The dark side of generative AI: How using AI in marketing, advertising and communications can expose confidential organisational information
Correct use of sensitive and confidential information requires guarding of valuable intellectual property. These challenges are further exacerbated in the age of generative AI. Generative AI tools open new possibilities for corporate content makers, but they also give risk to a whole new set of security risks.
Marketers, advertising professionals, communications and content experts are in the business of creating content and showcasing stories that highlight the strengths of the organisations they represent. That means they are often privy to highly confidential organisational insights and information that gives them knowledge much broader and deeper than their colleagues and in advance of most of the rest of the organisation and the market.
Many are currently jumping with joy thinking generative AI is the answer to their prayers to write media releases, social posts and campaign materials at lightning speed without the need to engage outside consultants. Others in the organisation may also use generative AI to write business emails, summarise long reports or create business plans. Generative AI promises instant gratification with seemingly decent results.
But generative AI tools require information. The information provided in a question or prompt to a generative AI tool may result in the sharing of confidential or sensitive organisational information. This introduces new risks for organisations of any size, including private companies, government departments and agencies, and not-for-profits.
Generative AI tools are built on large language models (LLMs) – vast collections of data that have been built by collecting data from hundreds of different sources that is analysed using powerful algorithms. When the model is asked a question, it produces an answer based on the data in the LLM, which may not even be current. Whenever a generative AI tool, like ChatGPT, is asked a question, it adds the data from the question to the pool of data in the LLM.
That means the data is added to the LLM and then usable by others that access the same generative AI tool. The data you feed into generative AI about your question is also captured and stored in its memory and can be exposed to third parties.
This can cause everything from a tarnished reputation for leaking confidential information through to potentially breaching data protection and privacy laws and regulations.
Marketers, communications and content teams need to be aware of these risks as do owners of startups, small and medium business, senior leaders in enterprise teams, government heads and boards.
The process starts with awareness and education. Everyone must understand that generative AI tools are trained using the data they ingest from myriad data sources and information that is presented to them. When a generative AI tool summarises a financial report, the data it summarises becomes part of the pool of data stored in the LLM that underpins the generative AI tool. If you ask it to improve the copy you have written, it now has all that information stored in its accessible memory about your organisation.
Rather than using public generative tools such as Chat GPT, Meta AI, Google Gemini, Microsoft CoPilot and others, organisations can look to invest in their own private generative AI tools that don’t share data publicly. Many generative AI tools are built with open-source software that be freely acquired and deployed in private systems.
While marketing, advertising, public relations, communications and content teams and the organisations they serve can reap huge benefits from generative AI tools, they must pay close attention to the information they provide it. It may seem helpful to provide as much pertinent information as possible to the tool but doing so may expose the business to the risk of accidental information leakage with the potential for serious consequences.
Anthony Caruana and Kathryn Goater are the co-CEOs of Media-Wize.
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