Daily Tech Digest - July 27, 2023

'FraudGPT' Malicious Chatbot Now for Sale on Dark Web

Both WormGPT and FraudGPT can help attackers use AI to their advantage when crafting phishing campaigns, generating messages aimed at pressuring victims into falling for business email compromise (BEC), and other email-based scams, for starters. FraudGPT also can help threat actors do a slew of other bad things, such as: writing malicious code; creating undetectable malware; finding non-VBV bins; creating phishing pages; building hacking tools; finding hacking groups, sites, and markets; writing scam pages and letters; finding leaks and vulnerabilities; and learning to code or hack. Even so, it does appear that helping attackers create convincing phishing campaigns is still one of the main use cases for a tool like FraudGPT, according to Netenrich. ... As phishing remains one of the primary ways that cyberattackers gain initial entry onto an enterprise system to conduct further malicious activity, it's essential to implement conventional security protections against it. These defenses can still detect AI-enabled phishing, and, more importantly, subsequent actions by the threat actor.

Key factors for effective security automation

A few factors generally drive the willingness to automate security. One factor is if the risk of not automating exceeds the risk of an automation going wrong: If you conduct business in a high-risk environment, the potential for damage when not automating can be higher than the risk of triggering an automated response based on a false positive. Financial fraud is a good example, where banks routinely automatically block transactions they find to be suspicious, because a manual process would be too slow. Another factor is when the damage potential of an automation going wrong is low. For example, there is no potential damage when trying to fetch a non-existent file from a remote system for forensic analysis. But what really matters most is how reliable automation is. For example, many threats actors today use living-off-the-land techniques, such as using common and benign system utilities like PowerShell. From a detection perspective, there are no uniquely identifiable characteristics like a file hash, or a malicious binary to inspect in a sandbox. 

API-First Development: Architecting Applications with Intention

More traditionally, tech companies often started with a particular user experience in mind when setting out to develop a product. The API was then developed in a more or less reactive way to transfer all the necessary data required to power that experience. While this approach gets you out the door fast, it isn’t very long before you probably need to go back inside and rethink things. Without an API-first approach, you feel like you’re moving really fast, but it’s possible that you’re just running from the front door to your driveway and back again without even starting the car. API-first development flips this paradigm by treating the API as the foundation for the entire software system. Let’s face it, you are probably going to want to power more than one developer, maybe even several different teams, all possibly even working on multiple applications, and maybe there will even be an unknown number of third-party developers. Under these fast-paced and highly distributed conditions, your API cannot be an afterthought.

What We Can Learn from Australia’s 2023-2030 Cybersecurity Strategy

One of the challenges facing enterprises in Australia today is a lack of clarity in terms of cybersecurity obligations, both from an operational perspective and as organizational directors. Though there are a range of implicit cybersecurity obligations designated to Australian enterprises and nongovernment entities, it is the need of the hour to have more explicitly stated obligations to increase national cyberresilience.There are also opportunities to simplify and streamline existing regulatory frameworks to ensure easy adoption of those frameworks and cybersecurity obligations. ... Another important aspect of the upcoming Australian Cybersecurity Strategy is to strengthen international cyberleaders to enable them to seize opportunities and address challenges presented by the shifting cyberenvironment. To keep up with new and emerging technologies, this cybersecurity strategy aims to take tangible steps to shape global thinking about cybersecurity.

Is your data center ready for generative AI?

Generative AI applications create significant demand for computing power in two phases: training the large language models (LLMs) that form the core of generate AI systems, and then operating the application with these trained LLMs, says Raul Martynek, CEO of data center operator DataBank. “Training the LLMs requires dense computing in the form of neural networks, where billions of language or image examples are fed into a system of neural networks and repeatedly refined until the system ‘recognizes’ them as well as a human being would,” Martynek says. Neural networks require tremendously dense high-performance computing (HPC) clusters of GPU processors running continuously for months, or even years at a time, Martynek says. “They are more efficiently run on dedicated infrastructure that can be located close to the proprietary data sets used for training,” he says. The second phase is the “inference process” or the use of these applications to actually make inquiries and return data results.

