Sections

Machine Learning: The New High-Tech Focus For Cybersecurity

Derek Klobucher

It’s already been a busy year for cybersecurity, as U.S. President Barack Obama warned NATO allies last weekend to closely monitor their impending elections for the kind of suspected Russian hacking that afflicted the latest U.S. presidential election. And last week top U.S. intelligence directors testified about those hacks at a Senate hearing, where topics included ever more sophisticated cyber-attacks and the growing need to fortify American cybersecurity strategy.

Machine learning — along with solid fundamentals — will likely be a key component of reliable cybersecurity in 2017.

“Criminals are fighting a 21st century war, attacking our critical infrastructure and financial systems using unconventional techniques, while we defend ourselves with antiquated methods,” The Hill stated last week. “All the passwords, tokens and other forms of strong authentication are meaningless if a person is tricked into handing over their credentials, inadvertently installs rogue software on their device that performs certain actions, or unwittingly gives a criminal access to their machine or account.”

These cybersecurity fundamentals have certainly become a failure point, and the unrelenting escalation of cyber-attacks has prompted the Federal Reserve, FDIC and others to propose new standards for cyber-risk management, according to The Hill. In the meantime, some organizations are turning to technology for protection.

Machine learning to the rescue

High-tech cyber-defense will place a new emphasis on detecting attacks, as opposed to simply preventing them, according to Nextgov, which tracks how technology and innovation transform government agencies. Machine learning could see a lot wider adoption — and greater success against cyber-attacks — this year.

“It’s clear [that] hackers have refined their art, and are outpacing enterprise security defenses,” Nextgov stated last month. “Machine-learning based solutions … will become more mainstream in 2017, as companies seek to become smarter — and faster to identify and respond to threats.”

Behavioral analytics, for instance, could help organizations use their own data to identify suspicious behavior within automated processes, such as verifying identities and machine-to-machine interactions, according to Nextgov. Based on successful interdictions, machine learning would then improve flexibility and efficiency in managing, investigating and responding to new threats.

Behavioral analytics, for instance, could help organizations use their own data to identify suspicious behavior within automated processes, such as verifying identities and machine-to-machine interactions, according to Nextgov. Based on successful interdictions, machine learning would then improve flexibility and efficiency in managing, investigating and responding to new threats.

But today’s machine learning won’t be enough.

High-tech security solutions can only protect organizations to a point. Likewise, passwords, tokens and other measures only work when users are diligent and savvy.

Machine learning new tricks

To maximize detection efforts, technology must move beyond the common pre-execution machine learning, which only analyzes files before they run, according to the Government Technology Agency of Singapore. In contrast, high-fidelity machine learning analyzes files before and during execution, when malicious code often reveals itself.

“This allows systems to study malicious files in greater detail to better anticipate future threats,” security software provider Trend Micro’s Dhanya Thakkar stated in GovTech last month. “To reduce false positives, high-fidelity machine learning utilizes noise-cancellation techniques … [that] identify known data and applications so that detection technologies can divert precious IT resources into scanning for unknown threats.”

This forward-looking technology has a lot of potential when employed alongside other measures to secure e-mail, mobile and other assets, according to Thakkar. But, as high-fidelity machine learning goes mainstream, cyber-criminals will continue looking for ways to defeat it — and they’re also turning to machine learning.

Up in arms race

“Security is an arms race, and cybercriminals are fine-tuning their methods with the help of machine learning,” McAfee Labs’ 2017 Threats Prediction stated. “It is clear that a considerable amount of research is conducted before the attacks are initiated … [and] we believe that cybercriminals are leveraging machine learning to target victims for BEC and similar scams.”

Business Email Compromise (BEC) “and similar scams” involve social engineering, in which cyber-criminals trick their victims into handing over confidential or private information — or money. These cyber-scams are increasingly sophisticated in order to improve the likelihood of their success; this includes timing attacks to correspond with the mark’s business travel.

Though high-tech cyber-attacks are increasingly sophisticated, basic security measures will still prevent many attacks.

