Daily Tech Digest - November 09, 2022

5 ways to use predictive insights to get the most from your data

With the proliferation of SaaS tools, we seem to be collecting so much more data, yet most companies still struggle to integrate it properly to extract insights that would be indicative of future performance. There are a variety of reasons for that: internal data privacy, legacy mindset around who owns what data, lags in data warehousing strategy or operational know-how about the mechanics of integrating it. ... The CMO Survey found that after a decade of integrating customer data across channels, marketers are still struggling, with most giving their organization a 3.5 out of 7 score on the effectiveness of their customer information integration across purchasing, communication and social media channels. ... Too often organizations are overly focused on dashboards and analyzing past trends to determine future actions. Dashboards and reports are often thought of as the final deliverables of data, but this thinking is limiting data’s value. Think about how your acquisition, monetization and retention journeys are orchestrated today, then feed predictive scoring data right into those business systems and tools. 

Coming Clean: Why Cybersecurity Transparency Is A Strength, Not A Weakness

In the wake of the new disclosure proposals, the management of cybersecurity events can no longer be an afterthought in maintaining operating standards. It’s now been elevated to a major concern along with financial risks, such as capital and credit risk. Despite the technical challenges, compliance is generally straightforward. Organizations must develop discipline in how they detect and defend against cyber threats. In addition, they must improve the way they report on them. If they don’t want their next cyber incident to turn into a material event, they need to minimize the risk of a breach in the first place. Remember, the opposite of due diligence is negligence. One way to get started is to focus on the application layer, as that’s where the “money” is. Decades of focus on network-based threats have improved the protection from some cyberattacks, but many business applications remain vulnerable. Applications suffer numerous vulnerabilities outlined by the OWASP Top 10. These are known, common threats that can be countered by using Web application firewalls.

AI eye checks can predict heart disease risk in less than minute, finds study

“This AI tool could let someone know in 60 seconds or less their level of risk,” the lead author of the study, Prof Alicja Rudnicka, told the Guardian. If someone learned their risk was higher than expected, they could be prescribed statins or offered another intervention, she said. Speaking from a health conference in Copenhagen, Rudnicka, a professor of statistical epidemiology at St George’s, University of London, added: “It could end up improving cardiovascular health and save lives.” Circulatory diseases, including cardiovascular disease, coronary heart disease, heart failure and stroke, are major causes of ill health and death worldwide. Cardiovascular disease alone is the most common cause of death globally. It accounts for one in four deaths in the UK alone. While several tests to predict risk exist, they are not always able to accurately identify those who will go on to develop or die of heart disease. Researchers developed a fully automated AI-enabled tool, Quartz, to assess the potential of retinal vasculature imaging – plus known risk factors – to predict vascular health and death.

Mobile Application Security Best Practices

Strong credentials are a must for both web and mobile application development. For mobile apps, you can choose to either have a native login flow, which means the user enters their credentials within the app, or a web-based login flow, where they are directed to a web browser to login. Native login flows provide a better user experience but are generally thought to be less secure. Hypermedia authentication APIs are a solution now popping up to bridge this gap and provide the best of both worlds. Hypermedia authentication APIs interact with the authorization server directly without the need for an intermediary like the browser window. Regardless of how the user enters their credentials, your app should enforce some type of password policy to ensure a strong password is used, and it should not store the access and refresh tokens anywhere except secure storage (like the iOS keychain or Android Keystore). ... Finally, your mobile app should follow best practices for secure coding, just as you would with web applications. Security should be incorporated from the start of the app’s design, with testing occurring throughout the development process.

Cybersecurity threats: what awaits us in 2023?

Businesses will still be mostly concerned with ransomware. The conflict between Russia and Ukraine has marked an end to any possible law enforcement cooperation in the foreseeable future. We can therefore expect that cybercrime groups from either block will feel safe to attack companies from the opposing side. Some may even perceive this as their patriotic duty. The economic downturn will lead more people to poverty, which always translates to increased criminality, and we know ransomware to be extremely profitable. ... Zero trust will take on greater prominence with the continued role of the remote and hybrid workplace. Remote work will continue driving the need for zero trust since hybrid work is now the new normal. With the federal government mandating agencies to adopt zero-trust network policies and design, we expect this to become more common and the private sector to follow suit as 2023 becomes the year of verifying everything. ... In 2023, we might see a slight decline in the raw number of ransomware attacks, reflecting the slowdown of the cryptocurrency markets. 

