September 19, 2025
3 Steps to Manage Hazardous Generative AI Security Risks
Contents
Generative AI, or GenAI, has exploded in popularity in the past couple of years. According to the McKinsey Global Surveys on the state of AI, organizational use of GenAI more than doubled from 2023 to July of 2024, climbing from 33% to 71% in just months. Forward-looking organizations are integrating it strategically into workflows, customer engagement, and decision-making. With this boom, though, comes a myriad of generative AI security risks.
For industries like financial services, insurance, and healthcare, regulations are strict. Reputational stakes are also high. In these sectors, managing AI risks isn’t optional. It’s essential. Proactive oversight ensures generative AI goes beyond experimentation. It becomes a sustainable source of value that aligns with enterprise goals and regulatory demands.
This guide will walk you through three crucial pieces of an effective AI risk management strategy, to empower you to start protecting your organization as soon as possible.
Step 1: Establish a Strong AI Use Policy
An AI use policy is the foundation for the safe, transparent adoption of AI in any organization. At its core, it is a set of written guidelines that defines how your organization will engage with AI. It clarifies what is permitted, what is restricted, and what requires review, tracking, and documentation. The best policies strike a balance: simple enough for employees to understand and follow, yet thorough enough to mitigate risk across the enterprise.
Even if you think your organization is in the minority and hasn’t adopted AI, the threat of shadow AI (employees experimenting with tools outside of official oversight) suggests that you can never be sure you’re immune to generative AI security risks. Teams without a clear AI use policy risk creating an environment with dangerous levels of shadow AI.
A well-crafted policy provides:
- Legal and Regulatory Compliance: Clear standards for aligning with laws, frameworks, and industry regulations.
- Data Privacy and Security: Guidance on how sensitive data can, and cannot, be used in AI systems.
- Monitoring and Oversight: Expectations for model tracking, auditing, and continuous evaluation over time.
- Transparency: Commitments to explainability and responsible decision-making that build trust with stakeholders.
To learn more about developing your own AI use policy, check out our related article: How to Build an AI Use Policy to Safeguard Against AI Risk.
Step 2: Implement GenAI Guardrails
Even the best AI use policies can’t eliminate AI risk on their own. Guardrails are the technical controls and monitoring mechanisms that ensure generative AI is used safely, responsibly, and in compliance with internal and external requirements. They minimize risks and help organizations get value from AI without compromising security or trust.
Effective guardrails go beyond simple restrictions. They can also:
- Protect sensitive data by preventing exposure of private or regulated information.
- Defend against adversarial attacks that manipulate AI systems into harmful outputs.
- Reduce hallucinations by monitoring accuracy and establishing thresholds for acceptable performance.
- Enforce ethical standards by blocking the use of AI in inappropriate or non-compliant contexts.
This is where responsible AI platforms like Lumenova AI play a vital role. We help you set up filters that automatically block risky inputs. These inputs can be identified by certain words, topics, adversarial attempts, or the presence of sensitive data. By embedding guardrails directly into workflows, we ensure that employees can leverage generative AI confidently, without exposing the organization to unintended risks.
Step 3: Monitor, Test, and Continuously Improve
Generative AI is not static. Models evolve, employees start trying new tools, and regulations shift. What was safe and compliant yesterday may no longer be tomorrow. That’s why continuous monitoring, testing, and improvement are essential. This perpetual advancement empowers your team to both manage risk, maximize return on AI investments, and avoid wasted spend on tools that no longer deliver value.
By monitoring AI systems in real time, leaders gain visibility into whether generative AI is performing as intended and where it may be drifting off course. Responsible AI platforms catch exposure to generative AI security risks so they can be mitigated before turning into crises.
When it comes to security risks, company culture is just as vital as technical controls. A responsible AI culture trains employees to use GenAI with care. It also aligns legal, compliance, and technical teams under one shared framework. This alignment turns oversight into a competitive advantage: leaders can scale what works, sunset what doesn’t, and ensure resources are directed toward initiatives that deliver measurable impact.
By treating monitoring as an investment discipline rather than a compliance chore, organizations safeguard against risk while strengthening the business case for AI.
Guardrails: A Stepping Stone to Growth
GenAI is offering a new path to organizations seeking innovation and a new competitive edge. Yet without clear policies, meaningful guardrails, and ongoing oversight, it can just as easily become a source of hidden costs, compliance failures, and wasted effort. The difference between success and stagnation lies in how leaders choose to govern it.
If you’re ready to talk about managing generative AI security risks, we invite you to reach out to book a demo today.