You must know how it flows through your AI systems, what it’s used for, and whether that usage is defensible under frameworks applicable to your business.

Increased Risk During AI Scaling

Here’s a pattern worth watching for. A company starts small—one or two AI tools, limited access, straightforward use cases. Then adoption picks up. More teams want access. More models get deployed. More data gets pulled into the mix. At some point, governance can’t keep up with the pace of development, and the gaps start to accumulate.

This is when risks start to play out. According to IBM’s Cost of a Data Breach Report 2025, organizations with high levels of shadow AI faced breach costs averaging $670,000 higher than those with low or no shadow AI. Security incidents involving shadow AI also resulted in more PII (65%) and intellectual property (40%) being compromised.

Overprovisioned AI agents are a good example of how this risk unfolds. An agent that was initially scoped to access one dataset gradually gets broader permission as its role expands. Nobody revokes the original access. Now it has more reach than it needs and more potential for damage if something goes wrong.

Reputation and Trust

The reputational stakes around AI incidents are high and rising. When Samsung employees accidentally leaked confidential source code through a generative AI tool in 2023, it became a widely cited example of what ungoverned AI adoption can lead to.

Customers and partners are now paying attention to how organizations handle AI. Demonstrating that you have clear, enforceable controls in place is increasingly a factor in buying decisions and partnership evaluations.

Core AI Security Best Practices for Stronger AI Data Security

Establishing strong AI security practices requires moving from reactive control to proactive governance. Here are some essential best practices organizations can follow to build a secure and scalable AI program.

1. Discover and Classify Sensitive Data

You can’t protect what you can’t see. The first step in any AI security strategy is identifying the sensitive data that fuels your AI models and tools. Data used for training, inference, and augmentation often includes regulated information such as customer records or intellectual property.

Comprehensive data discovery and classification allow security teams to understand where this information resides, how it is used, and who can access it. Given the volume and velocity of data moving through modern AI environments, doing this manually isn’t realistic. Purpose-built tools are essential for maintaining an accurate and up-to-date picture of your data landscape.

For example, Cyera’s data-centric platform automatically maps sensitive data across cloud, SaaS, and AI environments. By knowing what data exists and where it flows, enterprises can apply consistent controls and prevent unintentional exposure.

‍

2. Implement Continuous AI Security Posture Management (AI-SPM)

AI systems evolve quickly, so their security must evolve continuously as well. AI Security Posture Management (AI-SPM) provides a framework for maintaining ongoing visibility and risk assessment across AI environments.

AI-SPM extends the principles of Data Security Posture Management (DSPM) to AI. It helps teams identify which AI tools are in use, assess where they interact with sensitive data, and evaluate whether appropriate policies are applied.

Despite the clear need for oversight, only 9% of organizations currently monitor AI activity in real time. AI-SPM closes that gap by continuously evaluating configurations, access, and data movement, ensuring that new risks are identified before they become incidents.

3. Govern Access and Identity for AI

AI systems often act like users, yet many organizations do not manage them that way. Treating AI as a distinct identity class is essential for maintaining control and reducing risk. Without clear identity policies, AI models can easily access more data than they need to perform their function.

To address this, organizations should create AI-specific identity and access management policies. Each AI system should have a defined scope of access tied to data classification and business context. Permissions should be reviewed regularly and revoked automatically when no longer needed. This approach enforces least-privilege access and helps maintain compliance across dynamic AI environments.

4. Secure the Interface: Prompts and Outputs

The interface between humans and AI, prompts and outputs, is one of the most overlooked areas of security. It is also one of the most vulnerable. Sensitive data often flows through these interactions without clear oversight.

While most enterprises are still developing technical controls for this layer, the priority today is visibility. The risks are real and already playing out. In early 2025, a London-based pharmaceutical company suffered an IP breach when researchers used a publicly available generative AI tool to analyze proprietary research data. Similar molecular structures and insights later appeared in a competitor’s patent filing.

This is why security teams need to understand which tools handle sensitive data and how that information is used. Cyera helps provide this foundation by mapping data exposure across environments so organizations can define and enforce policies that limit unnecessary data sharing.

This visibility ensures that when AI models interact with sensitive content, security teams know where and how it happens, reducing risk and improving governance.

5. Build AI Governance That Maps to Evidence

Governance is more than policy; it is proof. Security leaders must demonstrate not only that controls exist, but that they operate effectively.