Google and Renault are creating a 'software-defined vehicle'

Renault will leverage Google's Cloud technology to securely manage data capture and analytics. They'll also use Google's ML and AI capabilities. "Our collaboration with Renault Group has improved comfort, safety, and connectivity on the road," Sundar Pichai, CEO of Google and Alphabet, said in a statement. "Today's announcement will help accelerate Renault Group's digital transformation by bringing together our expertise in the cloud, AI, and Android to provide for a secure, highly-personalized experience that meets customers' evolving expectations." Google shares that some features of the SDV will include predictive maintenance, accurate real-time detection of vehicle failures, a better driving experience, and insurance models reflective of driving behaviors. "Equipped with a shared IT platform, continuous over-the-air updates, and streamlined access to car data, the SDV approach developed in partnership with Google will transform our vehicles to help serve future customers' needs," said Luca de Meo

Why automating finance is just an integration game

What is clear is the increasing demand for decision intelligence with financial analytics at its heart. RPA suppliers are increasingly repositioning themselves as automated intelligence companies, using RPA tools to drive key functions, such as finance. Gartner believes a third of large organisations will be using decision intelligence for structured decision-making to improve competitive advantage in the next two years. Recent research by enterprise application integration firm Jitterbit backs this up. Focusing on mid-sized companies (referred to as Mittelstand) in the DACH region (comprising Germany, Austria and Switzerland), Jitterbit found that 73% of these businesses want to be hyperautomated within three years because “the health of their company depends on it”. The barriers to achieving this are typical – too many manual data process, isolated data silos and a lack of departmental integration. What is becoming clear is that financial analytics can be the core and the catalyst of intelligent automation transformations. 

Detecting Cyber Risks Before They Lead to Downtime

To avoid costly downtime, threats to operational continuity must be detected and investigated as early as possible. That can be accomplished by scanning connected devices for configuration changes and vulnerabilities. However, unlike traditional IT, OT assets cannot be continuously scanned in the same manner and many risks will remain unnoticed. Instead, a system designed for manufacturing environments must have the ability to passively monitor the network infrastructure to locate assets and detect behavior changes and anomalies. That requires understanding dozens of industrial protocols and continuously monitoring the communications and checking against a database of OT/ICS-specific Indicators of Compromise (IOCs, or evidence of a breach) and CVEs. The bane of many monitoring systems is they produce a flood of information about potential harm, not all of it urgent. To be useful, critical alerts must be prioritized based on operational or cybersecurity risk so the right team can respond. For example, OT engineers need to quickly spot undesired process values, incorrect measurements or when a critical device fails so they can resolve issues more quickly.

Challenges to Successful AI Implementation in Healthcare

Incorporating AI systems could improve healthcare efficiency without compromising quality, and this way, patients could receive better and more personalized care. Investigations, assessments, and treatments can be simplified and improved by using AI systems that are smart and efficient. However, implementing AI in healthcare is challenging because it needs to be user-friendly and procure value for patients and healthcare professionals. AI systems are expected to be easy to use and user-friendly, self-instructing, and not require extensive prior knowledge or training. Besides being simple to use, AI systems should also be time-saving and never demand different digital operative systems to function. ... The healthcare experts noted that implementing AI systems in the county council will be difficult due to the healthcare system’s internal capacity for strategic change management. For the promotion of capabilities to work with implementation strategies of AI systems at the regional level, experts highlighted the need for infrastructure and joint ventures with familiar structures and processes. 

AI Ethics: Four Essentials CIOs Must Know

Enterprises must investigate how the data used to train the algorithm is used in order to develop explainable AI. Although this won’t address the bias issue, it will guarantee that firms are aware of the underlying causes of any problems so they can take appropriate action. Synthetic data, in addition to actual data sets, is essential for addressing ethical issues. For instance, synthetic data can be used to correct biases in real data that are unjust and skewed toward particular groups of individuals. Additionally, synthetic data can be used to boost the volume and produce an objective dataset if the volume is inadequate. ... Executives must design AI systems that can instantly identify fabricated data and immoral behavior. This necessitates screening suppliers and partners for the improper use of AI in addition to examining a company’s own AI. Examples include the employment of convincing false text and videos to discredit competitors or the use of AI to carry out sophisticated cyber-attacks. As AI technologies become more accessible, this problem will worsen.

Quote for the day:

"Good leaders make people feel that they're at the very heart of things, not at the periphery." -- Warren G. Bennis

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