Generative AI is reshaping how work is performed, often through incremental adoption rather than through a formal rollout. This can expose enterprises to significant and unexpected risk.
As such, legal and security leadership must collaborate at the intersection point. Working together proactively minimizes risk, but just as importantly, avoids having to address concerns once generative AI tools are embedded in organizational workflows and can’t be undone.
Here are five priorities that CISOs and General Counsels can use to avoid unnecessary exposure before generative AI becomes part of internal operations.
1. Clarify Ownership and Accountability Before Deployment
Generative AI tends to spread across an organization before anyone formally assigns responsibility for it. Legal, security, IT and business teams may all touch the same tools, but without clear ownership, no one is truly accountable. This lack of definition is where problems start: decisions about data use and approvals are made on the fly, and risks compound as the tools become routine. Establishing a cross-functional governance structure ensures that decisions about AI use are intentional, documented and consistently applied.
- Technical teams are often focused on what a tool can do and how quickly it can be deployed.
- Legal teams are looking at regulatory exposure, contractual obligations and privilege.
Aligning technical and legal perspectives early helps ensure AI use cases move forward with a clear view of enterprise risk, including both security and legal risk.
Learn more about what specific issues legal and privacy teams should consider when selecting AI tools in our blog post.
2. Manage Data Inputs to Minimize Risk
One of the most overlooked operational risk elements is how data inputs themselves are managed. The most significant source of risk is often the data itself, not the model processing it. The moment sensitive or regulated information is entered into an AI tool, it can be logged, stored or integrated in ways the organization did not intend or expect. Without clear limits on what can be shared, well-intentioned projects may unintentionally expose information outside of protected enclaves.
Data ingestion, processing, and retention within AI add additional areas of risk alongside the existing access control and more traditional security risk landscape. Some AI tools, by design, keep records of user interactions and may reserve the right to reuse that information in their license agreements. If those practices conflict with privacy requirements, other contractual commitments or internal expectations, the organization is left to manage the fallout.
Security and legal leaders need transparency into data storage — how it is stored and its lifespan — as well as whether it can be used beyond the original interaction. From a tech security perspective, the team should evaluate where data comes from, how that prompt is stored, what the retention categories are, if DLP is in place to scrub sensitive data elements, and how that data and the prompt are used within the AI instance. You don’t want a vendor owning those processes.
“Most AI-related incidents do not start with a system failure. They start with someone pasting the wrong information into the wrong place. Input discipline matters more than almost any other AI control.”
3. Understand and Negotiate Vendor Terms
In practice, vendor terms determine how much risk an organization actually takes on with generative AI.
These contracts do not always look like the software agreements CISOs and GCs are used to reviewing, particularly regarding output ownership, customer data use and model training rights.
It’s important to thoroughly review these terms, as the consequences for skimming or overlooking can be significant. Limitations of liability are often tucked into standard terms and can leave the organization bearing responsibility for outcomes it expected the vendor to handle.
Transparency in vendor practices matters just as much as pricing or features. Contracts should clearly spell out:
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How data is used, stored and deleted
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What breach notification obligations are explicitly in place
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The commitments a provider has made to support existing compliance requirements, rather than shifting this responsibility solely to customers
- Who owns inputs, generated outputs and IP
- How an AI instance is separated, how data is separated
- Who can see log data, how it is viewed, and what elements are stored
- What, if any, elements of the process are used for vendor enhancements
- Is the GenAI writing code? Is that following a proper development lifecycle review?
Taking time to negotiate these terms upfront reduces the likelihood of discovering unacceptable risk only after the tool is widely deployed.
Does your generative AI vendor own the content it generates? Our blog post discusses why you should know the answer to this question.
4. Build Governance That Scales With Adoption
Generative AI is often integrated into everyday work tools, from calendars and collaborative platforms to document creation and research. Its use is appealing and convenient, but its actual risk is rarely considered at the individual-user level. Rigid prohibitions usually do not stop its use. In fact, they can lead to shadow generative AI, which is even riskier from a security and legal standpoint. As adoption expands across teams and functions, governance must evolve to structured oversight that can accommodate broader and more complex use cases.
The most effective AI policies focus on actual AI usage, setting clear guardrails around acceptable behavior, data inputs and review expectations. Grounding policies in practicality and workflow use encourages employee compliance and makes enforcement easier.
Governance also cannot be static. AI tool use agreements and functionalities are changing quickly. Policies written once and left untouched can quickly lose relevance. Instead, integrate regular review cycles that provide opportunities to make necessary adjustments. These cycles also allow the legal, security and business teams to help keep governance practical, current and defensible as adoption grows.
5. Prepare for Security and Compliance Challenges Unique to AI
Generative AI is changing the security and compliance landscape in unique ways. Integrations, plugins and automated agents can create new paths into systems and new ways for data to move without clear visibility. These capabilities expand the attack surface and require security teams to rethink threat models, monitoring and incident response with AI in mind.
Much of the risk around AI still comes down to how people regularly use it. Controls will only go so far if users do not understand what a tool does with the information copied into it and how they can reverse course if they put the wrong information into an AI tool accidentally.
Practical training and education built around real situations help employees make better judgment calls. This can reduce the likelihood that team members will expose sensitive data into AI platforms or integrate sensitive data into tools operating outside approved security and compliance frameworks. Another concern worth highlighting centers on the velocity of modern AI threats and the resulting impacts. If an account is compromised, internal AI assets could be weaponized against the enterprise, making traditional response schedules obsolete in the face of AI-facilitated rapid escalation.
“AI introduces new paths for data to move that traditional controls may not reach. If teams do not account for that shift, risk can accumulate.”
Practical Takeaways for CISOs and GCs
Generative AI decisions often surface suddenly for CISOs and general counsel. As a result, these decisions are frequently reactive rather than proactive.
Your AI adoption roadmap should:
- Bring legal, security, IT and business leaders into AI discussions early so ownership and accountability are clear.
- Evaluate data exposure before approving tools and be explicit about which information is off limits.
- Require rigorous review and negotiation of vendor terms to the extent possible.
- Create policies that reflect real work patterns and encourage transparency.
- Establish a repeatable path for review and formal approval of both new AI and AI features within existing tools.
When security and legal teams stay aligned, AI adoption is easier to manage and far less likely to create surprises later.
ZeroDay Law works with security and legal teams to bring clarity and structure to AI adoption before risk compounds. Reach out to our team directly to learn more about how we can help you.