August 03, 2024

Solving the tech debt problem while staying competitive and secure

Technical debt often stems from the costs of running and maintaining legacy technology services, especially older applications. It typically arises when organizations make short-term sacrifices or use quick fixes to address immediate needs without ever returning to resolve those temporary solutions. For CIOs, balancing technical debt with other strategic priorities is a constant challenge. They must decide whether to invest resources in high-profile areas like AI and security or to prioritize reducing technical debt. ... CIOs should invest in robust cybersecurity measures, including advanced threat detection, response capabilities, and employee training. Maintaining software updates and implementing multifactor authentication (MFA) and encryption will further strengthen an organization’s defenses. However, technical debt can significantly undermine these cybersecurity efforts. Legacy systems and outdated software can have vulnerabilities waiting to be exploited. Additionally, technical debt is often represented by multiple, disparate tools acquired over time, which can hinder the implementation of a cohesive security strategy and increase cybersecurity risk.

How to Create a Data-Driven Culture for Your Business

With businesses collecting more data than ever, for data analysts it can be more like scrounging through the bins than panning for gold. “Hiring data scientists is outside the reach of most organizations but that doesn't mean you can’t use the expertise of an AI agent,” Callens says. Once a business has a handle on which metrics really matter, the rest falls into place, organizations can define objectives and then optimize data sources. As the quality of the data improves the decisions are better informed and the outcomes can be monitored more effectively. Rather than each decision acting in isolation it becomes a positive feedback loop where data and decisions are inextricably linked: At that point the organization is truly data driven. Subramanian explains that changing the culture to become more data-driven requires top-down focus. When making decisions stakeholders should be asked to provide data justification for their choices and managers should be asked to track and report on data metrics in their organizations. “Have you established tracking of historical data metrics and some trend analysis?” she says. “Prioritizing data in decision making will help drive a more data-driven culture.”

How Prompt Engineering Can Support Successful AI Projects

Central to the technology is the concept of foundation models, which are rapidly broadening the functionality of AI. While earlier AI platforms were trained on specific data sets to produce a focused but limited output, the new approach throws the doors wide open. In simple — and somewhat unsettling — terms, a foundation model can learn new tricks from unrelated data. “What makes these new systems foundation models is that they, as the name suggests, can be the foundation for many applications of the AI model,” says IBM. “Using self-supervised learning and transfer learning, the model can apply information it’s learnt about one situation to another.” Given the massive amounts of data fed into AI models, it isn’t surprising that they need guidance to produce usable output. ... AI models benefit from clear parameters. One of the most basic is length. OpenAI offers some advice: “The targeted output length can be specified in terms of the count of words, sentences, paragraphs, bullet points, etc. Note however that instructing the model to generate a specific number of words does not work with high precision. The model can more reliably generate outputs with a specific number of paragraphs or bullet points.”

Effective Strategies To Strengthen Your API Security

To secure your organisation, you have to figure out where your APIs are, who’s using them and how they are being accessed. This information is important as API deployment increases your organisation’s attack surface making it more vulnerable to threats. The more exposed they are, the greater the chance a sneaky attacker might find a vulnerable spot in your system. Once you’ve pinpointed your APIs and have full visibility of potential points of access, you can start to include them in your vulnerability management processes. By proactively identifying vulnerabilities, you can take immediate action against potential threats. Skipping this step is like leaving the front door wide open. APIs give businesses the power to automate the process and boost operational efficiency. But here’s the thing: with great convenience comes potential vulnerabilities that malicious actors could exploit. If your APIs are internet-facing, then it’s important to put in place rate-limiting to control requests and enforce authentication for every API interaction. This helps take the guesswork out of who gets access to what data through your APIs. Another key measure is using the cryptographic signing of requests.

The Time is Now for Network-as-a-Service (NaaS)

As the world’s networking infrastructure has evolved, there is now far more private backbone bandwidth available. Like all cloud solutions, NaaS also benefits from significant ongoing price/performance improvements in commercial hardware. Combined with the growing number of carrier-neutral colocation facilities, NaaS providers simply have many more building blocks to assemble reliable, affordable, any-to-any connectivity for practically any location. The biggest changes derive from the advanced networking and security approaches that today’s NaaS solutions employ. Modern NaaS solutions fully disaggregate control and data planes, hosting control functions in the cloud. As a result, they benefit from practically unlimited (and inexpensive) cloud computing capacity to keep costs low, even as they maintain privacy and guaranteed performance. Even more importantly, the most sophisticated NaaS providers use novel metadata-based routing techniques and maintain end-to-end encryption. These providers have no visibility into enterprise traffic; all encryption/decryption happens only under the business’ direct control.

Criticality in Data Stream Processing and a Few Effective Approaches

With the advancement of stream processing engines like Apache Flink, Spark, etc., we can aggregate and process data streams in real time, as they handle low-latency data ingestion while supporting fault tolerance and data processing at scale. Finally, we can ingest the processed data into streaming databases like Apache Druid, RisingWave, and Apache Pinot for querying and analysis. Additionally, we can integrate visualization tools like Grafana, Superset, etc., for dashboards, graphs, and more. This is the overall high-level data streaming processing life cycle to derive business value and enhance decision-making capabilities from streams of data. Even with its strength and speed, stream processing has drawbacks of its own. A couple of them from a bird's eye view are confirming data consistency, scalability, maintaining fault-tolerance, managing event ordering, etc. Even though we have event/data stream ingestion frameworks like Kafka, processing engines like Spark, Flink, etc, and streaming databases like Druid, RisingWave, etc., we encounter a few other challenges if we drill down more

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