Mr. Maurice Uenuma, Blancco Vice President and Head of Americas Division
Artificial intelligence (AI) is increasingly being adopted by businesses to improve data-driven decision-making, automate processes, generate new content, and improve customer experiences. The emergence of generative AI (GenAI) applications like ChatGPT sparked widespread excitement about the technology, making AI available to nearly everyone for the first time.
However, the emergence of these applications has raised concerns about how to reduce risks while still reaping their benefits.
While GenAI models can certainly help in areas such as improving productivity, they also have drawbacks. Malicious AI chatbots such as WormGPT and FraudGPT and deepfake phishing are just a few of the AI-generated threats that have emerged recently. Without proper security measures, businesses are at risk of being exposed to these new attack vectors.
Dealing with GenAI red flags
AI is something for executives to consider, and there is good reason for both optimism and concern. Although the benefits and use cases are extensive, they are still largely unexplored, conceptual, and unproven. Most people have limited experience using his AI, so as the integration of his GenAI increases across all business lines, it is important for executives to clearly articulate and outline how he uses his GenAI. It is important to establish a targeted policy.
Without proper guardrails, GenAI tools that interact with external parties such as customers, partners, and vendors can expose companies to significant risks. These risks are similar to those associated with employees unknowingly interacting with infected files, visiting malicious websites, or accidentally sharing sensitive data with malicious parties. .
GenAI used in IT has the potential to undermine an organization's existing security posture by changing existing controls and safeguards, such as enterprise application security settings, data storage access, and security operating procedures. There is also. Gen AI applications can ingest sensitive corporate data or 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 the return on their AI investments while protecting sensitive data is through careful data governance and management. AI models place a new emphasis on data quality. Producing valuable results requires clean, high-quality datasets.
This makes it even more important for businesses to understand the value of data and regularly reduce the amount of low-quality data that does not improve AI output or help make informed business decisions. I am. Collecting excessive or irrelevant data reduces ROI and creates security issues by increasing the attack surface.
It is worth noting that GenAI can be a major source of sensitive and redundant, obsolete, or trivial (ROT) data leaks. For example, GenAI could combine cues to generate virtually accurate personally identifying information (which must be protected under existing regulations and standards) and make that information available without appropriate security controls. This could potentially expose businesses and their customers to new cyber risks.
Therefore, maximizing the ROI of AI must include a well-defined governance framework and investment in specialized tools for data discovery and classification. Data loss prevention solutions limit the spread of unauthorized data and provide an additional layer of security. Removing unnecessary data through data sanitization also minimizes storage costs, which become important as data volumes grow.
As cybersecurity threats evolve with AI, a disciplined approach to data collection and management is key to maximizing financial return while protecting sensitive information from new risks. In essence, the hype surrounding generative AI in data lifecycle management needs to be approached with caution and tempered with reality.
As AI becomes more pervasive and new regulations emerge to protect the public interest, businesses need to ensure compliance across complex new data workflows and value chains. Effective data governance is key to optimizing these processes.
Embrace the future of generative AI
Given that GenAI increases the sophistication and speed of cyberattacks while also strengthening cyberdefenses, enterprises will embrace it as a potentially powerful security tool. Waiting for government regulations to protect against AI cybersecurity threats is not a viable strategy. Instead, organizations should establish internal policies that provide guardrails for safely and securely using generative AI.
Additionally, companies need to leverage AI for competitive differentiation while being realistic about AI's ability to help achieve business objectives while mitigating associated security risks.
The future is now, and businesses must adapt their security strategies to keep up with the AI-powered data revolution. GenAI has immense potential for productivity improvements, but it must be approached with caution due to security risks. By establishing comprehensive policies, reducing data attack surfaces, and leveraging specialized tools, organizations can maximize return on investment from AI while protecting operations.
Companies must take proactive steps to responsibly and securely integrate GenAI into their systems. The success of companies in adopting these technologies will depend on their ability to avoid getting caught up in the AI hype and adapt and evolve with the data revolution it brings.
Disclaimer: The views and opinions expressed in this guest post are solely those of the author and do not necessarily reflect the official policy or position of The Cyber Express. The content provided by the author is the author's opinion and is not intended to defame any religion, ethnic group, club, organization, company, individual, or any other person.
