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2025 is a watershed moment forAIin the enterprise, especially generative AI.
Businesses across industries are integrating the technology at scale and with their critical systems and objectives.
Only then can AI be a net positive for business.
But how do they do so?
And AIsecurity, which monitors AI behavior, securing AI models, data, andapplications.
Safe, ethical systems are easier to secure; and secure systems are easier to govern.
Director of Product Management, AI Risk and Compliance at IBM.
They carefully oversee how ingredients are grown, procured, stored, and mixed.
Yet that same company keeps their factory doors unlocked and does not place tamper-proof seals on their products.
Do you trust them?
Do you trust them?
This same logic apples to AI.
You cannot govern a system that is not secure.
And you cannot secure a system without proper oversight.
A mix of fragmented tooling, poor communication, and skills gaps are driving this problem.
There are a dearth of integrated, end-to-end tools and processes for AI security and AI governance.
There is also a major skills gap: The people who create and maintain AI models are notcybersecurityexperts.
And security experts generally are not versed in AI.
These shortcomings carry steep costs.
Enterprises not only miss the full potential of AI, but also invite a range of risks and threats.
Data breaches can become more common costing businesses millions of dollars and violating compliance mandates.
And vulnerabilities can proliferate, creating attractive targets for bad actors.
Bridging the gap
AI governance and AI security are shared responsibilities.
The two disciplines have common objectives: heightening visibility and mitigating risk.
Both are also closely tied to data: Properly governing AI requires data governance.
And properly securing AI requires data security.
To properly entwine the two,collaborationmust happen both at the table and in the tech.
Meanwhile, the underlying technology for security and governance must be one unified, cross-functional experience.
This allowsemployeesworking on day-to-day model governance and cybersecurity to stay in constant contact with shared visibility.
The problem of shadow AI unauthorized models running within an organization provides a clear example.
If just one AI model eludes governance, it undermines the whole governance strategy.
This interplay can also apply to misconfigurations and vulnerabilities.
Businesses should have one shared approach, from the executive level down to their tactical tools.
This collaboration enables businesses to unlock the power of AI safely and securely.
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The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.