Strong AI governance connects policies to measurable outcomes. Teams should monitor coverage, assess how consistently AI activity is tracked, and measure time to detect and remediate risky behavior.

Ownership also matters. Establishing a dedicated governance function or cross-functional committee ensures accountability and oversight as AI adoption scales. Governance that is grounded in evidence and visibility is far more resilient than static documentation or one-time audits.

6. Understand the Threat Landscape for AI

Securing AI effectively means knowing what you’re actually defending against. The threat landscape for AI is distinct from traditional cybersecurity because it includes risks like:

• Prompt injection attacks, where malicious inputs manipulate model behavior
• Model inversion attacks, where adversaries attempt to reverse-engineer sensitive training data from model outputs
• Supply chain vulnerabilities introduced through third-party models or datasets

That’s why your team must track emerging attack techniques and map those findings back to your company’s own environments. If your organization is running a large language model that was fine-tuned on customer data, for example, understanding the risk of model inversion helps prioritize what controls belong around that system specifically.

7. Embrace an Agile, Cross-Functional Mindset

AI security requires cross-functional effort. Your models live in engineering, the data flowing through them is owned by business units, and compliance requirements sit with your legal team.

Building a cross-functional approach means bringing together security, data, engineering, legal, and business stakeholders around a shared understanding of AI risk. In practice, this might look like a standing AI governance committee that reviews new deployments before they go live or a shared framework that gives each team clarity on their role in the process.

The goal is to make security a built-in part of how AI gets deployed instead of a checkpoint that slows it down after the attack. Companies that get this right move faster because they’re not untangling access and compliance issues after deployment.

8. Continuous Monitoring Beyond Visibility

AI-SPM gives you the framework. Putting it into practice means actively tracking how your AI systems behave over time and whether activity patterns suggest something has gone wrong. 

This goes beyond dashboards and periodic audits. Consider an AI agent that operates normally for months, then begins accessing datasets outside its defined scope after a permission change that nobody flagged. Without continuous monitoring, your team wouldn’t notice this issue until it turns into a problem.

Continuous monitoring helps set baselines for normal AI behavior, alerting on deviations, and closing the loop with automated responses where possible. This is exactly why you’ll notice that companies that catch threats early are the ones treating monitoring as an active, ongoing process instead of a quarterly review.

If continuous monitoring is your goal, Cyera can help. It supports continuous data monitoring and movement across your AI environments, so deviations from normal behavior are flagged in real time.

9. Third-Party AI & Open-Source Model Vetting

Most companies aren’t building their AI models from scratch. They’re using third-party tools and open-source models. This means they’re also inheriting whatever risks come with them.

An open-source model trained on scraped internet data, for example, may carry embedded biases, licensing complications, or even deliberately poisoned weights if it came from an unverified source.

If you fail to properly vet third-party components, you could find yourself dealing with vulnerabilities like CVE-2026-21858 in n8n, an open-source workflow automation tool widely used in AI pipelines. So, always make third-party or open-source AI components go through a structured vetting process before deployment. Review the provenance of training data, assess the security posture of the vendor or maintainer, understand what data the tool will access in your environment, and confirm if it meets your compliance requirements.

Why a Data-Centric AI Security Platform Is the Best Foundation for Governance

Most AI security strategies are overly focused on the models themselves. But models don’t operate in isolation. They’re powered by data, and that’s where real governance work happens.

Autonomous AI agents sit at the intersection of three compounding risks:

• Broad data access
• Independent decision-making
• Limited human oversight

That combination makes AI agents difficult to secure and can lead to significant damage when something goes wrong. 

AI agents are different from traditional software. Instead of waiting for your instructions, they act. They retrieve data and trigger downstream processes on their own. When an agent is overprivileged or compromised, the blast radius can be significant before anyone notices.

Securing AI agents means treating them like any other high-risk identity in your environment. It requires scoped access, continuous monitoring, and clear escalation paths when activity falls outside expected parameters.

If you want to position yourself for what’s coming next, you need a data-centric AI security platform that helps you build your AI security foundation. Let’s look more closely at a few reasons why an AI security platform is mandatory for any AI-powered business. 

Protects the Data That Powers AI

Every AI security challenge begins and ends with data. Whether the issue is model bias, data leakage, or compliance failure, the underlying factor is often uncontrolled access to sensitive information.

