Generative AI (GenAI) is expanding so quickly that security professionals are struggling to track its impact. Right now, employees are drafting their emails and reports using ChatGPT as their writing assistant, and sales teams are piping customer relationship management (CRM) data directly into AI assistance tools. Some developers are even connecting their code repositories to Copilot. Many teams are embedding GenAI into their daily operations before they’ve even figured out how to govern it.
The main issue with all of this is the speed at which companies have latched onto GenAI but ignored the development of good security and governance. Chief Information Security Officers, or CISOs, are facing a growing data-security crisis, one that their legacy systems were not built to manage because they were designed in a time when the framework for taking these new concerns into consideration didn’t even exist yet.
And while businesses are eager to harness the productivity that GenAI promises, their security teams are often left scrambling to make certain that things like proprietary data, intellectual property, and private or regulated information aren’t leaking into the large language models (LLMs) that sustain AI or are otherwise being mishandled by unmonitored AI agents.
The New AI Concern
CISO concerns are not hypothetical. The reality is that companies and organizations are adopting GenAI at such a staggering rate that, according to recent industry analytics, 88% of them have already incorporated generative AI into at least one business function. Such a rapid integration shows how enthusiastic these companies are about AI’s potential, but it also highlights how responsible GenAI enablement needs to be a priority. One study found that only 24% of Chief Information Officers (CIOs) and CISOs felt that the necessary governance policies were even in place to properly manage their current AI-related risks.
As a result, the real test for security leaders is how to build the practical guardrails they need to moderate correctly, as well as how to modernize the current oversight so AI adoption doesn’t sacrifice security and data protection to greater AI-driven productivity goals.
Re-Architecting in the Age of AI
Currently, data security architecture leans into perimeter defense and endpoint controls. Unfortunately, that is proving increasingly insufficient in an environment where data is being moved, summarized, consumed, and regurgitated by sophisticated, and often third-party, AI services. These older models operated under the assumption that the data flow would always be predictable and manageable at all endpoints. GenAI breaks this pattern by creating new, and even hidden, pathways for data to pass through the pipeline.
Captain Compliance reports that “ChatGPT and related OpenAI products triggered a wave of GDPR [General Data Protection Regulation] enforcement proceedings beginning in 2023.” This and other investigations have led to several new Information Privacy Acts to try to combat the new threat. When employees use a publicly available LLM, they are effectively uploading corporate data to an environment that exists outside the direct control of the organization’s security team. Now, even though LLM providers offer better data agreements, such immediate and easy accessibility to AI tools means that “shadow AI” has become an ongoing concern, and that security teams have to treat every AI interaction as a potential data-loss event until they can prove otherwise.
One study by Proofpoint showed that the sheer volume of data being moved through GenAI tools is overwhelming existing data loss prevention (DLP) solutions, mostly because legacy DLP was designed for a world of email and file transfers, not for the high-speed data flow that comes with an AI model. This means security teams need to shift their focus from merely blocking certain suspect actions to fully understanding the context of the data that’s being used and the purpose behind each interaction.
The Three Pillars of Security
To more fully contain the new AI-saturated ecosystem, CISOs need to focus on three important pillars:
1. Visibility
You can’t govern what you can’t see. Organizations need tools that can monitor the data flow going in and out of AI services. This includes not only identifying which AI tools are being used, but also what data is moving around, which will require next-gen data security platforms that can track data lineage across cloud services and other environments.
2. Policy
Old generic acceptable use policies are no longer adequate. Security teams need to collaborate with their legal and compliance department to better design practical rules for GenAI use. This includes classifying data according to its sensitivity and then setting specific rules for how each classification can interact with different AI models.
3. Enforcement
Traditional controls need to be turned into data security management solutions that can enforce policies in real-time. This way, they can empower employees to use GenAI productively while also offering guardrails to prevent accidental or even malicious data exposure. Basically, using AI to secure AI by having the machine learn to identify data usage patterns and classify data sensitivity automatically.
The Battle Ahead
For modern CISOs, the coming battle is less about keeping AI out of the businesses and organizations they monitor, because that AI ship has already sailed, and more about just integrating it responsibly. There needs to be a focus shift from blanket restrictions to intelligent enablement so the necessary security and governance foundations can be built to withstand the rapid expansion of generative AI.
The time for a reactive approach is long past. The growing complexity of GenAI demands proactive security architecture and leaders capable of building it.
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