It’s no secret that software is a part of our daily lives. We use it to keep our schedules, connect with friends and family, manage our finances, and execute everyday tasks for work. The convenience and speed it offers us, it also offers to cybercriminals. Especially in the last several years, it’s been impossible to ignore the impact of cyber attacks, which have shut down utilities, frozen the operations of major companies, leaked highly sensitive personal and competitive information, and been leveraged to extract millions and millions in aggregate ransom.
The Benefits and Challenges of AI
Artificial intelligence (AI) has generated exciting new possibilities for us in commerce and everyday efficiency, and it’s done the same thing for cybercriminals. Year after year, we see the scale and sophistication of attacks increase. With the rise of innovative technologies like edge networks – which enable the next phase of evolution for things like autonomous cars and 6G – we also generate more attack vectors for threat actors to exploit. It’s clear now that cyber security is not only essential to protecting the foundation of our lives today, but also to protecting the success of our future. AI-powered security is indispensable to that challenge.
A mirror image of what it does for attackers, AI serves as a force multiplier for defenders. Scale is one of the great drivers of business, of course, but also complexity, especially when it comes to networks. AI can augment the capability of a good security team exponentially, allowing them to find, prioritize, and remediate network vulnerabilities that might’ve been lost in the haystack before. Precision is key here: by prioritizing the most dangerous risks through AI, security teams are able to progressively decrease risk on an ongoing basis.
Beyond the more technical aspects, AI combined with steps like security consolidation generate immense benefits when it comes to the user experience. Rather than mastering a multitude of distinct (and sometimes fairly arcane) tools with limited interoperability and separate portals, users are empowered by AI tools to work in an intuitive, conversational interface. Crucially, it allows teams to work from a centralized pane of glass, offering a singular window into the entire network from which to strategize and orchestrate security.
This creates workflow efficiencies that are impossible to replicate without consolidation and AI. Of course, we interact with AI in its software form as well. Which means it’s not immune from exploitation. Securing AI – not just in security, but also in operational tools – must be a priority.
In fact, AI models themselves are becoming a target, as adversaries seek to influence how AI is trained and operates by poisoning data and finding and exploiting weaknesses directly through prompts. They can use deepfake technology to erode safeguards like voice and video chat. They deploy generative AI to create grammatically perfect phishing lures for social engineering. Specialized AI tools can scan networks to find and exploit vulnerabilities at an unprecedented scale. There are several key steps organizations must take to secure their AI usage.
The Benefits of Zero Trust for Artificial Intelligence
First and foremost, it’s important to strictly govern access to AI services and data. Zero trust network access (ZTNA) is an integral part of most centralized, AI-powered security platforms, and it’s one of the most crucial. Without rigorous segmentation, companies remain vulnerable to an attacker, who can enter through any number of vectors – most commonly compromised credentials – and then move laterally to the most profitable, and damaging, operations and data. With zero trust, each person is granted only the access they need to execute their job and no more, limiting the fallout from any one unauthorized access. Beyond that, zero trust can also identify user behavior that falls outside their typical scope, so even the most targeted user compromise situations can be quickly identified and remediated.
ZTNA needs to be combined with other, AI-specific safeguards as well. Securing the AI pipeline, so organizations have a good understanding of the data they’re ingesting, its provenance, and its specific utility, rather than hoovering up whatever’s available, is a priority. User education will be increasingly important as well, as AI tools, particularly generative tools in the vein of ChatGPT, diffuse to everyday, nontechnical employees. Establishing a protocol for secure prompts is an example, so that employees don’t unwittingly upload trade secrets, competitive intelligence, or other sensitive data to public AI engines. We’ve already seen the impact this can have on companies, even going so far as to invalidate patents.
AI is more than a passing fad. It has the characteristics of a foundational technology upon which the innovation of the future can be built. But to realize those gains, security becomes a primary strategic objective, an engine of innovation, rather than an afterthought. Implementing centralized, AI-powered security systems to secure AI use is the first step toward the future. By leveraging AI security in this manner, organizations can effectively leverage their full stack of tools to be more efficient and drive better operations, quality, growth, and development.
The post Securing the Software Supply Chain with AI appeared first on Unite.AI.