By Maurice Uenumavice president and general manager of the Americas, Blancco
Artificial intelligence (AI) is increasingly becoming a tool used by businesses to improve data-driven decision-making, automate processes, generate new content and improve customer experience. The emergence of Generative AI (GenAI) as ChatGPT was the catalyst for widespread enthusiasm for technology, with AI within reach of almost everyone for the first time.
However, the emergence of these applications has raised concerns about how to mitigate risks while still enjoying these benefits.
Indeed, GénAI Models are useful in areas such as improving productivity, but they also have their flaws. Malicious AI chatbots such as WormGPT and FraudGPT and deepfake phishing are just some of the AI-generated threats that have emerged recently. Without appropriate security measures, businesses risk being exposed to these new attack vectors.
Responding to GenAI Red Flags
AI is a concern for executives and there are legitimate reasons to be both optimistic and concerned. Although the benefits and use cases are numerous, they remain largely unexplored, conceptual and unproven. With most people having limited experience with AI, it is critical that management establish policies to clearly articulate and describe the use of GenAI as it becomes more integrated across all lines of business.
Without proper guardrails, GenAI Tools who interact with external parties, including customers, partners or suppliers, may expose the company to significant risks. These risks are similar to those associated with employees unknowingly interacting with infected files, accessing malicious websites, or inadvertently sharing sensitive data with malicious actors.
GenAI used in IT also has the potential to erode an organization’s existing security posture by modifying existing controls and protections, including enterprise application security settings, storage access rights data or security operations procedures. Gen AI applications could extract sensitive business data, or even create new sensitive data that needs to be protected (such as new employee or customer data based on other existing data sets).
The Impact of AI on Data Lifecycle Management
One of the key ways organizations can maximize their ROI in AI while protecting sensitive data is through careful data governance and management. AI models place a new importance on data quality: they need clean, high-quality datasets to produce valuable results.
This makes it all the more critical for businesses to understand the value of their data and regularly reduce the amounts of low-quality data that does not improve AI outcomes and contribute to informed business decisions. Collecting excessive or irrelevant data weakens ROI and creates security concerns due to a broader attack surface.
It should be noted that GenAI may become a major contributor to exposed sensitive data as well as redundant, obsolete, or trivial (ROT) data. For example, GenAI may gather clues to generate factually accurate personally identifiable information (which must be protected by applicable regulations and standards) and make it available without appropriate security controls, thereby exposing the company and its customers to new threat. cyber risk.
Therefore, maximizing the ROI of AI must include clearly defined governance frameworks and investments in specialized tools for data discovery and classification. Data loss prevention solutions can limit unauthorized data propagation, providing an additional layer of security. Removing unnecessary data through data sanitization also minimizes storage costs, which is important as data volumes increase.
With cyber security With threats evolving alongside AI, a disciplined approach to data collection and management is essential to maximize financial returns while protecting sensitive information from new risks. Essentially, the hype around generative AI in data lifecycle management needs to be approached with caution and tempered with reality.
As AI becomes more widespread and new regulations emerge to protect public interests, businesses will need to ensure compliance is maintained throughout complex new data flows and value chains. Effective data governance will be essential to optimize these processes.
Embracing the Future of Generative AI
If we assume that GenAI will increase the sophistication and speed of cyberattacks while improving cyberdefenses, then businesses will adopt it as a potentially powerful security tool. Waiting for government regulations to protect against AI-related cybersecurity threats is not a viable strategy. Instead, organizations should establish corporate policies that provide guardrails for the safe and secure use of generative AI.
Additionally, businesses must leverage AI to differentiate themselves from competitors while remaining realistic about its ability to achieve their business goals while mitigating associated security risks.
The future is now, and businesses must adapt their security strategies to accommodate the AI-driven data revolution. GenAI offers immense potential to increase productivity, but must be approached with caution due to security risks. By establishing comprehensive policies, reducing the data attack surface, and leveraging specialized tools, organizations can maximize the ROI of AI while protecting their operations.
Companies should take proactive steps to ensure the responsible and secure integration of GenAI into their systems. Successful adoption of these technologies by businesses lies in their ability to not get carried away by the AI hype, but to adapt and evolve alongside the data revolution brought about by AI.
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