“Tools to perform the complex analysis behind target selection are readily available, and there are a plethora of public sources of data required to build and train malicious machine learning algorithms,” McAfee stated. “Looking to 2017 and beyond, we might even see purveyors of data theft offering ‘Target Acquisition as a Service’ built on machine learning algorithms.”

Back to basics

The fundamentals haven’t changed much.

“If you have anything of value, you have been penetrated,” former CIA and NSA director Michael Hayden said at the SAP Retail Forum 2013. “You’ve got to survive while penetrated — operate while someone else is on your network, wrapping your precious data far more tightly than your other more ordinary data.”

Going back to the basics won’t solve everything. But it can be a big help.

“Most incidents are not the result of a sophisticated, never-before-seen, unpreventable attack,” Data Privacy Monitor stated last month. “[Often] paying better attention to basic security measures would have prevented the issue.”

In short, there’s still no substitute for good cybersecurity fundamentals, “the passwords, tokens and other forms of strong authentication” that The Hill mentioned. So, as we rightly focus on machine learning and other high-tech forms of protection, we must also remember that diligent, savvy people are often still our best line of cyber defense.

This story originally appeared on SAP’s Business Trends. Follow Derek on Twitter: @DKlobucher

Comments

About Derek Klobucher

Derek Klobucher is a Brand Journalist, Content Marketer and Master Digital Storyteller at SAP. His responsibilities include conceiving, developing and conducting global, company-wide employee brand journalism training; managing content, promotion and strategy for social networks and online media; and mentoring SAP employees, contractors and interns to optimize blogging and social media efforts.

Data Analysts And Scientists More Important Than Ever For The Enterprise

Daniel Newman

The business world is now firmly in the age of data. Not that data wasn’t relevant before; it was just nowhere close to the speed and volume that’s available to us today. Businesses are buckling under the deluge of petabytes, exabytes, and zettabytes. Within these bytes lie valuable information on customer behavior, key business insights, and revenue generation. However, all that data is practically useless for businesses without the ability to identify the right data. Plus, if they don’t have the talent and resources to capture the right data, organize it, dissect it, draw actionable insights from it and, finally, deliver those insights in a meaningful way, their data initiatives will fail.

Rise of the CDO

Companies of all sizes can easily find themselves drowning in data generated from websites, landing pages, social streams, emails, text messages, and many other sources. Additionally, there is data in their own repositories. With so much data at their disposal, companies are under mounting pressure to utilize it to generate insights. These insights are critical because they can (and should) drive the overall business strategy and help companies make better business decisions. To leverage the power of data analytics, businesses need more “top-management muscle” specialized in the field of data science. This specialized field has lead to the creation of roles like Chief Data Officer (CDO).

In addition, with more companies undertaking digital transformations, there’s greater impetus for the C-suite to make data-driven decisions. The CDO helps make data-driven decisions and also develops a digital business strategy around those decisions. As data grows at an unstoppable rate, becoming an inseparable part of key business functions, we will see the CDO act as a bridge between other C-suite execs.

Data skills an emerging business necessity

So far, only large enterprises with bigger data mining and management needs maintain in-house solutions. These in-house teams and technologies handle the growing sets of diverse and dispersed data. Others work with third-party service providers to develop and execute their big data strategies.

As the amount of data grows, the need to mine it for insights becomes a key business requirement. For both large and small businesses, data-centric roles will experience endless upward mobility. These roles include data anlysts and scientists. There is going to be a huge opportunity for critical thinkers to turn their analytical skills into rapidly growing roles in the field of data science. In fact, data skills are now a prized qualification for titles like IT project managers and computer systems analysts.

Forbes cited the McKinsey Global Institute’s prediction that by 2018 there could be a massive shortage of data-skilled professionals. This indicates a disruption at the demand-supply level with the needs for data skills at an all-time high. With an increasing number of companies adopting big data strategies, salaries for data jobs are going through the roof. This is turning the position into a highly coveted one.