A data-centric approach shifts focus from perimeter defenses to the information itself. Instead of trying to secure every AI model independently, organizations secure the data that those models use. The distinction matters more than it might seem. AI systems, including training pipelines and inference engines, regularly interact with data that contains personally identifiable information (PII) and protected health information (PHI). Without proper controls, that information can be inadvertently included in training datasets or exposed through integrations with third-party tools.

By classifying, tagging, and monitoring sensitive data, teams can apply consistent rules around using and accessing PII and PHI, regardless of where or how AI operates. This reduces the risk of data leakage at its source, rather than trying to catch it downstream after exposure has already occurred.

The compliance benefit follows naturally. When privacy and data protection requirements are enforced at the data level, they apply consistently across every AI system that touches that data. This simplifies audit readiness and reduces the manual overhead of managing compliance model by model.

Enables Continuous, Real-Time Compliance

Compliance can’t be a one-time checkbox in AI environments where data moves fast and regulations keep evolving. A data-centric platform doesn’t just help you meet today’s requirements; it keeps you aligned as those requirements shift.

Rather than relying on periodic audits or manual reviews, your controls are always running. If a new AI tool starts interacting with PHI in a way that conflicts with your data policies, you know about it in real time and not six months later during an audit. That kind of responsiveness is increasingly what regulators expect, and it’s what separates companies that are genuinely compliant from those that only appear to be.

Provides Evidence-Based Governance

A data-centric platform helps you build governance and generate evidence that makes governance credible. Logs, access records, policy enforcement history, and documented controls are produced automatically, creating a paper trail without any manual documentation whatsoever. 

Data matters when:

• Regulators come asking questions
• When customers want assurance that their data is not being misused in your AI systems
• When your leaders need confidence that the AI program is operating responsibly

A data-centric platform creates the paper trail that makes governance credible by tying every control back to observable, measurable activity. This is exactly what you’ll need when dealing with regulators or concerned customers.

Improves AI Performance and Reliability

Security and performance are often considered competing priorities. In practice, a data-centric approach improves both.

When AI models are trained and operated on well-governed, accurately classified data, they tend to produce better outputs. Removing duplicate, outdated, or improperly classified data from AI pipelines reduces noise and improves model reliability. It also reduces the risk of models learning from data they shouldn’t have had access to in the first place.

Supports Scalability in Complex Environments

As AI adoption grows, so does the complexity of managing it. More models, more data sources, more teams, more tools—and all of it needs consistent governance regardless of where it lives.

A data-centric platform is built for that complexity. It allows you to enforce policies at the data layer instead of applying security controls model by model or environment by environment. This means those policies follow the data wherever it goes.

Whether you’re running AI across three cloud providers and a dozen SaaS tools or expanding into new use cases and geographies, the governance foundation stays intact. That consistency is what makes it possible to scale AI confidently, without having to rebuild your security every time something new gets added.

The Cyera Perspective on AI Security

Cyera’s data-centric approach enables organizations to securely enable AI while maintaining speed and innovation. The Cyera AI Security Platform combines data discovery, classification, and policy enforcement into one continuous workflow.

When enterprises use Cyera, they gain visibility into where sensitive data may intersect with AI systems and tools. This insight helps teams understand which information could be exposed through AI adoption and where governance controls are most needed. With this data intelligence, organizations can set policies and collaborate with IT and security teams to manage access, strengthen oversight, and ensure AI initiatives remain compliant and secure.

With Cyera’s AI Data Security Assessment, security teams can evaluate their readiness in just three minutes. By answering a few targeted questions, they uncover gaps and strengths in their AI and data security posture and receive a personalized PDF report with a score and actionable insights.

This level of precision allows organizations to innovate confidently with AI while knowing their most valuable data remains protected.

