In an increasingly digitally connected world, enterprise IT systems are more vulnerable than ever to security threats like viruses, application-specific hacks, phishing attacks, etc.

In an increasingly digitally connected world, enterprise IT systems are more vulnerable than ever to security threats like viruses, application-specific hacks, phishing attacks, etc. With the prolific use of the Internet and other interactive mobile technologies, many small to medium-sized organizations are facing gargantuan challenges of addressing security threats to their IT systems.
Cybercriminals are discreetly finding new ways to invade the most sensitive networks. Safeguarding business data is a huge challenge for businesses today. Here are some of the top threats to information systems:

• Phishing
• Virus back doors
• Blended attacks
• Spyware
• Spam
• Privilege Escalation
• Key loggers
• Adware
• Trojans
• Worms

How the Hacker Ecosystem is Blooming?
As AI advanced in terms of self-driving cars, big data, etc., in 2016, so did the number of botnets, ransomware, malware attacks and cyber criminals across the world [Source: Malware Byte’s State of Malware report]. Similarly, the year 2017 witnessed many destructive cyber attacks and hacks such as myriad of WordPress sites hacked, spread of malware through cell phone towers, and many other such instances.

It’s important to cite here one of the largest and most intrusive breaches in history – the big cyberattack on the Equifax credit reporting agency that took place last year. This incident led to the theft of Social Security numbers, birth dates, addresses and other data of about 143 million U.S. customers. The hackers found a soft target in this Atlanta-based consumer credit reporting agency and leveraged the vulnerability of this US website application to access certain files including dispute documents. One of the three major consumer credit reporting agencies in the U.S. – Equifax – is home to data of over 820 million customer and more than 91 million businesses across the world.

As per the latest WEF Global Risks Report, cyber attacks are one of the biggest threats to the world in 2018. This clearly implies that organizations need to address this issue and secure their systems so as to combat these likely-to-happen cyber attacks. It’s also predicted by many experts that the coordinated “botnets” will become more prevalent, especially with unsecured IoT devices.

ML and AI Transforming the Cyber Security Industry
We are at a turning point in this information age. Machines seemingly have far superior computational abilities than human beings today since those can sift through massive piles of data and leverage it to make better informed decisions.

Many cyber security experts see similar value in the predictive technologies such as machine learning
applications and AI. According to ESG research, 12% of enterprise organizations have already deployed
AI-based security analytics extensively, and 27% have deployed AI-based security analytics on a limited basis.

The important question that arises here is that why is AI and machine learning being perceived as the future of cyber security. This increasing technology adoption is being driven since this:

• Accelerates incident detection
• Triggers incident responses
• Enables organizations to better detect and communicate risks to the business
• Sifts through multiple software vulnerabilities, configuration errors, and threat intelligence
• Helps enterprises gain an improved understanding of cybersecurity situational awareness

Use Cases of AI and ML in Security Today
ML solutions supplement existing technologies with incremental analytics for driving enhanced efficiency and value, and act as helper applications. Some AI-based solutions function as standalone solutions as well as operate in synergy with different other technologies of security operations and analytics platform architecture (SOAPA).

According to ABI Research estimates, machine learning in cyber security will increase spending in big data, AI and analytics to $96 billion by 2021. Enterprise security vendors are plugging-in machine learning into new and existing products to improve malware detection. Google has already been leveraging machine learning to identify and assess potential threats against mobile endpoints on Android. The technology giant is also using machine learning to eliminate malware from the already infected handsets.

In 2017, Amazon Web Services (AWS) launched data security service – Macie – with machine learning, to identify, sort and safeguard sensitive data across the cloud service. The idea behind this cloud infrastructure giant using machine learning is to protect and analyze the increasing amount of sensitive data using a picture of historical patterns of positive and negative behaviors, as it grows within an organization.

Predictive technologies such as AI and machine learning are notably useful in filtering the sensitive data on high priority security incidents. The online payment platform – PayPal – uses machine learning algorithms to battle fraud. By leveraging deep learning techniques, PayPal analyzes humongous customer data and assesses risk accordingly. AI is a powerful technology stack that enables enterprises identify and eliminate bottlenecks in their cyber security roadmap to combat ever-increasing cyber attacks in this digital age. California-based Cylance is the first company to leverage AI and machine learning to cyber security for understanding the root cause of attacks and their prevention.