According to Harvard Professor Gary King, “There is a big data revolution. The big data revolution is that now we can do something with the data.” The big problem is that most enterprises don’t know what to do with data. Data professionals are helping businesses figure that out. So if you’re casting about for where to apply your skills and want to take advantage of one of the best career paths in the job market today, focus on data science.

I’m compensated by University of Phoenix for this blog. As always, all thoughts and opinions are my own.

For more insight on our increasingly connected future, see The $19 Trillion Question: Are You Undervaluing The Internet Of Things?

The post Data Analysts and Scientists More Important Than Ever For the Enterprise appeared first on Millennial CEO.

Comments

About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

When Good Is Good Enough: Guiding Business Users On BI Practices

Ina Felsheim

Image_part2-300x200In Part One of this blog series, I talked about changing your IT culture to better support self-service BI and data discovery. Absolutely essential. However, your work is not done!

Self-service BI and data discovery will drive the number of users using the BI solutions to rapidly expand. Yet all of these more casual users will not be well versed in BI and visualization best practices.

When your user base rapidly expands to more casual users, you need to help educate them on what is important. For example, one IT manager told me that his casual BI users were making visualizations with very difficult-to-read charts and customizing color palettes to incredible degrees.

I had a similar experience when I was a technical writer. One of our lead writers was so concerned with readability of every sentence that he was going through the 300+ page manuals (yes, they were printed then) and manually adjusting all of the line breaks and page breaks. (!) Yes, readability was incrementally improved. But now any number of changes–technical capabilities, edits, inserting larger graphics—required re-adjusting all of those manual “optimizations.” The time it took just to do the additional optimization was incredible, much less the maintenance of these optimizations! Meanwhile, the technical writing team was falling behind on new deliverables.

The same scenario applies to your new casual BI users. This new group needs guidance to help them focus on the highest value practices:

  • Customization of color and appearance of visualizations: When is this customization necessary for a management deliverable, versus indulging an OCD tendency? I too have to stop myself from obsessing about the font, line spacing, and that a certain blue is just a bit different than another shade of blue. Yes, these options do matter. But help these casual users determine when that time is well spent.
  • Proper visualizations: When is a spinning 3D pie chart necessary to grab someone’s attention? BI professionals would firmly say “NEVER!” But these casual users do not have a lot of depth on BI best practices. Give them a few simple guidelines as to when “flash” needs to subsume understanding. Consider offering a monthly one-hour Lunch and Learn that shows them how to create impactful, polished visuals. Understanding if their visualizations are going to be viewed casually on the way to a meeting, or dissected at a laptop, also helps determine how much time to spend optimizing a visualization. No, you can’t just mandate that they all read Tufte.
  • Predictive: Provide advanced analytics capabilities like forecasting and regression directly in their casual BI tools. Using these capabilities will really help them wow their audience with substance instead of flash.
  • Feature requests: Make sure you understand the motivation and business value behind some of the casual users’ requests. These casual users are less likely to understand the implications of supporting specific requests across an enterprise, so make sure you are collaborating on use cases and priorities for substantive requests.

By working with your casual BI users on the above points, you will be able to collectively understand when the absolute exact request is critical (and supports good visualization practices), and when it is an “optimization” that may impact productivity. In many cases, “good” is good enough for the fast turnaround of data discovery.

Next week, I’ll wrap this series up with hints on getting your casual users to embrace the “we” not “me” mentality.

Read Part One of this series: Changing The IT Culture For Self-Service BI Success.

Follow me on Twitter: @InaSAP

Comments

Taking Learning Back to School

Dan Wellers

 

Denmark spends most GDP on labor market programs at 3.3%.
The U.S. spends only 0.1% of it’s GDP on adult education and workforce retraining.
The number of post-secondary vocational and training institutions in China more than doubled from 2000 to 2014.
47% of U.S. jobs are at risk for automation.

Our overarching approach to education is top down, inflexible, and front loaded in life, and does not encourage collaboration.

