You’ve charted your course. Your organization has committed to AI adoption, understanding both its transformative potential and considerable challenges. But here’s what every experienced navigator knows: departure is the easy part. The real test begins when land disappears behind you and your vessel is surrounded by the dark, turbulent waters of landless uncertainty.
This is where many organizations falter. They invest in strategy development, secure buy-in, and enthusiastically smash the champagne bottle on the bow of their AI ship, only to discover that AI adoption isn’t a destination. It’s continuous navigation through waters that change daily. New vulnerabilities emerge. Regulations evolve. The AI landscape shifts faster than any technology environment you’ve managed before.
The question isn’t whether you’ll encounter challenges. It’s whether you’ll spot them in time to respond effectively.
Your Navigation Instruments: Operational Governance That Actually Works
Remember those frameworks we discussed last month (NIST AI RMF, ISO 42001)? They weren’t meant to gather dust on a SharePoint site. These are your nautical charts, and like any navigator worth their salt, you need to consult them constantly.
Strategy without execution is just planning. Execution without monitoring is just hope.
Effective operational governance means establishing regular checkpoints where you actually use your frameworks. Monthly reviews to assess new AI tool deployments. Quarterly deep-dives to evaluate whether your controls keep pace with your adoption rate. Annual assessments to ensure your AI strategy aligns with evolving business objectives and regulatory requirements.
Before any new AI system goes live, run it through structured evaluation. Not just IT security questions, but strategic ones. How does this tool handle our data? What happens if the vendor gets breached? Can we explain its decisions to regulators or customers?
We’ve developed a comprehensive resource to help organizations ask the right questions: The Richey May AI Risk Radar. It’s designed to identify risks before their consequences arrive, scanning your AI adoption horizon for threats that might otherwise catch you off guard. We’ll share more about this tool at the end of this article, including how you can access it for your own journey.
Reading the Water: Continuous Risk Monitoring
The AI security landscape doesn’t stand still. New attack vectors emerge weekly: prompt injection attacks that trick language models into revealing confidential data, model poisoning that corrupts algorithms, adversarial inputs that cause dangerous misclassifications. Case in point: Chinese hackers recently exploited Claude as part of their scheme.
Third-party AI tools introduce particularly complex challenges. Remember the MOVEit breach? A zero-day vulnerability in data transfer software created a supply chain attack that compromised hundreds of organizations. Now multiply that risk across the dozens of AI-powered tools your teams might be adopting (often without IT’s knowledge).
This is shadow AI adoption, and it’s far more prevalent than most executives realize. Marketing deploys an AI analytics platform. Sales uses an AI assistant for customer communications. Finance experiments with AI fraud detection. Each decision seems reasonable in isolation; each creates potential exposure.
Your continuous monitoring program needs to address two distinct risk categories:
- Technical AI Security Risks: Model vulnerabilities, data poisoning, adversarial attacks, API security weaknesses, and the unique challenge of AI hallucinations producing incorrect or dangerous outputs.
- Third-Party AI Vendor Risks: Supply chain vulnerabilities, vendor security postures, data handling practices, model training data provenance, and the vendor’s own AI risk management maturity. The questions you’d ask a traditional software vendor aren’t sufficient for AI providers.
The key is establishing mechanisms to detect risks before they become incidents. This means active scanning, not passive waiting. It means maintaining an accurate inventory of every AI system in your environment (both sanctioned and shadow deployments).
Weather-Proofing Your Vessel: Security Practices for AI Systems
How do you actually secure AI systems in practice?
Start with access management. AI systems often require access to substantial sensitive data. Role-based access control (RBAC) is your foundation, but AI systems may require more granular, attribute-based access control (ABAC) to properly limit exposure.
Data governance becomes exponentially more critical. Machine learning models are only as trustworthy as their training data. If attackers poison your training datasets, they can corrupt your model’s behavior in ways that might not surface until the damage is done. Your data pipelines need integrity controls, version management, and provenance tracking.