Drivers and Benefits of AI and ML Adoption
Cyber security organizations are increasingly adopting AI and ML tools to ward off the rising number of attacks on networks. As the world turns toward the proliferate use of IoT and connected devices, overlooking AI cannot be afforded by cyber security companies since this predictive technology helps them sift through and analyze enormous amounts of data for preventing networks from getting infiltrated.

Another huge benefit of adopting AI and ML is that it can be used to study files or their behavior and detect potential threats such as anti-malware, dynamic risk analysis and anomaly detection.

Can these key drivers of AI and ML adoption make people’s jobs in cyber security redundant? The answer is a plain “No”. Since humans only design malwares and hacks to bypass automated security solutions, enterprises would need humans, who can think fresh and find out innovative ways to defend networks from hackers and cyber criminals.

Thinking about the Future
Let’s take a look at an interesting angle. According to the Verizon Data Breach Report, over 70% of attacks exploit known vulnerabilities that already have available patches. The ever-rising number of cyber attacks and the unpreparedness of many enterprises to tackle those threats have posed a gargantuan risk on the business level. In addition to this, the cyber security workforce is hard pressed to keep pace with the pressure of addressing these potential threats.

According to ‘The Cybersecurity Jobs Report’, by 2021, it is estimated that there will be 3.5 million vacant cyber security positions worldwide. This means that the cyber security professionals would have two options – either to work real hard or work longer hours. With this current state of the cyber security industry, the implementation of machine learning and AI-based solutions into the mix can revolutionize the landscape. These systems can free up significant amount of time for your tech workforce.

 

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Artificial intelligence, the next frontier of automation, will be a business imperative for channel pros who want to become more agile, innovative, and competitive. By Esther Shein

ARTIFICIAL INTELLIGENCE is cutting a wide swath across nearly every industry, and managed services is no different. AI-embedded tools are being touted for their ability to help MSPs automate repetitive functions and boost productivity. For many MSPs, though, AI is still a nascent and abstract concept. Nevertheless, it is a business imperative if they want to become more agile, innovative, and competitive.

While MSPs broadly understand the value of AI, “there is still a good amount of unknown and probably some more hype than reality,” says David Tan, CTO of Syosset, N.Y.-based CrushBank, which developed an AI-based IT help desk application. There needs to be more education on how to harness and leverage the tools, he says.

Some MSPs have dipped their toes in to take advantage of automation. Georg Dauterman, president of New York-based Valiant Technology, is partnering with an MSSP that has built some AI technology “to allow us to offer more powerful security services to our clients.”

The MSSP is helping Valiant build a SOC SIEM and integrate it with the firm’s PSA tools to provide a single view of the network. That way, when alerts come in that the system indicates should be investigated, they can be remediated more quickly, Dauterman says.

Advanced cybersecurity is an area where AI can be very beneficial to MSPs by proactively identifying and neutralizing threats, agrees Rahul Joshi, CTO of CSS Corp, a customer experience and technology services provider with U.S. headquarters in San Jose, Calif.

The emerging category of AIOps uses AI and automation to enhance IT operations. For instance, AIOps can help MSPs with alert and incident correlation by combing through large amounts of data to determine what alerts are false and which ones need attention, Tan says.

And in the case of one CSS customer, “we have achieved a 60% reduction in TCO … [and] 99.999% availability across web-hosting platforms through enhanced observability and controllability” enabled by their AIOps platform, Joshi says.

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Gender biases can affect all aspects of working life for women, including disparities in pay and promotions.

Every year, on March 8th, organizations worldwide burst into a frenzy as they celebrate International Women’s Day. Despite the fervour, a lot of this day’s activity can become mere token activism without actual policy interventions or changes. When I read that this year’s theme was #BreakTheBias, I was compelled to sit back and think about what bias means to me, Gender Bias to be more specific.

My POV – Gender bias at its simplest, is about having prejudice towards one gender over another. This prejudice can result in differences and discrimination in the way employees are treated. Reality is a lot more complex than that, of course. Interestingly and unfortunately, not all bias is conscious and unconscious biases are deeply ingrained. Factors and influences such as our upbringing, experiences, society, and the environment play a role in shaping the choices and decisions we take in life.

And where bias is common, organizations suffer. The impact of bias is so evident even at the level – this creates a gap in having genuinely diverse teams. Also, workplace inclusivity suffers, and no one wins in this situation.

How can we reduce gender biases?

Gender biases can affect all aspects of working life for women, including disparities in pay and promotions. In many instances, women also do not give due credit to their skills and knowledge and underplay which is a bigger problem to solve for. But as I wrote earlier, hiring is often the first place where biases creep in. Take the classic study by InSync that showed even when interviewers were giving identical resumes with only differences in the names (Simon and Susan), Simon received more interviews and was more likely to be hired than Susan.