Smartphone apps that gamify learning or deliver lessons in small bits of free time can be effective tools for teaching. However, they don’t address the more pressing issue that the future is digital and those whose skills are outmoded will be left behind.

Many companies have a history of effective partnerships with local schools to expand their talent pool, but these efforts are not designed to change overall systems of learning.


The Question We Must Answer

What will we do when digitization, automation, and artificial intelligence eject vast numbers of people from their current jobs, and they lack the skills needed to find new ones?

Solutions could include:

  • National and multinational adult education programs
  • Greater investment in technical and vocational schools
  • Increased emphasis on apprenticeships
  • Tax incentives for initiatives proven to close skills gaps

We need a broad, systemic approach that breaks businesses, schools, governments, and other organizations that target adult learners out of their silos so they can work together. Chief learning officers (CLOs) can spearhead this approach by working together to create goals, benchmarks, and strategy.

Advancing the field of learning will help every business compete in an increasingly global economy with a tight market for skills. More than this, it will mitigate the workplace risks and challenges inherent in the digital economy, thus positively influencing the future of business itself.


Download the executive brief Taking Learning Back to School.


Read the full article The Future of Learning – Keeping up With The Digital Economy

Comments

About Dan Wellers

Dan Wellers is the Global Lead of Digital Futures at SAP, which explores how organizations can anticipate the future impact of exponential technologies. Dan has extensive experience in technology marketing and business strategy, plus management, consulting, and sales.

Tags:

Why Millennials Quit: Understanding A New Workforce

Shelly Kramer

Millennials are like mobile devices: they’re everywhere. You can’t visit a coffee shop without encountering both in large numbers. But after all, who doesn’t like a little caffeine with their connectivity? The point is that you should be paying attention to millennials now more than ever because they have surpassed Boomers and Gen-Xers as the largest generation.

Unfortunately for the workforce, they’re also the generation most likely to quit. Let’s examine a new report that sheds some light on exactly why that is—and what you can do to keep millennial employees working for you longer.

New workforce, new values

Deloitte found that two out of three millennials are expected to leave their current jobs by 2020. The survey also found that a staggering one in four would probably move on in the next year alone.

If you’re a business owner, consider putting four of your millennial employees in a room. Take a look around—one of them will be gone next year. Besides their skills and contributions, you’ve also lost time and resources spent by onboarding and training those employees—a very costly process. According to a new report from XYZ University, turnover costs U.S. companies a whopping $30.5 billion annually.

Let’s take a step back and look at this new workforce with new priorities and values.

Everything about millennials is different, from how to market to them as consumers to how you treat them as employees. The catalyst for this shift is the difference in what they value most. Millennials grew up with technology at their fingertips and are the most highly educated generation to date. Many have delayed marriage and/or parenthood in favor of pursuing their careers, which aren’t always about having a great paycheck (although that helps). Instead, it may be more that the core values of your business (like sustainability, for example) or its mission are the reasons that millennials stick around at the same job or look for opportunities elsewhere. Consider this: How invested are they in their work? Are they bored? What does their work/life balance look like? Do they have advancement opportunities?

Ping-pong tables and bringing your dog to work might be trendy, but they aren’t the solution to retaining a millennial workforce. So why exactly are they quitting? Let’s take a look at the data.

Millennials’ common reasons for quitting

In order to gain more insight into the problem of millennial turnover, XYZ University surveyed more than 500 respondents between the ages of 21 and 34 years old. There was a good mix of men and women, college grads versus high school grads, and entry-level employees versus managers. We’re all dying to know: Why did they quit? Here are the most popular reasons, some in their own words:

