November 27, 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
One clear solution to the problem of technology oversimplification is to tailor AI training and educational initiatives towards diverse endpoints. Research clearly demonstrates that know-how of the underlying functions of security professions has a real mediating effect on the excesses of encountering disruptive, unfamiliar conditions. The mediation of this effect by the oversimplification mentality, unfortunately, suggests that more is required. Specifically, discussion of the foundational functionality of AI systems needs to be married to as many diverse outcomes as possible to emphasize the dynamism of the technology. ... Naturally, one of the value propositions of studies like the one presented here is the ability for professionals to see the world as another kind of professional might. Whilst tabletop exercises are already a core tool of the cybersecurity profession, there are opportunities to incorporate comparative applications’ learning for AI using simple simulations. ... Finally, wherever possible, role rotation is of clear advantage to overcoming the issues illustrated herein. In testing, the diversity of career roles over and above career length played a similar role in mitigating the excesses of the impact of novel conditions on response priorities.
Building resilient project plans that can handle unforeseen, yet often inevitable changes, is key to ensuring timeline accuracy. "Understanding dependencies, identifying bottlenecks, and planning delivery around these constraints have shown to be important for timeline accuracy," Chandrasekar says. Project accuracy also depends on clear communication and tracking. "It's critical to consistently review timelines with your project team and stakeholders, making updates as new information is discovered," Naqib says. He adds that project timelines should be tracked with the support of a work management tool, such as SmartSheet or Jira, in order to measure progress and identify gaps. Yet even with perfect planning, unanticipated delays or changes may occur. Proper planning and communication are key to assuring timeline accuracy, says Anne Gee, director of delivery excellence for IT managed services at data and technology consulting firm Resultant. ... The best way to get a lagging timeline back on schedule is to work with your project team to identify the root cause, Naqib advises. "Then, you can work with your team and your greater organization to explore possible resolution accelerators that will keep your timeline on track."
AI benchmarking serves as a foundational tool for evaluating and advancing artificial intelligence systems. Its primary objectives address critical aspects of AI development, ensuring that models are efficient, effective, and aligned with real-world needs. ... Benchmarks provide valuable insights into a model’s limitations, serving as a roadmap for enhancement. For instance: Identifying Bottlenecks: If a model struggles with inference speed or accuracy on specific data types, benchmarks highlight these areas for targeted optimization. Algorithm Development: Benchmarks inspire innovation by exposing gaps in performance, encouraging the development of new algorithms or architectural designs. Data Quality Assessment: Poor performance on benchmarks may indicate issues with training data, prompting better preprocessing, augmentation, or dataset refinement techniques. ... AI benchmarking involves a systematic process to evaluate the performance of AI models using rigorous methodologies. These methodologies ensure that assessments are fair, consistent, and meaningful, enabling stakeholders to make informed decisions about model performance and applicability.
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“The value of data has skyrocketed in recent years, transforming it into one of the most sought-after commodities in the digital age. The rise of AI and machine learning has only amplified the threat to data, as attackers can now automate their efforts and create more sophisticated and targeted campaigns.” Saceanu noted that Irish organisations, like those globally, are struggling to secure their systems and private information, with industries that typically hold sensitive data, such as those in healthcare, finance and education, being particularly vulnerable. “We have seen a massive focus on targeting organisations that operate in critical infrastructure for various motivations – financially oriented or to disrupt operations. This means that there are more and more ransomware attacks on manufacturing, energy and healthcare that are not only encrypting data, but also exfiltrating this data to ask for enormous ransom payments because they know that these organisations cannot afford any disruption.” For Saceanu, this shift to an environment driven by data and under near constant threat has led organisations to experiment with advanced technologies such as AI in order to improve efficiency and spearhead innovation
When it comes to identity security, security teams should regularly monitor, identify, analyze, and report risks in their environment. If exploited, these risks can be detrimental to an organization, its assets, and stakeholders. They can also undercut ethical standards of privacy and data protection. Running risk assessments is especially important when there is a lack of visibility in company processes and security gaps. Organizations can systematically assess their security measures surrounding user identity data and ensure compliance with privacy policies and regulatory standards. ... Transparency is among the most vital aspects of ethical identity verification. It requires organizations to be upfront about how they practice data collection and management, and how the data is used. This has to be reflected in the company policies, culture, and of course, its technology, including data storage and access. Users, i.e., customers from whom data is collected, should be able to access the policy terms easily at any point. ... When companies are looking to procure ethical technology, it’s important to account for factors like privacy, accessibility, security, and regulations. The above factors look at the perspective of the company using the tech and how they should operate it.?
The rapid evolution of AI brings forth several new potential opportunities and challenges. Today, AI drives the business growth of an enterprise in more ways than one. Artificial intelligence for IT Operations or AIOps is a new concept that encompasses big data, data mining, machine learning (ML) and AI. AIOps is a practice that blends AI with IT operations to improve operational processes. AIOps platforms automate, optimize and improve IT operations and provide users with real-time visibility and predictive alerts to minimize operational issues and proactively resolve issues that may have arisen to ensure ideal IT operations. ... Adopting AIOps helps DevOps through automation, predictive intelligence and better data-driven decisions. This collaboration fosters efficient processes, improved quality and continuous improvement to meet the ever-changing demands of the industry and customer requirements. ... AI makes it easier for DevOps teams to find patterns in data, make meaning from such data and form informed decisions on which resources and processes to allocate. The convergence of AIOps and DevOps processes can yield valuable insights that can help improve decision-making.