September 28, 2024
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | BU Soft Tech | itTrident | Former Sr. VP & CTO of MF Utilities
The impact of IoT on product quality is not just reactive but also proactive. IoT-enabled traceability systems ensure that every component of a product can be tracked from its origin to the final assembly, ensuring full compliance with industry standards and regulations. Plus, automated systems can monitor and adjust energy usage in real-time, leading to more efficient operations that lower the overall carbon footprint of a facility. By minimizing energy waste, companies will contribute to a more sustainable environment while also realizing substantial cost savings. These savings can be reinvested into research and development, driving innovation and enhancing product quality. In return, compliance eliminates unnecessary product waste and energy consumption, which then lowers the final cost for consumers while heightening brand reputation. ... By combining the real-time data collection capabilities of IoT devices with AI-driven analytics, IoT technologies can be leveraged to enable the seamless integration of clean energy sources into industrial operations. Solar, wind, and other renewable energy sources can be efficiently managed through smart grids and automated systems that balance the energy load, ensuring that clean energy is utilized to its fullest potential.?
They exploit the all-presence and trustworthiness of PDFs to trick victims into opening malicious files that can contain malicious links, embedded code, or vulnerabilities that allow remote code execution. Security experts at Palo Alto Networks identified recently that hackers have been actively weaponizing PDF files to deliver new SnipBot malware. ... While the SnipBot employs a multi-stage infection process that begins with a signed executable which is disguised as a “PDF.” This uses the anti-sandbox techniques like “checking process names” and “registry entries.” To evade the detection the malware makes use of “Window message-based control-flow obfuscation” and “encrypted strings.” Besides this, it downloads additional payloads like a DLL that injects code into Explorer.exe through “COM hijacking.” The core functionality of SnipBot includes ‘a backdoor (single.dll)’ that creates a “SnipMutex” and enables threat actors to ‘execute commands,’ ‘upload/download files,’ and ‘deploy extra modules.’ ... As the SnipBot, various evasion techniques, payload delivery methods, and post-infection capabilities compromise systems and exfiltrate sensitive data.
Despite the potential for privilege escalation, Microsoft refused to accept the issue as a vulnerability. After Fortra reported it, the company responded by pointing to the "non-boundaries" section of the Microsoft Security Servicing Criteria for Windows, which outlines how "some Windows components and configurations are explicitly not intended to provide a robust security boundary." ... Reguly and Fortra disagree with Microsoft's perspective. "When UAC was introduced, I think we were all sold on the idea that UAC was this great new security feature, and Microsoft has a history of fixing bypasses for security features," he says. "So if they're saying that this is a trust boundary that is acceptable to traverse, really what they're saying to me is that UAC is not a security feature. It's some sort of helpful mechanism, but it's not actually security related. I think it's a really strong philosophical difference." ... Philosophical differences aside, Reguly stresses that businesses need to be aware of the risk in allowing lower-integrity admins to escalate their privileges to attain full system controls.
One of the key end goals for the integration of AI in manufacturing is the establishment of 'lights-out factories' which means fully automating everything within the factory environment so that there is minimal to zero need for human input. Such is the lack of a need for human intervention that you can effectively manage the production process with the lights turned off. FANUC is one example of a company that operates a lights-out factory in Japan to build its robots, having done so since 2001. The company makes 50 robots for every 24-hour shift, according to the Association for Manufacturing Technology, with the factory running unsupervised for up to 30 days without human input. Automotive manufacturing is another sector in which AI has been a major positive influence. BMW's AIQX?automates certain quality control processes by using sensor technology and AI. Algorithms analyze the data they record in real time and they send employees feedback immediately. It can quickly detect anomalies on the assembly line. Similarly, Rolls Royce has melded data analytics with AI, pulling in masses of data from in-service engines in real time and feeding this into digital twins.?
One of the most insidious threats in the post-ransomware landscape is the potential presence of multiple threat actors within a compromised environment. This scenario, while relatively rare, can have devastating consequences for victim organizations. The root of this problem often lies in the cyber incident ecosystem itself, particularly in the use of initial access brokers (IABs) by ransomware groups. These IABs, motivated by profit, may sell access to the same compromised network to multiple malicious actors. The result can be a perfect storm of cyber activity, with different groups vying for control of the same systems. ... Another vector for multiple-actor intrusions comes from an unexpected source: the tools used by information security professionals themselves. Malvertising campaigns have become increasingly sophisticated, targeting legitimate software distribution channels to spread compromised versions of popular security tools. Ironically, the very applications designed to protect systems can become Trojan horses for malicious actors. ... The complexity of modern cyber threats underscores the necessity of comprehensive forensic analysis following any security incident.
Beyond tackling data quality and scalability concerns, this necessary shift towards engineering innovation will lead to developing tools and frameworks that better support AI workflows, including handling large volumes of unstructured data (including images and videos). That, in turn, will foster a more collaborative and integrated approach between AI and data management practices. As the AI and data stacks complement each other, we can expect more cohesive and innovative solutions that address AI implementation’s technical and operational challenges. ...?This maturation process promises substantial benefits beyond the realm of developers and engineers. Just as the dot-com bubble burst led to the refinement and widespread adoption of internet technologies, the current focus on data curation and engineering in AI will pave the way for transformative applications across various industries. Imagine AI-powered healthcare diagnostics that rely on meticulously curated data sets or financial systems that leverage AI for predictive analytics to manage risks more effectively. These advancements aren’t just about enhancing technical capabilities; they’re about improving outcomes for society as a whole.