A security recovery plan is designed to stop, learn, and then correct the incident. "A disaster recovery plan may follow similar steps, but nomenclature would not likely use 'detection' to describe a fire or flood event, nor would there be much in the way of analytics," says Peter Fortunato, a manager in the risk and business advisory practice at New England-based accounting firm Baker Newman Noyes."Further, not many disasters require the collection of evidence." Another risk in merging plans is the possibility of gaining unwanted public attention. "For instance, invoking a disaster recovery plan often requires large-scale notifications going out to key stakeholders," Merino says.
Just as happened with Moore’s law when silicon chips met transistors, public cloud and big data are creating exponential effects. A recent research predicts that public cloud prices for big data processing and storage will decrease by half every few years while processing power will double. Public cloud skeptics predict that costs of big data storage in the public cloud will be the same or increase slightly in 10 years. however, this not true as the costs are expected to decrease significantly. On the other hand, the costs of upgrading big data software will increase. in a few years, Hadoop data lake will need to be upgraded. Right now, data scientists are preferring multiple versions of Spark, indicating the beginning of on-premise headaches. This can only get worse as Google, Amazon Web Services (AWS) and Microsoft pursue a serverless strategy.
It's difficult to talk about AI without evaluating its place in the ecosystem. Loosely speaking, it starts with the Internet of Things, in which objects are connected to the internet and used to gather data. Once enough data has been gathered, it passes the arbitrary threshold and becomes "Big Data", which AI is used to interpret. When there are so many data points that no human could ever process them all, artificial intelligence becomes the only real alternative. But AI doesn't always know what it's looking for, which is where machine learning comes in. Loosely speaking, that's the process of using AI to analyze data in such a way that it 'teaches' itself to interpret it. AI disruption, then, is largely going to come in the form of new ways of processing and interpreting data that have never before been available. Here are just five of the industries that AI is set to disrupt.
Ultimately it comes down to the Machine Learning experts, who are the main players behind "educating" the machines. Digital signature, authentication, hiding the IP, masking identity, encryption, firewall, etc have already been implemented by various firms... but what new strategies can machine learning discover? Deep machine learning can apply various algorithms to identify the malicious activities taking place on the network. It can be determined by finding unusual patterns interacting with the system infrastructure. You might have seen Google asking you to verify if you are not a robot. You click on some of the images and then it lets you browse. When you browse Google for long hours on end, this is the common activity that occurs.
While this finding underlines the importance of user education and training, the respondents said human error is exacerbated by understaffed security teams and a flood of alerts and false positives. This highlights the negative impact of companies struggling to recruit cyber security teams in the face of a worldwide shortage of people with information security skills and the need for greater staff support. This shortage of cyber defenders with the right skills is further underlined by the fact 43% of respondents said technology detected the attack, but the security team took no action, while another 41% said a combination of technology and human error was to blame. Respondents also blamed a lack of information resources to understand and mitigate attacks, with 42% saying they are left to figure them out themselves.
Recent conversations with Walker Stemple of Intel’s @intelAI organization got me thinking about where and how organizations can leverage “advanced analytics” to power their business models. Now “advanced analytics” is a broad definition, but I have included the following analytics in that definition: Regression, Clustering, Neural Networks, Machine Learning, Deep Learning, Artificial Intelligence and Cognitive Computing. And while these “classifications” seem to change on a regular basis (sometimes due to us getting smarter; sometimes due to non-value-add marketing hype), it is critical that tomorrow’s business leaders understand where and how to apply these advanced analytics to power their business models.
The National Bank of Ukraine - the country's central bank - declined to share a copy of the letter with Information Security Media Group, but confirmed that it had alerted banks to a new, potentially major attack. "In order to prevent cyber attacks, the National Bank of Ukraine consistently cooperates with banking sector participants, the State Service of Special Communication and Information Protection of Ukraine (SSCIPU), as well as relevant units of the Security Service of Ukraine and the National Police of Ukraine," a spokesman for the National Bank of Ukraine tells ISMG. "On August 11, the NBU promptly informed banks about new malicious code, its characteristics, indicators of compromise and the need to take preventive measures to prevent the networks from being attacked by malicious codes."
RPA is particularly appealing for companies that are juggling with millions or even billions of transactions a day. With such an overwhelming amount to deal with, they often struggle to effectively manage important tasks like addressing customer requests, processing files, moving information between different systems, allocating work and making decisions. But RPA promises to help some organizations alleviate the challenge and operate more efficiently by automating, and thus accelerating, transaction processing. They can then provide greater customer service, which inspires continued loyalty and has a direct, positive impact on a business’ bottom line. ... The promise of RPA is creating massive hype in the market, which is being leveraged by RPA vendors to position their products as the “silver bullet” for any company looking to streamline and optimize operations.
Data mining comes with its share of risks and challenges. As with any technology that involves the use of potentially sensitive or personally identifiable information, security and privacy are among the biggest concerns. At a fundamental level, the data being mined needs to be complete, accurate, and reliable; after all, you’re using it to make significant business decisions and often to interact with the public, regulators, investors, and business partners. Modern forms of data also require new kinds of technologies, such as for bringing together data sets from a variety of distributed computing environments (aka big data integration) and for more complex data, such as images and video, temporal data, and spatial data.
Rust started as a Mozilla research project partly meant to reimplement key components of the Firefox browser. A few key reasons drove that decision: Firefox deserved to make better use of modern, multicore processors; and the sheer ubiquity of web browsers means they need to be safe to use. But those benefits are needed by all software, not just browsers, which is why Rust evolved into a language project from a browser project. Rust accomplishes its safety, speed, and ease of use through the following characteristics: Rust satisfies the need for speed. Rust code compiles to native machine code across multiple platforms. Binaries are self-contained, with no runtime, and the generated code is meant to perform as well as comparable code written in C or C++.
Quote for the day:
"No great manager or leader ever fell from heaven, its learned not inherited." -- Tom Northup