As organizations rapidly adopt AI copilots, large language models, and autonomous AI agents, are their security programs evolving just as quickly?
Artificial intelligence is transforming how organizations operate. From customer service and software development to cybersecurity operations and business analytics, generative AI is becoming deeply embedded into everyday workflows. Enterprises are deploying LLMs, AI copilots, and intelligent automation platforms to improve productivity, accelerate decision-making, and unlock new business opportunities.
However, every new AI capability also introduces new security challenges. Unlike traditional applications, large language models process natural language, interact with external data sources, generate dynamic outputs, and increasingly perform autonomous actions through AI agents. These capabilities significantly expand the organizational attack surface, creating risks that many existing cybersecurity programs were never designed to address.
This is why AI security has become one of the fastest-growing priorities for CISOs, CIOs, AI governance leaders, and security architects. Organizations are realizing that deploying AI without structured security controls can expose sensitive information, increase regulatory risk, and create new opportunities for attackers. Among the most valuable resources helping organizations address these challenges is OWASP's growing body of AI security guidance.
The Rise of AI Security Risks
The rapid adoption of LLMs has fundamentally changed enterprise security.
Traditional cybersecurity focused primarily on protecting infrastructure, endpoints, networks, and applications. AI introduces an entirely new category of risks because models interact directly with users, enterprise data, external knowledge sources, and automated business processes.
Modern AI deployments often include:
- Enterprise copilots
- Customer-facing chatbots
- Internal knowledge assistants
- AI-powered search
- Autonomous AI agents
- Decision support systems
Each of these technologies introduces unique security considerations.
Unlike conventional software, LLMs continuously interpret user instructions, retrieve information, generate responses, and sometimes trigger automated actions.
As organizations deploy more AI-powered services, they also inherit new categories of risk, including:
- Prompt injection
- Model theft
- Sensitive information disclosure
- System prompt leakage
- Data and model poisoning
- Training data poisoning
- Insecure output handling
- Excessive agency
- Retrieval vulnerabilities
Traditional application security frameworks provide only partial guidance for these scenarios.
This gap is precisely why OWASP expanded its work into AI security.
What Is OWASP AI?
OWASP, the Open Worldwide Application Security Project, is one of the world's most respected organizations dedicated to improving software security.
For more than two decades, OWASP has published practical guidance, security standards, testing methodologies, and awareness materials that help organizations build more secure applications.
As artificial intelligence began transforming software development, OWASP extended its mission to include AI security.
Today, OWASP develops specialized guidance addressing risks associated with:
- Large language models
- AI assistants
- Autonomous AI agents
- Machine learning systems
- Enterprise AI applications
Its goal is simple:
Help organizations understand how AI systems can fail—and how to secure them before attackers exploit those weaknesses.
The OWASP GenAI Security Project
One of the organization's most important initiatives is the OWASP GenAI Security Project.
The project provides practical resources focused specifically on securing modern AI systems.
Rather than treating AI as simply another application, the project recognizes that AI introduces entirely new attack techniques requiring specialized defensive strategies.
The OWASP GenAI Security Project covers topics including:
- Secure AI architecture
- Threat modeling
- Secure development practices
- AI governance
- AI risk management
- Security testing
- Operational controls
It has become an essential reference for organizations deploying enterprise AI.
OWASP Top 10 for LLM Applications
Perhaps the best-known OWASP initiative for AI is the OWASP Top 10 for LLM Applications.
Similar to the original OWASP Top 10 for web applications, this project identifies the most critical risks affecting applications built on LLMs.
Examples include:
- Prompt injection
- Insecure output handling
- Sensitive information disclosure
- Supply chain vulnerabilities
- Model theft
- Excessive agency
- System prompt leakage
- Overreliance
- Unbounded consumption
These risks differ significantly from traditional software vulnerabilities.
For example, prompt injection allows attackers to manipulate model behavior through carefully crafted instructions rather than exploiting software bugs.
Likewise, system prompt leakage can expose confidential instructions that define how an AI assistant behaves.
Understanding these risks is becoming increasingly important as organizations integrate AI into business-critical processes.
Beyond LLMs: OWASP Top 10 for Agentic Applications
AI technology continues evolving rapidly.
Organizations are increasingly deploying autonomous AI agents capable of performing multi-step business operations.
These agents can:
- Access enterprise systems
- Execute workflows
- Retrieve documents
- Call APIs
- Make decisions
- Interact with multiple applications
Because agentic systems introduce additional complexity, OWASP has also developed the OWASP Top 10 for Agentic Applications.
This guidance focuses on risks unique to autonomous AI behavior, including:
- Excessive permissions
- Autonomous decision-making
- Unsafe action execution
- Poor authorization controls
- Inadequate behavioral monitoring
As AI agents become more capable, organizations must strengthen governance and AI security accordingly.
Why OWASP AI Matters for Modern Organizations
Many executives initially assume AI security is simply another extension of traditional cybersecurity.
In reality, AI changes the threat landscape considerably.
One important difference is that LLMs frequently interact with sensitive enterprise information.
This creates risks involving:
- Intellectual property
- Customer information
- Financial records
- Legal documents
- Internal communications
- Business strategies
Without appropriate controls, AI systems may inadvertently expose confidential information through generated responses.
This is why AI security has become a critical business issue rather than simply a technical concern.
Data Exposure Risks
One of the most significant AI-related threats involves sensitive information disclosure.
Enterprise AI assistants often retrieve information from:
- SharePoint
- Microsoft Teams
- OneDrive
- Internal databases
- Knowledge repositories
If permissions are poorly managed, AI systems may surface information users were never intended to access.
Similarly, poor input validation practices can increase the likelihood of prompt injection attacks that manipulate model responses.
Organizations should implement strong identity controls, data classification, and access governance before deploying AI broadly.
Compliance Challenges
AI adoption is also reshaping regulatory expectations.
Frameworks such as the EU AI Act place increasing emphasis on:
- Transparency
- Human oversight
- Risk management
- Data governance
- Security controls
Organizations deploying enterprise AI must demonstrate that security has been incorporated throughout the AI lifecycle.
OWASP guidance complements governance frameworks by providing practical recommendations for protecting AI systems from emerging threats.
Business Continuity and Operational Resilience
Security incidents involving AI can have consequences beyond data exposure.
Compromised AI systems may:
- Produce misinformation
- Generate inaccurate recommendations
- Disrupt automated workflows
- Create operational delays
- Reduce customer trust
As organizations increasingly rely on AI agents, maintaining operational resilience becomes essential.
Business continuity planning should therefore include AI-specific security scenarios.
Protecting Intellectual Property
Many organizations are training proprietary AI models using internal knowledge.
This information often includes valuable intellectual property.
Attackers may attempt model theft, unauthorized extraction of model behavior, or training data poisoning designed to compromise model integrity.
These attacks highlight why AI security must extend beyond infrastructure protection.
Organizations need governance over models themselves.
Emerging Attack Techniques
The AI threat landscape continues evolving rapidly.
Security teams should monitor risks including:
- Adversarial attacks
- Data and model poisoning
- Training data poisoning
- Vector and embedding weaknesses
- Retrieval-Augmented Generation (RAG) manipulation
- Remote code execution scenarios involving AI plugins
- Supply chain vulnerabilities affecting AI components
Many of these attack techniques are unfamiliar to traditional application security teams, making continuous education and threat modeling increasingly important.
