The CISOs Survival Guide to Generative AI Adoption in 2025

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Imagine it is Q2 of 2025. Your CEO just announced that starting Q3, every department needs to demonstrate improvements in productivity from AI use. Meanwhile, your security dashboards are flashing indications that employees have copied and pasted sensitive customer information into ChatGPT, asked Bard to write code snippets, and submitted HR questions to GenAI chatbots.

This is not a hypothetical scenario. In 2023, Samsung employees inadvertently uploaded some of the company’s source code to ChatGPT. In 2024, several banks reported that confidential customer data was quaintly included in the training datasets for large language models (LLMs).
There is real tension:
More productivity translates into more risks too: sensitive data exfiltration, vulnerability injection into any system or application, and hackers automatically execute exploits at scale.

The GenAI Boom: Fuel and Fire

Generative AI is not a novelty, it is a life-changing $4.2 trillion opportunity that is transforming finance, healthcare, SaaS, manufacturing, retail, and public sector services.
However, here’s the irony: while AI helps us speed up efficiency, it counterbalances this benefit by creating perpetual and new shadow AI risks – unauthorized tools, unmanaged flows of data, and new attack surfaces.

The 2025 CISO Dilemma: Secure AI or Get Left Behind

GenAI is not a technology project by 2025; it is a board-level mandate.
Boards are asking: “How do we use AI to get ahead?”
CISOs must respond: “How do we secure AI without slowing down innovation?”
Hackers are already using AI to:
In contrast, CISOs should be expected to:

Six Steps CISOs Should Take to Safely Enable GenAI in 2025

We will go through 6 practical steps to safely allow usage of Gen AI across your organization.

Step 1 - Visibility

As a CISO, you should know which all Gen AI applications are being used in the company. These don’t have to be limited to what is accessed using SSO. Any access should be captured in this list.

There are few ways of doing this: talk to your SSE vendor and see how much traffic is being analyzed by them. Are they configured to use a split tunnel where a lot of Internet traffic is skipped? If yes, you may need another solution that runs on the endpoint. Otherwise, you can look at their logs and see if you are able to capture this list. We provide this list using Kitecyber while skipping 0% of Internet traffic.

Step 2 - Access Control: Sanctioned vs Unsanctioned Gen AI Apps

Have a discussion with your team to see which all applications they want to use or experiment with. You can start with a smaller list since there is no fundamental difference between one LLM to another. Many of our customers start with OpenAI or Claude and then explore other models if needed. If you must use something on-premises, you can deploy models like Mistral or Llama in house and use their API endpoint.
Once you have the list, you should be able to mark the approved list as sanctioned and others as unsanctioned, so that you can actually block the access to them.

Step 3 - Discover Sensitive Data

Knowing and blocking Gen AI is a good starting point, but the real thing you want to know about and protect is data. There are two types of solutions used for discovery:

Endpoint DLP: use these solutions to classify files and discover sensitive data across your endpoint devices.

DSPM: The data can also be in cloud drives or other cloud storage platforms. You can use a DSPM solution to discover the data in cloud drives or cloud storage.

Using a combination of a DLP and DSPM solution, you should be able to create a complete view of the sensitive data across devices, cloud storage and drives. Note that most DSPM solutions are not inline so although they can discover data, they are not in the network path. Similarly many Endpoint DLP solutions are also not inline with network traffic.

Step 4 - Track data movement and stop leakage

Once you know where the sensitive data lives, the next step is to track its movement and block exfiltration to unauthorized places. The data movement can happen from cloud storage, or from endpoint devices. For cloud storage, the first defence is to maintain proper access control. Make sure every access can be uniquely identified to a specific user or service account. If you are using the principle of least privilege access grant, then you can significantly reduce the overall risk of data leakage.
Most DSPM solutions do discovery and classification but are not in the data movement path. For endpoint devices, there are two types of approaches:
Endpoint based: In endpoint based approaches, there is inspection of data as part of network stack or browser extension. This is a distributed and more efficient approach as all the data does not need to be tunneled through an external gateway.

Network based: In network based approaches like SSE or secure web gateway solutions, one has to tunnel all the Internet traffic through them. This traffic needs to be decrypted, inspected and encrypted back before getting forwarded. This hairpinning of data adds latency, bandwidth limitations and also does not work for many sites that are end to end encrypted. Some of the ones we have heard are Zoom, Teams and other meeting applications. Also a lot of sites are bypassed due to impact on their latency or performance.

Step 5 - Auditability and Reporting

Gen AI apps are evolving at a fast pace and sometimes the configurations are not able to catch up with what’s out there. Also it is not possible in many cases to add very strict controls as that may impact employees’ productivity.

The best solution in that case is to have a product that allows for complete auditability of access to all the apps. That acts as a deterrent for insider threats and also gives confidence to your customers that you are serious about security and are deploying tools to give you ability to do incident analysis if something happens.

Final Word: CISOs as AI Enablers

By 2025, GenAI adoption won’t just be required, it will be an existential necessity. And so, too, of security.

CISOs who find success will not only think about security lines drawn in the sand, they will also think of how to enable GenAI adoption but in a way where productivity doesn’t come at a cost to security.

If your team is looking to adopt GenAI in a secure way that doesn’t sacrifice privacy or performance, solutions like Kitecyber can assist by integrating endpoint DLP, network DLP (data loss protection), and AI informed cyber guidance that acts like having a security expert sitting at the elbow of every employee.

Ajay Gulati

Ajay Gulati is a passionate entrepreneur focused on bringing innovative products to market that solve real-world problems with high impact. He is highly skilled in building and leading effective software development teams, driving success through strong leadership and technical expertise. With deep knowledge across multiple domains, including virtualization, networking, storage, cloud environments, and on-premises systems, he excels in product development and troubleshooting. His experience spans global development environments, working across multiple geographies. As the co-founder of Kitecyber, he is dedicated to advancing AI-driven security solutions.

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