When AI Joins the Attack Surface: Executive Strategies for Risk Mitigation

Artificial Intelligence has quietly but pervasively infiltrated enterprise operations — embedded in SaaS applications, used to automate processes, and increasingly trusted to assist with strategic decisions. Yet with this reliance comes risk, and much of it sits unmonitored. AI is no longer a backroom experiment. It is now part of your attack surface. And critically, it’s also part of your governance responsibility.

AI: The Silent Operator Inside Your Business

Modern AI doesn’t always arrive with flashing lights or banners. It is embedded within cloud platforms, recruitment tools, marketing software, and vendor services. These systems operate invisibly — making decisions, recommending actions, and accessing sensitive data. The result? A rapidly growing inventory of AI inside the enterprise that most Boards have never seen. This lack of visibility is more than a technical issue — it’s a strategic oversight. Boards are being asked to demonstrate accountability for technology that is:

• Hidden in vendor pipelines

• Powered by models they didn’t build

• Making decisions they can’t trace

• Accessing data that may never have been properly classified

And now, regulators and investors are taking notice.

Why Traditional Cyber Controls Aren’t Enough

AI presents a unique risk profile that traditional cybersecurity doesn’t fully cover. Here’s why:

• AI behaves differently in runtime. It can generate unexpected or even harmful outputs.

• It learns and adapts. Which means security can degrade over time if not actively managed.

• It draws from vast data pools. Including those that were never intended for AI consumption — raising major privacy and compliance concerns.

• It amplifies internal weaknesses. Especially poor information governance and access controls, leading to accidental oversharing or data misuse.

What’s required is a new approach — one that integrates cybersecurity, data governance, legal, and operational oversight into a unified AI risk framework.

Operational Governance Blueprint: 5 AI Risk Controls for Board Oversight

1. Establish an AI Inventory and Catalog

Every organization should know exactly what AI models, agents, and tools are being used — whether purchased, embedded, or developed in-house. 

Board Action: Instruct management to implement an enterprise-wide AI catalog. This should include risk scoring, usage tracking, model ownership, and data lineage.

2. Demand Runtime Oversight of AI Behavior

AI systems don’t just need to be secure — they need to be monitored in real-time. Just as a firewall inspects traffic, AI runtime enforcement must inspect outputs, detect anomalies, and prevent dangerous interactions.

Board Action: Ensure security operations teams are equipped to monitor and respond to AI behaviors across models, apps, and agents — including prompt injections, hallucinations, and policy violations.

3. Fix the Information Governance Gaps

AI can only be as trustworthy as the data it’s trained and fed on. Poor data classification, open access permissions, and unstructured data chaos are some of the biggest threats to safe AI.

Board Action: Oversee a full review of enterprise information governance. Require clear ownership of data discovery, classification, and access management — especially in collaboration tools like Microsoft 365 or Google Workspace.

4. Align Cross-Functional AI Risk Teams

AI risk cuts across security, compliance, legal, HR, procurement, and the business. But many organizations still manage these in silos. That leads to fragmented oversight and missed threats.

Board Action: Push for the formation of a dedicated AI Risk Council — a cross-functional governance structure that oversees the full AI lifecycle, from model selection to ethical use, security, and audit.

5. Maintain Independence from AI Vendors

Boards must ensure their organizations aren’t locked into a single AI vendor or model. AI is a fast-moving market — and relying solely on a platform’s built-in controls will limit flexibility, increase costs, and reduce governance control.

Board Action: Require vendor-agnostic governance policies. Contracts with AI vendors must include enforceable transparency, acceptable-use guarantees, and clear lines of responsibility.

A Governance Framework Built for AI

The future of AI governance lies in what is now being called AI TRiSM — Trust, Risk, and Security Management. This approach brings together:

• AI governance (ownership, policies, risk scoring)

• Runtime inspection (real-time anomaly detection, output monitoring)

• Information governance (data protection, lineage, permissions)

• Infrastructure protections (model sandboxing, prompt injection defenses)

These layers together form a resilient architecture that ensures AI behaves in line with enterprise intent.

Final Thought: Boards Must Lead the Shift

The days of deferring AI risk to the CIO or CISO are over. With AI now shaping financial outcomes, operational decisions, and even public trust — the Board must take ownership of AI oversight.

AI may be silent, fast, and invisible — but its impact is anything but. The organizations that lead this transformation won’t just comply with future regulations — they’ll win trust, attract capital, and operate with confidence in a machine-assisted world.