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As the demand for data science jobs keeps growing, the supply of meritorious candidates is not keeping pace. According to a study we conducted in 2019, around 97,000 data science positions were available to be filled that year alone. It is a travesty then that despite the demand, companies face a lot of challenges when hiring for these positions, and CSS Corp is no different.

However, despite the struggle, the company has managed to attract and retain a significant portion of this talent. Credited largely to its stringent and unique hiring practices, CSS Corp has overcome the challenge of sourcing that small proportion of data scientists dispersed across the nation.

“Being a new-age digital services provider in the B2B space, we pick candidates who have that extra slant for AI, analytics, and automation, instead of the usual coding or testing skills,” says Brijesh Balakrishnan, SVP & Delivery Head – Digital Engineering Services and GIS, at CSS Corp. “In short, we map a career for those who have the inner thrust to learn and grow,” he adds.

So who would be the ideal candidate for a data science position at CSS Corp?

“We look for candidates with a knack for thinking logically and analytically toward solving business problems,” says Balakrishnan. “It’s about fresh thinking, not clichéd answers. An ideal candidate would be one who balances technical forte with soft-skills, right attitude, and personality in a team setting,” he adds.

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As the demand for data science jobs keeps growing, the supply of meritorious candidates is not keeping pace. Market research firm Gartner highlights that just 20 percent of employees currently have the abilities required to succeed in their current and future job roles. In another recent survey, an average of 43 percent of young people said that they were confident that they will need to retrain and have the ability to do so.

As revealed in the Future of Jobs Report 2018 released by the World Economic Forum, ubiquitous high-speed mobile Internet, artificial intelligence, widespread adoption of big data analytics, and cloud technology are only some of the drivers of change that could lead to demand for new skills and create the need for continuous learning for entire workforce, irrespective of their industry or domain.

As uncertain as it could get, the entire world of work has taken a hit with COVID-19 pandemic. Businesses across many sectors have been urged to now operate remotely and quickly adapt to the new ways of working. The role of leaders has become most critical, they have to help the organizations and the entire workforce navigate through this uncertainty looming around the whole ecosystem.

To be able to navigate the complexities of the business ecosystem at present and in the future, as it gets more uncertain, leaders of today and tomorrow have to follow the mantra of ‘Learn, Unlearn & Relearn’.

The need to learn, unlearn & relearn
The metaphor, ‘Change is the only constant’, is as real as it could get in the era of industry 4.0. The onus thus, comes on the business and HR leaders to invest in continuous development of their workforce and ensure that they are always up to date with the in-demand skills.

Anurag Bansal, Senior Director and Business Head, HUGHES Global Education shared, “In recent times, businesses across the globe are increasingly facing complex problems due to rapid changes in technology, disruptions happening through innovation, turbulent market conditions and changing consumer behavior. During such uncertainty, especially, given the complexities COVID-19 pandemic has led to, it is very important to have leaders at the helm who can steer their companies to success in these scenarios.”

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WHILE THERE SEEMS TO BE ADVOCATES OF BOTH FORMAL AND INFORMAL EDUCATION IN THE DATA SCIENCE COMMUNITY, WHAT DOES THE GENERAL HIRING LANDSCAPE SUGGEST?

We may be living unique lives, but there are a few common experiences that tie most of us together. While the instinctive response seems to be what the Covid-19 pandemic has left in its trail, a less grim example would be the chain of events that mark a few decades of our lives – school -> college -> job

While most of these remain unchanged, there has been a growing clamour to rethink education systems as we know it; to one, that both shields learners from the negative effects of new technologies in the workplace, as well as reskills them to prepare for new cross-functional roles that will inevitably be created as a result.

While one way of addressing this talent and labour market issue would be to enrol and improve the quality of education, upskilling in silo would be a more efficient way of achieving this without burdening the formal education system across the world.

Tied to lifelong learning, upskilling through relevant certification is increasingly becoming an accepted norm. But the field of data science is faced with a critical question – can its vast universe be merely acquired through self-study?

Hiring Trends In Data Science

While there seems to be advocates of both formal and informal education in the data science community, what does the general hiring landscape suggest?

While IT services company CSS Corp places an ‘inestimable value’ on a formal academic background, LinkedIn admittedly hires candidates based on skill sets alone, and not necessarily on their educational background.

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