April 01, 2024

The Future Is Now: AI and Risk Management in 2024

Generative AI can write code, generate personalized content, and even compose music. IT and business leaders are incorporating custom LLMs into their workflows to maximize worker productivity and streamline routine procedures. Governments are developing regulations for the use of all forms of AI, with the goal of making it safer for humans to use. Organizations are also writing internal guidelines to clarify and standardize the deployment of AI in their own operations. ... Generic, generative AI isn’t reliable – especially when lives are on the line. AI for risk management is tailored to industry-specific needs, providing accurate, relevant data to allow for expedited decisions and swift actions. It considers historic threats and delivers updates in real time as events unfold. It can also understand linguistic nuances specific to risk management. ... AI-powered risk intelligence must be continuously fed quality data vetted by expert data scientists who specialize in machine learning and risk intelligence. The technology should be monitored and trained by humans to ensure it provides only the most accurate and trustworthy information.

Strengthening Cyber Security

In today’s interconnected digital ecosystem, the ramifications of a cyber breach extend far beyond mere data loss. They can disrupt essential services, compromise national security and erode public trust in Government institutions. Therefore, the imperative to fortify cyber security measures cannot be overstated. Regular security audits serve as a pre-emptive measure to identify vulnerabilities, assess risks and implement corrective actions before they can be exploited by malicious actors. By mandating such audits, the GAD is not only demonstrating foresight but also fostering a culture of vigilance and accountability within Government Departments. Moreover, the emphasis on engaging CERT-in-empanelled agencies underscores the importance of leveraging expertise and best practices in cyber security. ... Equally crucial is the timely implementation of audit findings and recommendations. Too often, audits yield valuable insights that languish in bureaucratic inertia, leaving vulnerabilities unaddressed. This entails establishing clear lines of responsibility, allocating adequate resources, and instituting mechanisms for continuous monitoring and evaluation.

The Complexity and Need to Manage Mental Well-Being in the Security Team

“Security people are often overwhelmed,” comments Thea Mannix, director of research (and a cyberpsychologist) at Praxis Security Labs. Apart from the day to day work, she adds, “They’re expected to be futurologists able to predict the future, and psychologists able to understand the human elements of security – how users may react to social engineering and how they may subvert security controls to make work easier. And they never get any positive feedback; it’s mostly negative because the whole process of security is mostly negative – stop the outside bad guys doing anything bad, and stop the inside good guys doing anything wrong.” But there’s also a disturbing edge to this ‘human’ side of cybersecurity. Security teams sometimes work with SBIs and the FBI on criminal investigations. Tim Morris, chief security advisor at Tanium, knows what can be involved because he and his team have done this. “We do cybersecurity to protect data and people. And the only reason we must do this is because there’s an evil side of humanity.?

How to Become a Cyber Security Analyst? A Step By Step Guide

If you’re contemplating a career in cybersecurity, you’re positioned advantageously. The field is experiencing a robust surge and is poised for continued expansion in the coming years. ... The core responsibility of a cyber security analyst revolves around safeguarding a company’s network and systems against cyber attacks. This entails various tasks such as researching emerging IT trends, devising contingency plans, monitoring for suspicious activities, promptly reporting security breaches, and educating the organization’s staff on security protocols. Additionally, cyber security analysts play a pivotal role in implementing threat protection measures and security controls. They may conduct simulated security attacks to identify potential vulnerabilities within the organization’s infrastructure. Given the ever-evolving tactics and tools employed by hackers, cyber security analysts must remain abreast of the latest developments in digital threats. Staying informed about emerging cyber threats ensures that analysts can effectively anticipate and counteract evolving security risks.

Viewpoint: AI Is Changing the Cyber Risk Landscape

Any time an organization adopts new technology, that organization inherently opens itself up to risk by introducing a new set of unknowns to their business practices. Allowing the wrong users access to a program, for example, or flaws in the program’s code, are technological issues that can create security vulnerabilities which need to be addressed by IT and cybersecurity professionals. The practice of hacking – where cybercriminals use code to break through an organization’s cybersecurity systems – is increasingly difficult, but the sudden ubiquity of AI offers a new way to create vulnerabilities by targeting system users with lifelike dupes. Emails that look genuine but are designed to extract important security credentials are not a new phenomenon, but generative AI has allowed new, sophisticated forms of phishing attacks to proliferate on an unprecedented scale. Deepfakes are a convincing new form of cyberattack where criminals develop highly convincing visual and auditory assets to impersonate others.?

Achieving Data Excellence: How Generative AI Revolutionizes Data Integration

Data integration combines data from various sources for a unified view. Artificial intelligence (AI) improves integration by automating tasks, boosting accuracy, and managing diverse data volumes. Here are the top four data integration strategies/patterns using AI: Automated data matching and merging – AI algorithms, such as ML and natural language processing (NLP), can match and automatically merge data from disparate sources. Real-time data integration – AI technologies, such as stream processing and event-driven architectures, can facilitate real-time data integration by continuously ingesting, processing, and integrating data as it becomes available. Schema mapping and transformation – AI-driven tools can automate the process of mapping and transforming data schemas from different formats or structures. This includes converting data between relational databases, NoSQL databases, and other data formats — plus handling schema evolution over time.?Knowledge graphs and graph-based integration – AI can build and query knowledge graphs representing relationships between entities and concepts.?

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