As artificial intelligence-powered tools spread across enterprise knowledge estates and cloud environments, they are amplifying existing dangers, according to cybersecurity expert Liat Hayun.
According to an interview with TechRepublic, Hayun, vice president of product administration and analytics for cloud security at Tenable, advises organizations to focus on comprehending their threat posture and tolerance before addressing key concerns such as cloud misconfigurations and safeguarding sensitive information.
As AI’s accessibility surges, a notable concern arises: while companies remain cautious, the technology itself exacerbates certain risks. Notwithstanding this, she posits that contemporary CISOs are transforming into enterprise enablers, with AI potentially serving as a powerful tool in strengthening security.
As AI-driven technologies continue to revolutionize various industries, cybersecurity and knowledge storage are no exception. AI’s ability to analyze vast amounts of data at incredible speeds has given rise to innovative solutions that can detect potential threats in real-time, thereby fortifying the digital fortress. Additionally, AI-powered search engines have taken the realm of information retrieval to unprecedented heights, allowing users to effortlessly uncover relevant answers amidst a sea of data.
Initially, artificial intelligence (AI) has become significantly more accessible to organizations. Ten years ago, organizations developing AI required a team with specialized expertise in knowledge science and statistics, comprising PhD-holders in these fields, to design and implement machine learning and AI algorithms. With the advancements in AI technology, organizations can now easily integrate artificial intelligence solutions into their systems, much like implementing a new programming language or library. With the advent of artificial intelligence, numerous organizations – including both major players like Tenable and emerging startups – can now seamlessly integrate AI capabilities into their products.
One of the key requirements for developing effective artificial intelligence is a vast repository of knowledge. As numerous additional organizations seek to collect and process increasingly larger amounts of data, this inherently involves handling more sensitive information. Previously, my streaming service stored limited information about me.
As a result, possibly due to geographical misconceptions, I may develop specific recommendations influenced by those biases, stemming from factors such as age and gender, amongst others. As a direct outcome, individuals will leverage this expertise to drive business growth, prompting them to store it in larger quantities and with increasing levels of sophistication?
If you wish to retain a vast amount of knowledge, it’s Every time you decide to store a new type of data, the volume of information you’re holding increases accordingly? Don’t bother going within yourself to recall stored memories and summon fresh insights; instead, explore external sources for new perspectives and learning. By clicking a single button, a brand-new knowledge hub is instantly created, ready for use. The advent of cloud technology has significantly streamlined the process of storing information.
The three parts form a self-sustaining cycle that perpetuates its own existence. As storage becomes easier, the ability to retain more information improves, prompting further motivation to store even more knowledge, creating a self-reinforcing cycle. Over the past couple of years, the increasing accessibility and widespread adoption of Large Language Models (LLMs) by organizations have introduced challenges across all three verticals.
As AI technologies continue to advance at a breakneck pace, it’s imperative that we grasp the potentially devastating consequences of unchecked artificial intelligence. With AI systems increasingly integrated into our daily lives, from self-driving cars to medical diagnosis tools, the stakes are higher than ever before.
While AI adoption has surged among individual users worldwide, its implementation within organisations is still in its nascent stages. To ensure the successful integration of new technology, organisations must adopt a thoughtful approach that minimises potential risks without unduly threatening their operations. While statistical data is limited, the available examples are primarily experimental in nature and may not provide an ideal representation.
A potential pitfall arises when AI systems are trained on sensitive information. That’s one thing we’re seeing. It’s not because organizations are neglecting caution; it’s due to the significant challenge of distinguishing sensitive information from nonsensitive data while still implementing an effective AI system trained on the appropriate knowledge set.
The second factor we’re currently observing is that. Even when an AI agent is trained on non-sensitive information, if this data becomes publicly available, an attacker can manipulate the publicly accessible knowledge repository and have the AI utter unintended statements by injecting their own knowledge into it? It’s not this all-knowing entity. The entity is cognizant of its observations.
How do organizations gauge the scope of their publicity, encompassing the cloud, artificial intelligence, and data – as well as everything tied to their utilization of third-party vendors and diverse software within their organization?
The second half is, ? Given the public nature of this asset and its critical vulnerability, addressing this issue should be your top priority? Isn’t it truly a blend of both, though? When faced with two seemingly similar issues, one requiring sensitive information and the other not, it’s essential to address the former priority first.
You should also be aware of the necessary steps to mitigate these exposures with a minimal impact on your business operations.
Three key challenges we frequently caution our prospective clients about exist.
The primary cause of cloud security breaches is often attributed to misconfigurations. Owing to the intricacies of infrastructure, the multifaceted nature of the cloud, and the diverse array of applied sciences it encompasses, even within a single cloud environment – but particularly in multi-cloud scenarios – the likelihood of something becoming problematic due to inadequate configuration remains unacceptably high. To ensure seamless integration into existing workflows, I would prioritize simplifying the learning curve for new technologies, such as AI.
The second is over-privileged entry. Despite widespread complacency, many organizations remain vulnerable to unexpected threats. But when your home becomes a fort, and you’re freely handing out your keys to everyone around you, that’s still a significant challenge. Extremely sensitive access to critical knowledge and infrastructure is another key area of attention. Despite thorough configuration and a lack of malicious hackers, this setup still poses additional risks.
What key stakeholders primarily focus on is identifying and detecting potential security breaches from an extremely early stage. By leveraging AI-powered tools within our security frameworks, we can harness their ability to quickly scan vast amounts of data, thereby enabling real-time detection of suspicious or malicious activities in a given environment? We must address these behaviors and actions at the earliest opportunity to prevent any critical consequences from arising prematurely?
Harnessing the power of Artificial Intelligence (AI), businesses can’t afford to miss this unprecedented opportunity.
With over 15 years of experience in the cybersecurity field, I’ve… While many security professionals have evolved over the years, it’s refreshing to see that most safety specialists and CISOs have transformed from their predecessors just a decade ago. Rather than playing the role of gatekeeper, or dismissing innovative ideas because they pose some level of risk, these individuals ask themselves a far more productive question: “How can we utilize this concept in a way that minimizes its potential dangers?” This shift in mindset is truly an exemplary approach. As they continue down this path, they’re transforming into a full-blown enabler.
Rather than fearing the risks, organizations must proactively consider how to integrate AI, rather than simply dismissing it as too hazardous at present. You won’t be able to do this.
Organizations failing to integrate AI into their operations within the next few years risk falling irreparably behind the curve. This innovative software has the potential to significantly benefit numerous business applications, both internally by fostering seamless collaboration, evaluation, and insights, as well as externally through the tools we can showcase to our clients. There’s just too great an opportunity to miss out on. To enable organizations to adopt a forward-thinking approach where they confidently say, “We’re embracing AI, and we’ve mitigated the risks accordingly,” that would be the ultimate validation of my work.
