Discover the OWASP (Open Worldwide Application Security Project)Top 10 AI Security Risks, from data leakage and model manipulation to AI supply chain vulnerabilities. Learn what these mean for enterprises adopting AI and how to strengthen governance, compliance, and resilience with private AI strategies. 

Why AI Security Demands Enterprise Attention 

AI is no longer experimental, it’s embedded in workflows across finance, healthcare, government, and technology. Yet as adoption grows, so does risk. According to the OWASP Foundation’s Top 10 for Large Language Model Applications (2025), AI systems introduce new, unique vulnerabilities that traditional cybersecurity tools weren’t designed to handle. 

Enterprises that rely on AI for automation, decision-making, or communication must now ask: 

Are our models trustworthy, auditable, and compliant, or are they quietly exposing our data and reputation to risk? 

Understanding the OWASP Top 10 AI Security Risks 

The Open Worldwide Application Security Project (OWASP) is a respected global authority on application security. In 2024–2025, they released the Top 10 AI Security Risks, a framework designed to help organizations identify and manage the most pressing threats in large language model (LLM) systems. 

Here’s what enterprises need to know: 

1. Prompt Injection 

Malicious prompts manipulate AI models into revealing sensitive information or performing unintended actions. 
Example: A user embeds hidden instructions in text that cause the AI to output confidential data or bypass filters. 
Enterprise impact: Data leakage, brand damage, and compliance violations. 

2. Data Poisoning 

Attackers corrupt training data, influencing AI behavior or degrading accuracy. 
Enterprise impact: Skewed analytics, manipulated results, and compromised automation pipelines. 

3. Model Theft or Replication 

Unauthorized entities extract model weights or copy proprietary AI systems. 
Enterprise impact: Loss of intellectual property, competitive disadvantage, and regulatory exposure. 

4. Sensitive Information Disclosure 

AI models can unintentionally expose personal, financial, or corporate data during output generation. 
Enterprise impact: GDPR and HIPAA violations, customer trust erosion. 

5. Insecure Plugin or Integration Use 

Many AI systems rely on third-party APIs or plugins. Without governance, these integrations can become data exfiltration points. 

Enterprise impact: Shadow AI and API-level vulnerabilities leading to cross-system exposure. 

6. Model Denial of Service (DoS) 

Flooding AI systems with complex or malformed prompts can degrade performance or crash services. 

Enterprise impact: Business disruption and operational downtime. 

7. Supply Chain Vulnerabilities 

AI systems depend on multiple external sources, pre-trained models, datasets, open-source frameworks. Each represents a potential backdoor. 

Enterprise impact: Propagation of vulnerabilities across systems, non-compliance with data sovereignty laws. 

8. Inadequate Sandbox or Isolation 

Running AI models in unsegmented environments risks cross-contamination of sensitive data.  

Enterprise impact: Data mixing between departments or clients, a serious regulatory concern. 

9. Overreliance on Model Output 

Human operators trusting AI-generated results without validation can lead to flawed decisions. 
Enterprise impact: Financial, legal, or reputational harm due to inaccurate outputs. 

10. Insufficient Monitoring & Governance 

Without visibility, enterprises can’t detect misuse, anomalies, or emerging risks. 
Enterprise impact: AI drift, undetected insider threats, and failed audits. 

Why These Risks Matter for Enterprises 

AI risks are not theoretical, they are already shaping compliance requirements. 
Frameworks like GDPR, HIPAA, and the EU AI Act mandate that companies maintain full control over where and how AI systems process data. 

For enterprises, the OWASP Top 10 isn’t just a technical checklist. It’s a strategic roadmap for protecting AI infrastructure, maintaining customer trust, and ensuring business continuity. 

Building an AI Risk Management Strategy 

To address these risks, enterprises should focus on four pillars: 

1. Visibility: Know which AI systems are in use, officially and unofficially (Shadow AI). 

2. Data Control: Restrict what data models can access or generate. 

3. Access Governance: Apply least-privilege policies across teams and models. 

4. Continuous Monitoring: Detect abnormal prompts, data leaks, or non-compliant use in real time. 

AI Security and Cybersecurity: The New Convergence 

Traditional cybersecurity tools were built for networks, devices, and users, not for models that learn and adapt. As AI becomes a critical enterprise asset, AI security must evolve into a fusion of cybersecurity, data protection, and governance. 

This is where new AI security layers, like AI Firewalls and Private AI deployments, are becoming essential. 

Final Thoughts 

While Pragatix does not simply “patch” AI risks, it helps enterprises govern AI usage from within. Our platform embeds real-time governance across every model and interaction by combining: 

• AI Firewalls: Stop unapproved or risky prompts before data exposure occurs. 

• Private LLM Deployments: Deploy secure, compliant AI models on-premises or air-gapped. 

• AI Risk Monitoring: Track all AI activity for auditability and compliance alignment. 

• Data Security Posture Management (DSPM): Ensure sensitive data is accessed only by authorized users. 

Together, these solutions turn the OWASP AI Security framework into a living governance model that scales with enterprise AI adoption. 

Learn more: Explore Pragatix AI Security Solutions 

Frequently Asked Questions 

Q1: What is the OWASP Top 10 for AI Security? 
A: It’s a framework developed by OWASP to identify the most critical risks in large language model (LLM) applications, helping enterprises secure AI usage. 

Q2: Why should enterprises care about AI-specific risks? 
A: Because traditional security controls can’t detect AI misuse, data leakage, or model manipulation. AI risks require specialized governance and tools. 

Q3: How can AI Firewalls help prevent prompt injection or data exposure? 
A: AI Firewalls intercept and analyze every request to block sensitive, malicious, or non-compliant inputs and outputs in real time. 

Q4: What regulations apply to AI security? 
A: GDPR, HIPAA, and the EU AI Act all require transparency, accountability, and control in how AI systems handle personal or corporate data. 

Q5: How does Pragatix align with OWASP AI risk guidance? 
A: Pragatix solutions map directly to OWASP controls by enforcing data boundaries, monitoring model usage, and preventing unauthorized access. 

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