The Future of AI Security Best Practices and AI-SPM

The trajectory is clear: AI will become more autonomous and more regulated. The organizations best positioned for what’s coming are the ones building governance infrastructure now, not after an attack occurs. Here’s an overview of where we’re headed:

• Shadow AI will become a primary discovery challenge: As AI tools proliferate, employees will continue to adopt unauthorized or unmanaged tools outside IT’s purview. AI-SPM will increasingly need to actively surface these blind spots, not just govern the AI that’s already known.
• Security will shift left into the AI development lifecycle: Embedding security earlier at the model design and training stage, rather than post-deployment, will become standard practice. Their DevSecOps-for-AI approach catches vulnerabilities before they reach production, where they’re far more costly to fix.
• Proactive risk mapping against established frameworks will become the norm: Forward-thinking security teams are already mapping their AI environments against frameworks like OWASP Top 10 for LLMs and MITRE ATLAS, which cover threats like data poisoning, prompt injection, and model theft. Expect this kind of structured threat modeling to become a baseline expectation.
• Automated remediation will close the gap between detection and response: Identifying risk is only half the equation. Next-generation AI-SPM platforms will increasingly prioritize risks by impact and trigger automated fixes, reducing the window between when a vulnerability is spotted and when it’s resolved.
• Regulatory scrutiny will deepen, and audit trails will matter more: Emerging frameworks will demand not just that controls exist, but that organizations can demonstrate how AI decisions are made and how data is protected throughout. The evidentiary bar is rising, and governance programs that aren’t built on real-time data will struggle to meet it. In fact, Gartner predicts that by 2027, AI governance will become a requirement of all sovereign AI laws and regulations worldwide, making the governance infrastructure you build today a direct competitive advantage tomorrow.

Cyera Research Labs will continue to study how AI and data security intersect, providing evidence-based guidance that helps organizations measure readiness and strengthen controls.

If you’re ready to see where your organization stands, download Cyera’s 2025 State of AI Data Security Report for the latest data on how enterprises are managing AI risk and what separates organizations that are ahead from those that are still catching up. 

Achieve Safe AI Adoption Through AI Security Best Practices

AI adoption is accelerating, but visibility into how it touches sensitive data hasn’t exactly kept pace. And that gap is a source of risk. To become an AI-powered business, you need to proactively manage this risk.

Companies that get AI right are the ones that built the right foundation early with clear data visibility, continuous governance, and controls that scale with their AI ambitions. The best practices covered in this guide aren’t about slowing you down. They’re making sure the speed of innovation doesn’t come at the cost of security or compliance.

Our goal here isn’t to answer whether your organization will face AI security challenges. It will at some point. Our focus is to help you gain visibility to catch these challenges before they turn into incidents.

See exactly where AI touches your sensitive data. Request a demo to learn how Cyera helps you ensure AI security.

‍

AI Security Best Practices FAQs

What are AI Security Best Practices?

AI security best practices are the policies and controls that protect data, systems, and users across the AI lifecycle. They include data discovery, access management, continuous monitoring, and incident response.

What is AI-SPM?

AI Security Posture Management, or AI-SPM, is a continuous process that identifies and mitigates risks in AI environments. It provides visibility into how AI interacts with sensitive data and enforces security policies automatically.

Why is a data-centric approach essential for AI security?

Data is at the heart of every AI process. Securing the data ensures that any model or system using it operates safely and compliantly.

How does Cyera support AI security?

Cyera’s AI Security Platform discovers, classifies, and protects sensitive data across cloud, SaaS, and AI tools. It gives security teams visibility and control to enable AI adoption without increasing risk.

What are the best government-compliant tools for secure AI development?

The right tools depend on your regulatory environment. However, a few categories consistently matter for secure and compliant AI development.

AI-SPM and DSPM platforms provide visibility and policy enforcement across sensitive data. Tools such as Cyera help organizations discover and classify regulated data, like PII, PHI, and financial records, before it is used in AI systems.

Cloud infrastructure with built-in compliance controls is also essential. Platforms like Amazon Web Services, Microsoft Azure, and Google Cloud offer environments designed to support frameworks such as FedRAMP, HIPAA, and SOC 2.

Organizations often rely on data lineage and audit tools that track how data moves through AI pipelines. These systems create the audit trails required for regulatory frameworks like GDPR or the EU AI Act.

How can you avoid the security threats posed by AI?

Start with visibility. Organizations need to know what AI tools are being used and what data those systems can access. Data security platforms like Cyera can help identify sensitive datasets and flag risks before that information is exposed to AI models.

Then, apply least-privilege access so AI systems only interact with what they genuinely need. Monitor continuously for anomalous behavior and policy violations. 

Vet third-party models before deployment. Evaluate how they handle training data, prompts, and outputs, and ensure they meet your organization’s security and compliance requirements.

And treat governance as a cross-functional responsibility, not just a security team problem. Even with all of these, you can’t remove AI risk entirely. But you can manage it systematically.

‍

‍

‍