Model monitoring deserves special attention. Unlike traditional software, AI models can drift over time. Your security practices must include:
- Input validation and sanitization: Treating AI system inputs with the same suspicion you’d apply to any user-submitted data
- Output monitoring: Watching for anomalous model behavior or patterns indicating compromise
- Regular model evaluation: Periodic testing against adversarial examples and bias assessments
- Secure deployment practices: Treating model files as sensitive assets with proper version control
Then there’s penetration testing, but specifically for AI systems. Traditional pen testing doesn’t adequately address AI vulnerabilities. You need security professionals who understand how to test for model extraction attacks, membership inference attacks, and other AI-specific threat vectors.
The goal isn’t perfection. It’s discovering vulnerabilities before attackers do, giving you time to remediate rather than respond to an active incident.
Your Crew’s Readiness: People and Culture
Let’s come to terms with a paradoxical truth: most AI security incidents don’t result from sophisticated attacks. They result from well-meaning employees making reasonable-sounding decisions without understanding the implications.
A developer copies production data into an AI testing environment without proper anonymization. A business analyst shares confidential documents with a language model to generate a presentation. A sales representative uses an unapproved AI tool because it saves time, inadvertently exposing customer information.
None of these people are malicious. All of them create risk. Your strongest defense is a workforce that understands both the opportunities and boundaries of safe AI use.
This requires clear, enforceable policies on acceptable AI use. No vague aphorisms, but specific guidance on which AI tools are approved for which data types, what constitutes confidential information that should never be shared with AI systems, and how to evaluate new AI tools before adoption.
But policies alone accomplish nothing without training. Regular, engaging education on AI-specific risks. Not general cybersecurity training, but targeted content on AI security challenges relevant to each role.
Consider establishing AI champions within each department: people who understand both the business value and security implications of AI adoption. They become your early warning system, helping identify shadow AI deployments and ensuring their teams follow approved processes.
Your people are either your strongest defense or your weakest link. The difference is whether they understand the risks and feel empowered to make secure decisions.
Storm Preparedness: When Things Go Wrong
Despite your best efforts, AI-related security incidents will occur. The question is whether you’re prepared to respond effectively.
AI incidents have unique characteristics that traditional incident response playbooks may not address. How do you preserve evidence when the “crime scene” is a machine learning model? What data do you need to understand how an adversarial attack succeeded?
Your incident response plan should explicitly address AI scenarios:
- Detection mechanisms
- Containment procedures
- Investigation requirements
- Communication protocols for explaining technical incidents to non-technical stakeholders
- Recovery procedures for compromised models
The goal isn’t to dwell on worst-case scenarios. It’s ensuring that when an incident occurs, your team responds with confidence rather than confusion. Tabletop exercises that walk through AI-specific incident scenarios help test your response procedures before you need them in a crisis.
Organizations that provide vCISO services and incident response capabilities specifically for AI security can be invaluable, either as an ongoing resource or as emergency support when you need expertise fast.
Tools for the Journey
Safe AI adoption isn’t about eliminating risk (that’s impossible in a landscape evolving this rapidly). It’s about navigating risk intelligently: identifying threats early, implementing proportionate controls, and maintaining agility to adapt as conditions change.
This requires systematic approaches to risk identification and the discipline to apply them consistently.
That’s why we’ve developed The Richey May AI Risk Radar: a comprehensive questionnaire that covers:
- Technical security
- Vendor management
- Data governance
- Regulatory compliance
- Ethical implications
- Operational resilience
Think of it as your early warning system, scanning the horizon for risks that might otherwise catch you off guard, helping you map the unique landscape shaped by your industry, risk tolerance, and strategic objectives.
Here’s our gift to you: Download The Richey May AI Risk Radar and get a complimentary 30-minute consultation with our cybersecurity experts. We’ll walk through the questionnaire together, discuss your specific situation, and identify the most critical considerations for your AI adoption journey. No sales pressure; just experienced navigators helping you chart a safer course.
The best time to identify risks is before they become incidents. The best defense is understanding where you’re vulnerable before attackers discover it. The organizations that succeed with AI adoption aren’t the ones that rush ahead recklessly or hold back fearfully; they’re the ones that navigate deliberately, with eyes open to both opportunities and risks.
The waters ahead are challenging. But with the right tools, the right partners, and the right approach, your organization can thrive securely.
Ready to identify the risks on your horizon?
Download The Richey May AI Risk Radar and schedule your complimentary consultation today. Fill out the form below to download or contact the Richey May cybersecurity team at info@richeymay.com.
Download The Richey May AI Risk Radar
