November 01, 2024
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
When facing difficulties in both their professional and personal lives, people can start to withdraw and be less interested in contributing, even doing the bare minimum. They might also make mistakes more often or miss deadlines, or they can care less about how their colleagues or managers perceive their work. Body language can also provide insight into an employee’s emotional state and engagement level. When assigning tasks, Michelle Duval, founder and CEO at Marlee, a collaboration and performance AI for the workplace, looks her colleagues in the eyes. “Avoiding eye contact or visible sighing… are helpful clues,” she says. ... When it comes to helping employees improve their performance, the key point is to understand why they have problems in the first place and act quickly. “The best coaching depends on what type of problem you’re fixing,” says Caroline Ceniza-Levine, executive recruiter and career coach. “If the employee’s work product is suffering, they may need more direction or skills training. If the employee is disengaged, they may need help getting motivated – in this case, giving them more information around why their work matters and how important their contribution is may help.”
AI’s ability to synthesise vast amounts of data allows organisations to connect data from previously disparate sources, and then analyse it to detect historical patterns and deliver forward-looking insights. In the banking industry, this is happening at both a high level through traditional data analysis, and, increasingly, through more advanced AI tools including Natural Language Processing (NLP) and Machine Learning (ML). As organisations continue gathering these predictive analytics, many are also in the process of providing feedback to their AI systems which will ultimately improve their predictive accuracy over time. The main use case in which banks are currently seeing the biggest impact from AI-powered predictive insights is in forecasting consumer behaviour. ... AI-powered fraud detection algorithms can analyse vast amounts of transaction data in real-time at a scale that’s unattainable by humans. The real-time nature of these systems also allows organisations to prevent loss by intercepting anomalous transactions before they’re settled. This scalable, automatic approach also makes it easier for financial organisations to stay in compliance with relevant anti-money laundering (AML) and anti-terrorist financing regulations and avoid steep penalties.
The federal government is heightening its warnings about dangerous software development practices, with the U.S. Cybersecurity and Infrastructure Security Agency (CISA) and the Federal Bureau of Investigation (FBI) issuing stark warnings about basic security failures that continue to plague critical infrastructure. ... The report also states that the memory safety roadmap should outline the manufacturer’s prioritized approach to eliminating memory safety vulnerabilities in priority code components. “Manufacturers should demonstrate that the memory safety roadmap will lead to a significant, prioritized reduction of memory safety vulnerabilities in the manufacturer’s products and demonstrate they are making a reasonable effort to follow the memory safety roadmap,” the report said. “There are two good reasons why businesses continue to maintain COBOL and Fortran code at scale. Cost and risk,” Shimmin told The New Stack. “It’s simply not financially possible to port millions of lines of code, nor is it a risk any responsible organization would take.” ... Finally, it is good that CISA is recommending that companies with critical software in their care should create a stated plan of attack by early 2026, Shimmin said.
领英推荐
Threat hunting, on the contrary, is a proactive approach. It means that cyber teams go out into the wild and proactively identify potential risks and threat patterns, isolating them before they can cause any harm. A threat-hunting team requires specific knowledge and skills. Therefore, it usually consists of various professionals, such as threat analysts, who analyze available data to understand and predict the attacker's behavior; incident responders, who are ready to reduce the impact of a security incident; and cybersecurity engineers, responsible for building a secure network solution capable of protecting the network from advanced threats. These teams are trained to understand their company's IT environment, gather and analyze relevant data, and identify potential threats. Moreover, they have a clear risk escalation and communication process, which helps effectively react to threats and mitigate risks. Specialists often use a combination of tools that help in threat hunting. ... Endpoint detection and response (EDR) systems combine continuous real-time monitoring and collection of end-point data with a rule-based automated response.
Using IoT sensing technology can provide early warning of disaster events and keep an eye on equipment if human access to facilities is cut off. Sensors and cameras can be helpful in determining when it may be appropriate to switch operations to other facilities or back up servers. Moisture sensors, for example, can detect whether floods may be on the verge of impacting device performance. ... In disaster-prone regions, it is advisable to proactively facilitate relationships with government authorities and emergency response agencies. This can be helpful both in ensuring continued compliance and assistance in the event of a natural disaster. “There are certain aspects of [disaster response] that need to be captured,” Miller says. “A lot of times in crisis mode, that becomes a secondary focus. But [disaster management] systems allow the tracking and the recording of that information.” Being aware of deadlines for compliance reporting and being in contact with regulators if they might be missed can save money on potential fines and penalties. And notifying emergency response agencies may result in prioritization of assistance given the economic imperatives of IT continuity.
Traditional "extract, transform, load" and "extract, load, transform" data pipelines have historically been the primary method for moving data into analytics. But analytics consumers have often had limited control or influence over the source data model, which is typically defined by application developers in the operational domain. Data is also often stale and outdated by the time it arrives for processing. "By shifting data processing and governance, organizations can eliminate redundant pipelines, reduce the risk and impact of bad data at its source, and leverage high-quality, continuously up-to-date data assets for both operational and analytical purposes," LaForest said. Real-time data streaming is especially crucial in sectors such as finance, e-commerce and logistics, where even a few seconds of delay can negatively impact customer satisfaction and profitability. ... Real-time data streaming is emerging as the foundation for the next wave of AI innovation. For predictive AI and pattern recognition, data needs to be available in real time to drive accurate, immediate insights. Real-time data pipelines are essential for enabling AI systems to deliver smarter, faster insights and drive more accurate decision-making across the enterprise.