  • Millennials are risk-takers. XYZ University attributes this affection for risk taking with the fact that millennials essentially came of age during the recession. Surveyed millennials reported this experience made them wary of spending decades working at one company only to be potentially laid off.
  • They are focused on education. More than one-third of millennials hold college degrees. Those seeking advanced degrees can find themselves struggling to finish school while holding down a job, necessitating odd hours or more than one part-time gig. As a whole, this generation is entering the job market later, with higher degrees and higher debt.
  • They don’t want just any job—they want one that fits. In an age where both startups and seasoned companies are enjoying success, there is no shortage of job opportunities. As such, they’re often looking for one that suits their identity and their goals, not just the one that comes up first in an online search. Interestingly, job fit is often prioritized over job pay for millennials. Don’t forget, if they have to start their own company, they will—the average age for millennial entrepreneurs is 27.
  • They want skills that make them competitive. Many millennials enjoy the challenge that accompanies competition, so wearing many hats at a position is actually a good thing. One millennial journalist who used to work at Forbes reported that millennials want to learn by “being in the trenches, and doing it alongside the people who do it best.”
  • They want to do something that matters. Millennials have grown up with change, both good and bad, so they’re unafraid of making changes in their own lives to pursue careers that align with their desire to make a difference.
  • They prefer flexibility. Technology today means it’s possible to work from essentially anywhere that has an Internet connection, so many millennials expect at least some level of flexibility when it comes to their employer. Working remotely all of the time isn’t feasible for every situation, of course, but millennials expect companies to be flexible enough to allow them to occasionally dictate their own schedules. If they have no say in their workday, that’s a red flag.
  • They’ve got skills—and they want to use them. In the words of a 24-year-old designer, millennials “don’t need to print copies all day.” Many have paid (or are in the midst of paying) for their own education, and they’re ready and willing to put it to work. Most would prefer you leave the smaller tasks to the interns.
  • They got a better offer. Thirty-five percent of respondents to XYZ’s survey said they quit a previous job because they received a better opportunity. That makes sense, especially as recruiting is made simpler by technology. (Hello, LinkedIn.)
  • They seek mentors. Millennials are used to being supervised, as many were raised by what have been dubbed as “helicopter parents.” Receiving support from those in charge is the norm, not the anomaly, for this generation, and they expect that in the workplace, too.

Note that it’s not just XYZ University making this final point about the importance of mentoring. Consider Figures 1 and 2 from Deloitte, proving that millennials with worthwhile mentors report high satisfaction rates in other areas, such as personal development. As you can see, this can trickle down into employee satisfaction and ultimately result in higher retention numbers.

Millennials and Mentors
Figure 1. Source: Deloitte


Figure 2. Source: Deloitte

Failure to . . .

No, not communicate—I would say “engage.” On second thought, communication plays a role in that, too. (Who would have thought “Cool Hand Luke” would be applicable to this conversation?)

Data from a recent Gallup poll reiterates that millennials are “job-hoppers,” also pointing out that most of them—71 percent, to be exact—are either not engaged in or are actively disengaged from the workplace. That’s a striking number, but businesses aren’t without hope. That same Gallup poll found that millennials who reported they are engaged at work were 26 percent less likely than their disengaged counterparts to consider switching jobs, even with a raise of up to 20 percent. That’s huge. Furthermore, if the market improves in the next year, those engaged millennial employees are 64 percent less likely to job-hop than those who report feeling actively disengaged.

What’s next?

I’ve covered a lot in this discussion, but here’s what I hope you will take away: Millennials comprise a majority of the workforce, but they’re changing how you should look at hiring, recruiting, and retention as a whole. What matters to millennials matters to your other generations of employees, too. Mentoring, compensation, flexibility, and engagement have always been important, but thanks to the vocal millennial generation, we’re just now learning exactly how much.

What has been your experience with millennials and turnover? Are you a millennial who has recently left a job or are currently looking for a new position? If so, what are you missing from your current employer, and what are you looking for in a prospective one? Alternatively, if you’re reading this from a company perspective, how do you think your organization stacks up in the hearts and minds of your millennial employees? Do you have plans to do anything differently? I’d love to hear your thoughts.

For more insight on millennials and the workforce, see Multigenerational Workforce? Collaboration Tech Is The Key To Success.

Comments