Agentic AI vs Generative AI: The Critical Security Guide

Key Takeaways

• Agentic AI is designed to act on its own based on predefined rules. For example, an AI agent may be programmed to manage patient scheduling or plan a work trip. 
• Generative AI can answer questions, write code, or create images after being prompted. 
• If unsecured, agentic AI poses several security risks to enterprises, including memory poisoning or task drift.  
• Anjuna’s confidential computing provides hardware attestation to secure AI agents and data within Trusted Execution Environments (TEEs).

Both generative AI (also known as gen AI) and autonomous AI agents are changing the way business is done, but they also introduce new security risks. The type of risk depends on whether you’re using agentic AI vs gen AI. 

Gen AI is the technology behind tools like ChatGPT, Claude, and Midjourney. It reacts to human prompts, responding based on the vast data it has been trained on. Gen AI can generate pictures, write blog posts, and produce code. Agentic AI, on the other hand, can act autonomously, using tools and making decisions without a human prompting it step by step. 

In this guide, we discuss the differences between gen AI and agentic AI, use cases for each, and the unique risks that autonomous AI agents bring to enterprises. 

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Agentic AI vs Gen AI

Here’s a quick look at agentic AI vs gen AI:

Agentic AI: 

• Works autonomously to achieve its goal
• Can reason, handle multi-step problems, and make decisions without human input. 
• Without guardrails, agentic AI can be corrupted or go rogue, taking actions beyond what it should. 

Gen AI: 

• Generates new text, images, code, music, video, etc. after being prompted. 
• Cannot act on its own. 
• Memory is usually limited to one conversational turn.
• Generative AI can “hallucinate” or provide biased responses, but doesn’t typically pose a security threat to enterprises. 

What Is Agentic AI?

Agentic AI refers to AI systems that can pursue goals autonomously. They plan, use tools, make decisions, and take actions over multiple steps without needing a human to prompt them at every turn. 

Core characteristics include:

• Goal-oriented: You give it an objective, not just a single instruction. 
• Autonomous: It figures out the steps and executes them.
• Tool-using: It can search the web, call APIs, run code, real files, send emails, and more. 
• Stateful: It remembers what it has done and adjusts based on results.
• Self-corrective: If something fails, it tries a different approach. 

What Is Gen AI?

Gen AI refers to AI models that create new content, including text, images, code, audio, and video, based on patterns learned from training data. It’s the technology behind ChatGPT, Claude, Gemini, DALL-E, Midjourney, and similar tools. The biggest difference between agentic AI vs gen AI is that gen AI doesn’t act on its own. It receives a single prompt and responds, but doesn’t continue to act. 

Core characteristics include:

• Pattern-based: It generates outputs by predicting what comes next based on training data.
• Prompt-driven: You give it a prompt, and it produces a response. 
• Creative: It can write, draw, compose, and brainstorm. 
• Stateless by default: Each prompt is handled independently unless you provide conversation history.

Gen AI doesn’t do anything on its own. It waits for a prompt, responds, and waits again. 

Agentic AI Use Cases

Agentic AI is the latest in Artificial Intelligence, but it’s less mature than gen AI, so use cases are still being developed. Here are some of the best use cases of agentic AI: 

1. Personal research assistant: Use agentic AI to answer complex questions. For example, you may ask it to research fleet vehicles for your organization. The AI agent can search, read reviews, compare specs, and deliver a ranked summary, all without you clicking a single link. 
2. Customer support: Agentic AI can be used to handle customer support tasks and offer personalized customer service. Chatbots powered by agentic AI can have a natural-sounding conversation with customers and respond to requests. Most importantly, they can escalate more complex tasks to human agents. 
3. Competitive monitoring: AI agents can be programmed to watch competitor websites and social channels daily to detect price changes or new product launches, and draft a brief with recommendations delivered to your inbox. 
4. Travel planning: Use agentic AI to research flights, compare prices, check your calendar, and book the best option. 
5. Self-driving cars: Agentic AI powers self-driving cars, making driving decisions based on real-time traffic conditions.  

Gen AI Use Cases

Gen AI has been adopted by a number of organizations to save time and increase productivity. Here are some of the top use cases of gen AI:

1. Content creation: Gen AI is commonly used to write blog posts, social media captions, email newsletters, ad copy, and video scripts. It can be prompted to optimize content so it will rank higher on search engine pages and drive organic traffic to the site. 
2. Image generation: Organizations use gen AI to create product mockups, concept art, marketing visuals, and social media graphics from text descriptions. It can also be used to create line graphs, pie charts, and other data visualizations.
3. Summarization: Gen AI can condense long documents, research papers, or meeting transcriptions into brief summaries that are easy to digest. 
4. Code generation: Gen AI is able to write functions, generate boilerplate, translate code between languages, explain unfamiliar code, debug code,  and write unit tests. 
5. Data analysis: Organizations can use gen AI to extract insights from survey responses, analyze sentiment in customer reviews, or categorize support tickets. 
6. Translation: Gen AI powers translation between languages while preserving tone, context, and nuance. It can generate translated captions or audio in real-time. 

Autonomous AI Security Risks

One of the main differences between agentic AI vs gen AI is the level of risk each introduces. Gen AI models are relatively safe by design. They sit in an API, respond to prompts, and don’t do anything unless told. The biggest risks typically include hallucinations, bias, and misinformation.

But autonomous AI security threats are much higher. AI agents have access to tools, APIs, files, and the ability to take actions in the real world. That power comes with some serious risk and the need for updated security models. 

Traditional security is about controlling who can do what. Agentic AI breaks that model because the agent has credentials, makes decisions, and executes actions. If an agent is compromised, misconfigured, or tricked, it can cause major damage. 

