What Your Cybersecurity Needs in a Rapidly Changing Digital World
The importance of cybersecurity cannot be overstated. In the face of a multitude of threats that have plagued digital systems over the past few decades, companies globally are increasingly grappling with this crucial question: Why is cybersecurity essential?
Despite the numerous efforts to strengthen digital systems, cybersecurity threats persist, and their impact on the world can be severe.
Technology is constantly evolving and so is software. The advent of artificial intelligence, particularly generative AI, has revolutionized how companies integrate software into their operations. But as AI finds more use, software users are grappling with more complex security concerns, as it exposes considerable vulnerabilities.
How should companies go about safeguarding their software and data? One must recognize that cybersecurity is a critical aspect in today’s digital landscape. The objectives companies strive for through their security initiatives must therefore adapt to the growing complexity of data and software usage. By staying on guard against threats and being proactive about preventing them, companies can leverage technology to drive growth and maintain an edge in the market.
Cybersecurity programs cannot afford to focus solely on incident avoidance. Given the delicate nature of software systems, AI models, and underlying data, failures are inevitable and must be embraced. Intrinsic to AI, for instance, some errors are certain to occur occasionally – albeit less frequently than human errors. As software systems grow more complex, susceptibility to vulnerabilities increases. Consequently, cybersecurity programs must redouble their efforts and output to detect and respond actively to incidents when they do happen, rather than only preventing them.
One way in which to tackle risks is the adoption of “zero trust” architectures. These function under the assumption that adversaries will compromise all systems. Even the U.S. Government has adopted a zero trust strategy within its various departments and agencies. However, incorporating these architectures is simply one of the many comprehensive changes required to accept software system failures.
Companies must allocate additional resources to their incident response programs, conduct simulated “red team” attacks to reveal potential vulnerabilities in AI and software systems, and invest in in-house incident response planning. Furthermore, it is important to pursue other necessary measures to fortify organizations against the risks of today’s rapidly changing technological landscape.
Moreover, companies must expand their perception of “failure” beyond security threats when dealing with software and data systems. The scope of digital breakdowns has stretched beyond security fears and now encompasses various forms of harm spanning from performance hiccups, privacy breaches, to instances of discrimination, and even more. With AI technology infiltrating quickly, even defining security events has become a protracted and unclear process.
The security of AI models raises concerns about intellectual property and privacy. IP theft can occur if models are used unethically and if the training data is compromised. This can lead to unintended applications of AI systems and even the creation and spread of disinformation. Failures can also occur due to altered data, intricate dependencies, and unintended applications of AI systems. In today’s world, software systems cannot rest easy on the assumption of adversary involvement. Compromise can come from known and unknown sources.
Cybersecurity programs shouldn’t just focus on addressing security breaches as their remit. That limited approach would make their actions less and less effective, given the ever-growing scope of software failures. Instead of limiting themselves, security initiatives should be integrated into comprehensive risk management programs that involve evaluating all the ways that failures might occur. By doing so, teams can develop strategies to manage them effectively, whether they stem from a malicious actor or other sources.
Information security and risk management teams must comprise experts with diverse knowledge beyond just security. Legal professionals, privacy experts, data engineers and specialists in other fields all play vital roles in protecting software and data from emerging threats and ensuring comprehensive safety.
It’s essential for cybersecurity teams to prioritize monitoring failures. Sadly, it’s too common for organizations to find breaches and vulnerabilities in their systems not through their own security programs, but from third-party sources. The reliance on such entities for detection implies insufficient comprehension of software failures. As such, teams should take it upon themselves to understand the occurrences and mechanisms of these failures completely.
Each software system and database should incorporate a dedicated monitoring plan that includes metrics to identify potential failures. Although this approach is already gaining adoption in AI system risk management, embracing this best practice for software systems and databases on a larger scale will promote more effective risk mitigation.
Nonetheless, it’s vital to acknowledge that third-party entities can still bear significant responsibility in incident detection. In fact, they might even be essential in pinpointing failures. Initiatives like incentivizing “bug bounties” for identifying potential threats and establishing direct communication channels for users to report system vulnerabilities have consistently yielded positive results. Nevertheless, it’s crucial to bear in mind that the onus of identifying digital weaknesses cannot rest solely with third parties.
Are the aforementioned recommendations sufficient? Certainly not. As software systems are getting more complex, cybersecurity programs need to address an ever-increasing array of risks. This will require a significant amount of additional work and resources at every phase of the data and software life cycle. To achieve that, continuous monitoring is needed to guarantee data integrity over time. Security should also be an integral part of the development process, utilizing approaches like DevSecOps that integrate security throughout the entire development life cycle. Additionally, with the continued expansion of AI utilization, data science teams will need to invest more resources in risk management efforts to succeed.
Failures have become a norm in all digital systems, as companies learn from challenging experiences. As a result, cybersecurity programs need to address this reality in practice, not only because it is an undeniable fact, but also because it is already deeply embedded in the tech industry.