Agentic AI stole the show at Black Hat this year, dominating headlines, conference sessions, and hallway conversations. Since then, dozens of articles have explored what it is, why it matters, and where it’s going next. If you’re responsible for security, IT, or compliance in your organization, you’ve likely felt the buzz around it—and maybe the anxiety.

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This post breaks down why agentic AI is having a moment right now, what it means for your workforce’s use of AI, how it impacts your AI governance program, and what you should be watching in the months ahead.

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Understanding the agentic AI hype

Agentic AI refers to AI systems—often built on large language models—that can take autonomous, multi-step actions toward a goal, not just respond to a single prompt. Instead of giving you an answer and stopping there, an agentic AI can plan a sequence of steps to complete a task, choose which tools or applications to use, and execute actions on your behalf, like sending emails, creating reports, or updating databases.

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Think of it this way: If a large language model (LLM) is the brain that predicts what to say next, an AI agent is the brain, plus the body and the job description, giving it the tools, context, and instructions to act in the world.

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At Black Hat, presenters and vendors showed off agents booking travel, executing SOC runbooks, generating and testing code, and even chaining together in “multi-agent collaborations” to solve complex problems.

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This shift from “ask-and-answer” AI to goal-seeking autonomy is what has the security world paying attention—because autonomy changes the risk profile dramatically.

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Why agentic AI matters for your workforce’s AI use

Agentic AI isn’t just something vendors are building. It’s something your employees may already be trying out. Recent media coverage and new product announcements from companies like Microsoft, Google, and emerging AI startups have made these capabilities easier to access, often through SaaS tools your workforce already uses.

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Agentic AI is poised to transform how work gets done in SaaS and cloud environments. But with transformation comes change to the risk landscape, governance requirements, and even the day-to-day responsibilities of IT and security teams.

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How agentic AI changes the risk equation

Traditional AI assistants wait for a user to act. Agentic AI flips the model: once triggered, agents can make independent decisions, sequence multi-step actions, and communicate with other applications or agents. This power unlocks major productivity potential, but also opens pathways for attackers to manipulate agents into performing actions the human owner never intended.

• Automated, autonomous actions: AI agents are not bound by a human operator’s shift schedule. They operate continuously, meaning potential exploits can spread faster.
• Adaptive behavior: Agents may change their approach based on conditions, making their actions harder to predict and secure.
• High trust levels: Many organizations treat AI agents like internal applications, granting them high or persistent access privileges.

This independence can lead to unintended, unauthorized, or risky outcomes—especially when governance guardrails are incomplete or absent.

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Emerging risks your AI governance program needs to address in the agentic AI era

Agent-driven shadow AI

Just like shadow IT, AI agents can be deployed outside formal channels—but with the added risk that they can integrate with systems, initiate changes, and act without human review.

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Autonomy means expanded attack surfaces

Agentic AI isn’t just reading data—it’s modifying systems, creating resources, and connecting to external services. That means more potential for over-provisioned accounts, unvetted integrations, and hard-to-track data flows.

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Every OAuth token, API key, and cross-platform connection an agent uses represents another potential entry point for attackers—especially if permissions are overly broad or poorly monitored.

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Persistent access requires new governance models

Whereas traditional AI assistants may never touch your production systems, agentic AI often requires ongoing OAuth tokens or API keys. If these aren’t monitored and scoped properly, they become long-term liabilities.

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Multi-application workflows cross compliance boundaries

An AI agent might pull data from one regulated system and process it in another that isn’t covered by the same compliance framework—creating inadvertent violations.

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Operational changes demand policy updates

From onboarding AI agents like you would a new employee, to adding “AI change management” to your governance playbooks, enterprise policies need to evolve to address autonomous digital workers.

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Reduced human-in-the-loop oversight

Autonomous execution reduces the opportunities to detect or halt a risky action before it’s carried out, increasing the importance of real-time monitoring and automated guardrails.

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Four steps you can take to get ahead of agentic AI risks

1. Inventory and classify AI use: Identify where agents are in use, what systems they connect to, and what permissions they have.
2. Apply least privilege: Audit and rein in programmatic access, including OAuth scopes, API keys, and integration permissions.
3. Educate your workforce: Make sure employees understand your organization’s guidelines for acceptable AI use—and the added risks that autonomous agents can bring.
4. Implement guardrails: As your employees experiment with AI tools and agents to drive productivity, automated guardrails can help you make sure their usage remains secure and compliant.

Bottom line: Agentic AI offers incredible productivity potential, but its autonomy demands a new level of visibility, control, and governance. For security, IT, and compliance teams, now is the time to get ahead of the curve—before agents start making decisions you didn’t authorize.

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How Nudge Security can help

As agentic AI expands into the workforce, visibility and control become critical. Nudge Security provides security and IT teams with a clear view of every SaaS and AI tool in use across the organization—including connected apps, OAuth tokens, and API integrations that agents rely on. With this automated discovery paired with real-time guardrails and remediation workflows, we help you reduce risk without slowing down innovation.

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Ready to get ahead of agentic AI risks? Start your free trial today and see how Nudge Security can help you secure and govern AI use.