Risks of Autonomous AI Agents

Here are some of the most significant risks when deploying autonomous AI agents: 

1. Privilege escalation via prompt injection: An attacker tricks an agent into using its credentials to do something it shouldn’t, such as reading private data or modifying records. Because AI agents often have elevated access, a single compromised prompt can have far-reaching effects. 
2. Unauthorized tool use: Agents can call external tools, APIs, and services. If an agent is manipulated or misconfigured, it might delete files, send emails, modify code, or execute financial transactions before anyone notices. 
3. Data exfiltration: Agents that can read internal data like customer records, financials, or source code can be tricked into sending that data outside the organization.
4. Lack of observability: Traditional systems log every action, but AI agents often operate in “black boxes,” which makes it hard to track their actions. If an agent deletes a database row, was it following instructions or was it compromised? Without good logging, you can’t tell. 
5. Supply chain risks: Many agent frameworks pull in third-party models, plugins, and tools. A compromised dependency in the agent’s toolchain can give attackers a backdoor into your environment. 
6. Hallucination-driven actions: Even without malicious input, an agent might misinterpret a request, “hallucinate” a step that doesn’t exist, and take an action based on incorrect reasoning. Unlike a chatbot that just gives a wrong answer, an agent can do something harmful based on that wrong conclusion. 

How to Secure Autonomous AI Agents

Securing autonomous agentic AI is possible with a combination of thoughtful design and strong governance. Whether your business uses AI agents to research market trends, book travel accommodations, or provide customer service, these steps will help you secure your autonomous AI agents: 

1. Before you secure anything, you need to know what you’re protecting. Document every agent, every tool it uses, and what data it has access to. 
2. Apply least privilege. Agents should have the minimum permissions needed to do their job and nothing more. If an agent only needs to read a database, don’t give it write permissions. Where possible, issue short-lived tokens that expire after the agent’s task completes. Audit regularly to ensure agents' permissions haven’t expanded. 
3. Implement hard guardrails. Don’t rely on the agent’s own judgment to stay in bounds. Use a trusted governance control layer like Anjuna Overwatch, for example, to secure the agentic AI within Trusted Execution Environments, leveraging the hardware-rooted trust for an incorruptible control plane. . 
4. Build observability into every agent. Every agent action needs to be logged, traceable, and replayable. Log the full reasoning chain, including the agent’s plan, the intermediate decisions, why it chose that tool, and what it observed afterward. Capture every tool call, and implement session replay. Set up real-time alerts to flag anomalous behavior. 
5. Harden AI agents against prompt injection. This is the number one attack for AI agents, and it requires layered defenses. Use input sanitization to protect against known injection patterns. Architecturally separate system instructions from user input, and add a “reasoning firewall” that ensures user instructions don’t conflict with the agent’s core directives. 
6. Secure the supply chain. Agentic AI frameworks are new and rapidly evolving, with many dependencies that haven’t been fully tested. Vet all open-source packages, plugins, and third-party tools your agent framework uses. 
7. Update your incident response plans to account for AI agents. Create agent-specific playbooks, build a kill switch, and practice tabletop exercises. 
8. Audit your AI agents continuously, reviewing permissions, logs, and policy compliance. Maintain an agent inventory of every agent in use, its purpose, access scope, and risk level. For example, use the National Institute of Standards and Technology (NIST) AI Risk Management Framework as your baseline. 

Agenitc AI vs Gen AI Conclusion

Although there are significant differences between agentic AI vs gen AI, the most powerful systems combine both. A generative model thinks while the agentic layer acts, increasing productivity and efficiency for all types of organizations. But with that power comes responsibility. Autonomous agents introduce real security risks to enterprises that cannot be ignored, and traditional security methods alone aren’t enough. 

That’s where Anjuna comes in. Anjuna’s confidential computing secures agent workloads and the governance/control layer in hardware-enforced TEEs. Agent reasoning, tool calls, and sensitive data stay encrypted, invisible even to the infrastructure running them. That means no prompt injections, no rogue agents, and no exfiltration. 

Ready to secure your AI agents with an incorruptible supervisor? Contact Anjuna today to schedule a demo or talk to one of our experts. 

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FAQs

What risks do autonomous AI agents bring to enterprises?

AI agents have privileged access to sensitive data and the ability to act on their own. Without oversight, AI agents are subject to agent hijacking, task drift, and supply chain compromise. Rogue agents can expose sensitive data, while bad actors can spoof trusted agents to gain unauthorized access. 

Why do enterprises need agentic AI security framework?

Traditional security doesn’t cover AI agents. Agentic AI can act on its own, doing hundreds of tasks in a matter of seconds. By the time a human notices something wrong, the damage is done. Identity and access management controls who accesses systems, but agents are given broad credentials to do their jobs, which creates a single point of failure and a large attack surface. Enterprise security teams need specific solutions to monitor AI agents and guardrail their actions. 

How can organizations audit AI agents?

Auditing AI agents is essential for security and emerging regulatory compliance. Start by identifying every single AI agent in use, providing each with a unique identifier. Log every step of the reasoning chain, from plan to action. Maintain an immutable audit trail, and implement human-in-the-loop (HITL) checkpoints, especially for high-risk actions. Run TEW and red team testing, and continuously monitor for abnormal actions. 

Why is agent integrity crucial for AI governance?

An agent’s decisions are only as trustworthy as its reasoning process. Whether it's based on a hallucination or prompt injection, compromised reasoning leads to compromised actions, which can spiral out of control quickly. Anjuna Overwatch prevents compromise, ensuring agent integrity stays intact. 

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