September 12, 2024

Navigating the digital economy: Innovation, risk, and opportunity

As we move towards the era of Industry 5.0, Digital Economy needs to adopt Human Centred Design (HCD) approach where technology layers revolve around the Human’s as the core. By 2030, it is envisaged to have Organoid Intelligence (OI) to rule the digital economy space with its potential across multi-disciplines with Super Intelligent capabilities. This capability shall democratize digital economy services across sectors in a seamless manner. This rapid technology adoption exposes the system to cyber risks which calls for advanced future security solutions such as Quantum Security embedded with digital currencies such as e-Rupee, crypto-currency, etc. ‘e-rupee’, a virtual equivalent of cash stored in a digital wallet, offers anonymity in payments. ... Indian banks are already piloting blockchain for issuing Letters of Credit, and integrating UPI with blockchain could combine the strengths of both systems, ensuring greater security, ease of use, and instant transactions. Such cyber security threats, also create opportunity for Bit-coin or Crypto-currencies to expand from its current offering towards sectors such as gaming, etc.?

From DevOps to Platform Engineering: Powering Business Success

Platform engineering provides a solution with the tools and frameworks needed to scale software delivery processes, ensuring that organizations can handle increasing workloads without sacrificing quality or speed. It also leads to improved consistency and reliability. By standardizing workflows and automating processes, platform engineering reduces the variability and risk associated with manual interventions. This leads to more consistent and reliable deployments, enhancing the overall stability of applications in production. Further productivity comes from the efficiency it offers developers themselves. Developers are most productive when they can focus on writing code and solving business problems. Platform engineering removes the friction associated with provisioning resources, managing environments, and handling operational tasks, allowing developers to concentrate on what they do best. It also provides the infrastructure and tools needed to experiment, iterate, and deploy new features rapidly, enabling organizations to stay ahead of the curve.

Scaling Databases To Meet Enterprise GenAI Demands

A hybrid approach combines vertical and horizontal scalability, providing flexibility and maximizing resource utilization. Organizations can begin with vertical scaling to enhance the performance of individual nodes and then transition to horizontal scaling as data volumes and processing demands increase. This strategy allows businesses to leverage their existing infrastructure while preparing for future growth — for example, initially upgrading servers to improve performance and then distributing the database across multiple nodes as the application scales. ... Data partitioning and sharding involve dividing large datasets into smaller, more manageable pieces distributed across multiple servers. This approach is particularly beneficial for vector databases, where partitioning data improves query performance and reduces the load on individual nodes. Sharding allows a vector database to handle large-scale data more efficiently by distributing the data across different nodes based on a predefined shard key. This ensures that each node only processes a subset of the data, optimizing performance and scalability.

Safeguarding Expanding Networks: The Role of NDR in Cybersecurity

NDR plays a crucial role in risk management by continuously monitoring the network for any unusual activities or anomalies. This real-time detection allows security teams to catch potential breaches early, often before they can cause serious damage. By tracking lateral movements within the network, NDR helps to contain threats, preventing them from spreading. Plus, it offers deep insights into how an attack occurred, making it easier to respond effectively and reduce the impact. ... When it comes to NDR, key stakeholders who benefit from its implementation include Security Operations Centre (SOC) teams, IT security leaders, and executives responsible for risk management. SOC teams gain comprehensive visibility into network traffic, which reduces false positives and allows them to focus on real threats, ultimately lowering stress and improving their efficiency. IT security leaders benefit from a more robust defence mechanism that ensures complete network coverage, especially in hybrid environments where both managed and unmanaged devices need protection.

Application detection and response is the gap-bridging technology we need

In the shared-responsibility model, not only is there the underlying cloud service provider (CSP) to consider, but there are external SaaS integrations and internal development and platform teams, as well as autonomous teams across the organization often leading to opaque systems with a lack of clarity around where responsibilities begin and end. On top of that, there are considerations around third-party dependencies, components, and vulnerabilities to address. Taking that further, the modern distributed nature of systems creates more opportunities for exploitation and abuse. One example is modern authentication and identity providers, each of which is a potential attack vector over which you have limited visibility due to not owning the underlying infrastructure and logging. Finally, there’s the reality that we’re dealing with an ever-increasing velocity of change. As the industry continues further adoption of DevOps and automation, software delivery cycles continue to accelerate. That trend is only likely to increase with the use of genAI-driven copilots.?

Data Is King. It Is Also Often Unlicensed or Faulty

A report published in the Nature Machine Intelligence journal presents a large-scale audit of dataset licensing and attribution in AI, analyzing over 1,800 datasets used in training AI models on platforms such as Hugging Face. The study revealed widespread miscategorization, with over 70% of datasets omitting licensing information and over 50% containing errors. In 66% of the cases, the licensing category was more permissive than intended by the authors. The report cautions against a "crisis in misattribution and informed use of popular datasets" that is driving recent AI breakthroughs but also raising serious legal risks. "Data that includes private information should be used with care because it is possible that this information will be reproduced in a model output," said Robert Mahari, co-author of the report and JD-PhD at MIT and Harvard Law School. In the vast ocean of data, licensing defines the legal boundaries of how data can be used. ... "The rise in restrictive data licensing has already caused legal battles and will continue to plague AI development with uncertainty," said Shayne Longpre, co-author of the report and research Ph.D. candidate at MIT.?

Read more here ...