Siloed data: the mountain of lost potential

Given AI’s growing capabilities for handling customer service are only made possible through data, the risk of not breaking down internal data siloes is sizeable, not just in terms of missing opportunities. Companies could also see a decline in the speed and quality of their customer service as contact centre agents need to spend longer navigating multiple platforms and dashboards to find the information needed to help answer customers’ queries. Eliminating data siloes requires educating everyone in the business to understand the necessity of sharing data through an open culture and encouraging the data sides of operations to co-ordinate efforts, align visions and achieve goals. The synchronisation of business operations with customer experience, alongside adopting a data-driven approach, can produce significant benefits such as increased customer spending. ... Data, working for and with AI, must be placed at the centre of the business model. This means getting board buy-in to establish a data factory run by qualified data engineers and analysts who are capable of driving the collection and use of data within the organisation.

An Overview of Data Governance Frameworks

Data governance frameworks are built on four key pillars that ensure the effective management and use of data across an organization. These pillars ensure data is accurate, can be effectively combined from different sources, is protected and used in compliance with laws and regulations, and is stored and managed in a way that meets the needs of the organization. ... Furthermore, a lack of governance can lead to confusion and duplication of effort, as different departments or individual users try to manage data with their own methods. A well-designed data governance framework ensures all users understand the rules for managing data and that there is a clear process for making changes or additions to the data. It unifies teams, improving communication between different teams and allowing different departments to share best practices. In addition, a data governance framework ensures compliance with laws and regulations. From HIPAA to GDPR, there are a multitude of data privacy laws and regulations all over the world. Running afoul of these legal provisions is expensive in terms of fines and settlement costs and can damage an organization’s reputation.

Governance — the unsung hero of ESG

What's interesting is that for the most part, they're all at different stages of transformation and managing the risks of transformation. A board has four responsibilities, observing performance, approving, and providing resources to fund the strategy, hiring and developing the succession plan, and risk management. Depending on where you are in a normal cycle of a business or the market, the board is involved in these 4. Also, I take lessons that I've learned at other boards and apply them possibly to Baker Hughes' situation and vice versa: take some of the lessons that I'm learning and the things that I'm hearing in the Baker Hughes situation — unattributed, of course — and bring it into other boards. Sometimes there's a nice element of sharing. As you know, Baker Hughes has a very strong Board and I am a good student at taking down good and thoughtful questions from board members and bringing that to other company boards, if appropriate.

Why whistleblowers in cybersecurity are important and need support

“Governments should have a whistleblower program with clear instructions on how to disclose information, then offer the resources to enable procedures to encourage employees to come forward and guarantee a safe reporting environment,” she says. Secondly, nations need to upgrade their legislation to include strong anti-retaliation protection against tech workers, making it unlawful for various entities to engage in reprisal. This includes job-related pressure, harassment, doxing, blacklisting, and retaliatory investigations. ... To further increase chances, employees can be offered regular training sessions in which they are informed about the importance of coming forward on cybersecurity issues, the ways to report wrongdoing, and the protection mechanisms they could access. Moreover, leadership should explain that it has zero tolerance for retaliation. “Swift action should be taken if any instances of retaliation come to light,” according to Empower Oversight. The message leadership should convey is that issues are taken seriously and that C-level executives are open for conversation if the situation requires such an action.

Cloud Optimization: Practical Steps to Lower Your Bills

Optimization is always an iterative process, requiring continual adjustment as time goes on. However, there are many quick wins and strategies that you can implement today to refine your cloud footprint:Unused virtual machines (VMs), storage and bandwidth can lead to unnecessary expenses. Conducting periodic evaluations of your cloud usage and identifying such underutilized resources can effectively minimize costs. Check your cloud console now. You might just find a couple of VMs sitting there idle, accidentally left behind after the work was done. Temporary backup resources, such as VMs and storage, are frequently used for storing data and application backups. Automate the deletion process of these temporary backup resources to save money. Selecting the appropriate tier entails choosing the cloud resource that aligns best with your requirements. For instance, if you anticipate a high volume of traffic and demand, opting for a high-end VM would be suitable. Conversely, for smaller projects, a lower-end VM might suffice. 

Quote for the day:

"Your job gives you authority. Your behavior gives you respect." -- Irwin Federman

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