What is agentic AI risk? It’s more than just AI. Understanding it could be the difference between your organization thriving in an AI-everywhere world or becoming its victim.
For security and compliance leaders, the threat model has fundamentally changed. It's no longer just about physical access control or even AI in security operations—it's about managing autonomous systems that learn, adapt, and act on their own.
Agentic AI risk isn’t just another cybersecurity buzzword. It’s a paradigm shift—and if we don’t adapt our threat models now, we risk being outpaced by systems that evolve faster than we can react.
What is agentic AI risk?
Unlike traditional security threats, agentic risks come from systems and entities (both human and AI) that demonstrate autonomy, learning capabilities, and adaptability.
These risks take two forms:
- Internal agentic risks: When your own AI tools lead to IP theft, privacy breaches, or data leakage. Think an employee using a note-taking AI that trains its models on your confidential meeting data.
- External agentic risks: When bad actors use adaptive AI to continually evolve their attacks against your organization, learning from each attempt.
The unpredictable nature of these systems means they're not deterministic and can't be managed with traditional risk frameworks.
Organizations in highly regulated industries like aerospace and defense face unique security challenges where agentic AI adds another layer of risk to their already stringent security requirements. Companies like StandardAero have implemented comprehensive visitor management solutions to address these challenges at their multiple facilities.
The difference between managing and controlling for risk
Most organizations are still stuck in old patterns when it comes to emerging AI threats:
- Managing risk: Writing down potential problems, creating mitigation plans, and addressing issues reactively, one by one.
- Controlling for risk: Building systems and processes that continuously evaluate and update security measures in cycles.
The latter approach is what's needed for agentic risk. “Controlling for risk means having the systems and ceremonies in place to continually evaluate and update security operations cyclically,” says Jolie Summers, Senior Director of AI Implementation at Sign In Solutions. “Ironically, you can control for agentic AI risk using agents.”
Our risk and compliance management solutions are designed specifically to help you move from simply managing risk to actively controlling it across your organization.
5 steps to take control of agentic AI risk
Want to get ahead of this issue before it becomes a crisis? Here's how:
1. Establish a tool evaluation framework
Before adopting any AI tool, it's critical to run it through a rigorous vetting process. Evaluate where the tool stores data, whether your information might be used to train external models, and the security certifications the tool holds. Additionally, you should confirm whether you can control the tool’s data retention policies. Thorough vetting ensures that you mitigate risks before the tool even touches your operations.
2. Create clear AI usage policies
It’s essential to document exactly how employees should interact with AI systems. Define which types of data can be shared with AI tools and clearly state which tools are approved for specific purposes. Specify who has access to various AI capabilities and what oversight mechanisms are in place. For organizations with multiple locations, implementing consistent visitor policies across all sites is critical to maintaining uniform security standards. Financial services companies like Everfox have successfully standardized their approaches, demonstrating how clear guidelines can help manage agentic risk across operations.
3. Implement continuous monitoring of AI systems
AI systems can't simply be deployed and forgotten. You must actively monitor for unusual patterns in AI system behavior, carefully track data flows in and out of AI tools, and create alerts for anomalies that could signal emerging threats. Regularly auditing AI decision processes ensures that the systems remain transparent and under control, helping to identify potential risks before they escalate into full-blown issues.
4. Develop human-AI collaboration frameworks
To maximize safety and effectiveness, position AI as a tool to enhance human capabilities rather than replace them. Train security personnel to prompt AI systems effectively, establish domains where human judgment has the final say over AI recommendations, and create formal supervision structures for AI agents. Focusing on building skills in information interrogation rather than information creation will help human teams critically evaluate AI outputs, maintaining oversight and reducing risks associated with blind reliance on AI.
5. Establish AI governance structures
Clear governance is non-negotiable when it comes to managing agentic AI risk. Assign responsibilities by designating AI safety officers and creating cross-functional review boards that oversee AI operations. Develop incident response procedures that are specifically tailored for AI-driven systems, ensuring that your organization is prepared to act quickly and decisively in the event of an AI-related incident. Governance structures should be tested and updated regularly to remain effective in a constantly evolving threat landscape.
A shifting mindset: AI in security operations
The integration of AI into security operations demands a fundamental change in how you approach your work.
When I have a complicated research problem, I open up ChatGPT, turn on deep research mode, type my question, and come back half an hour later. The AI has produced a report, and I can focus on reviewing and interrogating the findings.
This represents a profound shift:
- Old approach: Spend time gathering information, then make decisions based on what you found.
- New approach: Define the right questions, let AI gather information, then critically evaluate results and probe deeper.
Security professionals who master this new approach will thrive. Those who don't will increasingly find themselves outpaced by those who can effectively leverage AI as a force multiplier.
What this means for visitor management and access control
For organizations managing physical security, visitor access, and compliance, agentic AI risk introduces several new considerations. AI can now assist with enhanced pre-screening, evaluating visitors against threat intelligence databases even before they arrive on-site. It also enables behavior pattern analysis, allowing systems to identify unusual visitor behaviors that might signal reconnaissance activities or security testing. Automated compliance checks become more feasible, helping organizations streamline regulatory adherence processes without sacrificing overall security. Furthermore, visitor experience personalization becomes possible, as AI can tailor legitimate visitor interactions while still maintaining rigorous security standards.
However, rise of hybrid work models has complicated visitor management, making AI-powered solutions both more valuable and more complex from a risk perspective. Each of these benefits also introduces corresponding risks that organizations must actively manage.
Data privacy concerns emerge around how visitor information is stored, processed, and protected. Algorithmic bias poses another challenge, requiring vigilance to ensure AI systems do not unfairly target or flag certain groups. Transparency requirements are critical too, as security teams must maintain clear visibility into how AI systems make decisions. Finally, it’s crucial to establish override procedures that allow for human intervention whenever necessary, ensuring that humans maintain ultimate authority in security operations.
Why is AI in security operations important now?
The pace of AI advancement is not slowing down, and organizations that act now to take control of agentic risk will build significant competitive advantages. Proactively addressing emerging threats before they materialize equips organizations with the tools to substantially reduce the number and severity of security incidents.
AI also enables improved operational efficiency by automating routine security tasks, freeing human personnel to focus their attention where it matters most. Organizations can enhance their compliance posture by meeting evolving regulatory requirements surrounding the use of AI in security operations. Moreover, taking a proactive approach helps strengthen business continuity, reducing vulnerabilities to increasingly sophisticated AI-powered attacks.
Conversely, those who delay adapting to this new landscape risk falling behind. Waiting to address agentic risk could result in costly breaches, regulatory compliance failures, and operational disruptions—consequences that could have been avoided with a forward-thinking approach to AI security.
The human-AI partnership
Despite all the focus on technology, the human element remains central to effective agentic AI risk management.
I imagine a world where you've got one human and a dozen agents working for them. That human is able to oversee and supervise that collection. The human has the final say—we can't give up our responsibility just because AI is doing stuff for us.
This vision of human collaboration represents the future of AI in security operations—not humans replaced by AI, but empowered by it, making better decisions faster while maintaining ultimate responsibility.
Organizations that build this partnership effectively will create security operations that are more than the sum of their parts: leveraging the speed and processing power of AI with the contextual understanding and ethical judgment that only humans can provide.
Will you tighten security operations today?
Agentic risk is here. The question isn’t whether we’ll face it—it’s whether we’ll be prepared. The organizations that learn to lead in this new paradigm won’t just survive; they’ll define the future.
Get in touch with the Sign In Solutions team—we’ll help you navigate new challenges in highly regulated environments while creating a world-class visitor experience.
