AI Security 101: Best Practices & Pitfalls
AI is transforming the way businesses operate, from AI-powered tools to embedded AI in business apps, and now AI Agents that can perform tasks autonomously. But as AI adoption grows, so do concerns around security.
In this lesson, we’re going to break down the major security considerations for each type of AI deployment and discuss best practices for ensuring AI is used safely in your organization.
We’ll also cover how bad actors can use AI for phishing attacks and cyber threats, and we’ll separate fact from fiction when it comes to AI-related security fears.
By the end of this session, you’ll understand how to secure AI applications, AI Agents, and embedded AI inside business software, while also protecting your company’s data, workflows, and systems.
Understanding AI Security Across Different Deployments
AI is being deployed in three major ways:
AI Apps – Standalone AI-powered tools like ChatGPT, Jasper AI, and Midjourney
Embedded AI – AI features inside existing software, like Microsoft Copilot or AI-powered CRM assistants
AI Agents – Fully autonomous AI systems that can interact with applications and perform business tasks
Each of these deployments has its own security risks, and it’s important to understand the differences.
Security Risks in AI Apps
AI applications—like ChatGPT, Jasper AI, and image generation tools—are great for productivity, but they also come with data security risks if not used properly.
One of the biggest concerns is data leakage. Many consumer-grade AI models, including the free version of ChatGPT, train on the data that users input. This means that if an employee uploads confidential documents, proprietary code, or sensitive customer information, that data could be stored and used to improve future AI responses—which is a massive security risk.
The good news is that Enterprise AI solutions, like OpenAI’s business offering, do not train on company data. If your organization is using AI at scale, always ensure that your AI tools are being used under an enterprise license to protect sensitive information.
Bad actors can also use AI to improve phishing attacks, generating more convincing scam emails with perfect grammar and context-aware messaging. This means organizations must train employees to recognize AI-generated phishing attempts and strengthen their cybersecurity protocols.
Security Risks in Embedded AI
Embedded AI—AI built into existing software like CRMs, spreadsheets, and productivity apps—has a lower risk of data leakage because it operates within the security framework of the host application.
However, there are still concerns. Since AI is often used to summarize documents, draft emails, or provide insights, employees may inadvertently expose sensitive data by requesting AI to process information that wasn’t meant to be shared or analyzed in certain ways.
For example, if an AI-powered CRM generates customer insights, what controls are in place to prevent unauthorized employees from accessing private customer data?
Organizations must set up role-based access controls (RBAC) to ensure AI-powered insights are only available to the right users.
Another risk is misuse of AI-generated content. Since AI is designed to predict responses, it can sometimes make up information or misinterpret context. Employees need to verify AI-generated reports, summaries, or financial projections before taking action.
Security Risks in AI Agents
AI Agents are the most powerful form of AI deployment because they don’t just assist users—they take action inside business systems.
Because of this, they also carry the highest level of security risk if not deployed correctly.
One of the biggest mistakes businesses make is giving AI Agents too much access to applications, databases, or communication channels.
For example, an AI Agent designed to schedule sales meetings should only have:
Access to the sales team’s calendar
The ability to send emails on behalf of the sales team
Access to a CRM system to update lead statuses
But it should not have access to finance, HR records, or system-wide administration controls.
The best practice here is to treat AI Agents like employees—only giving them the permissions they need to perform their assigned tasks.
This is where platforms like Raia come in. Raia allows businesses to train, test, and control AI Agents, ensuring they operate within defined guardrails. Organizations can set up access controls, monitor AI activity, and prevent unauthorized actions.
AI Agents also need secure authentication methods. Instead of giving them full system access, companies should use:
API authentication to limit the AI’s capabilities
OAuth permissions to restrict data access
Audit logs to track every action the AI takes
By treating AI Agents like external software applications instead of open-ended assistants, businesses can ensure they operate safely.
The Biggest AI Security Myths & Misconceptions
There’s a lot of fear around AI security, but many concerns are overblown or misunderstood.
One of the biggest misconceptions is that AI models store and execute commands like a human brain. In reality, language models are read-only databases—they do not have independent memory unless they are built into an application with persistent storage.
This means AI cannot execute tasks on its own unless it is connected to a system via software. AI isn’t making unauthorized decisions—it’s simply following instructions from the applications it’s embedded in.
So, while AI security is important, it’s not fundamentally different from securing any other SaaS product. The real security risks come from how AI is integrated, how much access it’s given, and how it’s monitored.
Best Practices for AI Security
To help businesses deploy AI safely, here are some best practices to follow:
Use an Enterprise AI License Always ensure employees use enterprise-grade AI models like OpenAI’s business plan, which does not train on company data.
Limit AI Access Using API Authentication Treat AI like any other third-party software and only grant access to specific functions the AI needs to perform its tasks.
Train & Test AI Agents Before Deployment Use a platform like Raia to properly train, test, and set guardrails before allowing AI Agents to operate autonomously.
Manage AI Access Control at the Agent Level If your company is using multiple AI Agents, use Raia or a similar management platform to ensure only authorized users can train, modify, or update agents.
Encrypt Proprietary Data for AI Training If you are using AI to process sensitive documents, ensure data is encrypted in the vector store before being referenced by AI.
Monitor & Log AI Activity Set up audit trails to track what AI Agents are doing and require human oversight for critical actions.
Educate Employees on AI Security Risks Train employees on AI phishing risks, responsible AI usage, and how to verify AI-generated content.
Limit AI Memory & Persistent Storage Ensure that AI-powered applications do not store sensitive data beyond what is necessary for a given session.
Final Thoughts
AI security isn’t about fearing AI—it’s about deploying it responsibly.
The good news is that AI security is not all that different from securing traditional software applications. The key is to understand where the risks are, set up the right controls and guardrails, and use enterprise-grade AI solutions that offer better security protections.
By following best practices, businesses can safely integrate AI into their workflows without exposing sensitive data or creating unnecessary risks.
In the next lesson, we’ll explore how to properly train and monitor AI Agents to ensure they stay aligned with business goals.
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