With 2012 just a few short days away, it’s that time of year when, in the words of McAfee Labs’ Dave Marcus, we “dust off the crystal ball, put on our battered Mr. Wizard hat,” and speculate about what the new year has in store. McAfee Labs recently announced its 2012 threat predictions, to which I’d like to add some color, and throw in some observations of my own.
Attacks on Critical Infrastructure
We expect that the volume and sophistication of attacks focused on critical infrastructure – in particular electric, oil and gas, and chemical, will continue to rise in 2012, taking the form of extortion, Denial of Service, and targeted Stuxnet-like attacks. In an ever more networked world, the cyber vulnerabilities of critical infrastructure pose challenges to governments and owners and operators in every sector across the globe.
Threats to Mobile Devices
With increasing popularity, and use cases expanding beyond games and books to work-related tasks like banking, we are seeing more and more people trying to exploit mobile systems. Last month, McAfee Labs released its Q3 Threats Report, which showed that the Android mobile operating system solidified its lead as the primary target for new mobile malware. The amount of malware targeted at Android devices jumped nearly 37% since Q2, putting 2011 on track to be the busiest in mobile malware history. We expect this trend to continue into 2012, with more organizations leveraging Virtual Desktop Infrastructure solutions to sandbox organizations from users’ consumer devices.
Consumerization of IT
In 2012, we expect to continue to see an increased use in tablets for mobile computing, as well as an increased use of social media applications from mobile devices. There will be more demand from both technical and business users wanting to bring their own devices, whether or not the company has authorized their use. 35 different brands of tablets were released this year – it’s a huge and growing industry, and organizations are leveraging technology like virtualization, network access control, and solutions like McAfee Enterprise Mobility Management to adapt to this flood of new technology.
Social Media
Social media is already such an ingrained part of our personal lives, but it has now infiltrated even the depths of our businesses and organizations. Data loss prevention controls, firewalls, IPS, and the like will need to become more application aware in 2012 in order to allow organizations to continue to use social media from a business perspective. We are seeing more and more threats coming in through vehicles like Facebook and Twitter, and we expect to continue to see malware growth in this area, a threat that McAfee is taking very seriously.
McAfee’s Innovation Team has been working hard on a project to apply the concept of reputation from McAfee Global Threat Intelligence to social media systems, letting us probe sites like Twitter for malware-related concepts. On the horizon for 2012 are products using this data – for example, allowing bad tweets to be stripped out of your feed, and flagged in your Twitter reader.
Stealth Rootkits
We expect to continue to see an increase in malware and rootkits getting below the user space and into the kernel space, making it tough for most security controls to detect them. Rootkits will self-mutilate – when traditional anti-malware solutions look for malicious content, a rootkit doesn’t come up as looking like anything bad. But the malware is designed to reassemble itself so it can function. The system looks good, you back it up, and a few weeks later that machine you’re running is infected. You restore from what you thought was a good backup, but you restore with a rootkit that has reassembled itself.
Sometimes this means a whole rebuild and a new OS – one of the reasons why we are looking to move security down to the silicon level. Products like McAfee Deep Defender utilize McAfee DeepSAFE technology with Intel, to sit between the processor and the OS to help protect vital system software residing in the physical memory, providing a new view of the drivers and other software as they operate.
Hacktivism
In the past, financial gain served as the primary motivation behind cybercrime, but we’re seeing increased groups of hackers with other motivations. They are guided by economic, political, or religious interests that generally go beyond their nation’s borders. In 2011, hacktivist “groups” like Anonymous and Lulzsec grabbed a significant number of headlines, and we expect to see this trend continue into 2012. Especially since many of these groups have garnered publicity and notoriety for their cause, we expect that more individuals will decide to take this path.
Spearphishing and SQL Injection Attacks
As the easiest and most common ways to penetrate an organization, these types of attacks are effective and extremely prevalent. User awareness and reputation solutions will be used to combat these types of threats, as well as improved coding techniques and better database security controls.
Cyberwarfare
In 2012, we expect to see at least one major cyber security event similar to South Korea’s 10 Days of Rain attacks – a blatant attack from a nation state that will serve as a prelude to information warfare. Cybercrime has evolved from something of a hobbyist affair to a very professional activity, and is now being leveraged to increase a country’s political power. As the world enters a new period of tension, many countries have redirected their services toward a cyberwar strategy, and many states have not hesitated to put forward their expertise in this arena.
Connected Solutions
Here at McAfee, 2012 will continue to see a bringing together of network security, data, endpoint and security management. We’re looking for cohesive solutions – disparate parts that enrich each other with reputation information from McAfee Global Threat Intelligence, and pieces such as our acquisition of SIEM provider NitroSecurity, McAfee Risk Advisor, and security at the silicon level with McAfee Deep Defender. We will be bringing all of these pieces together, making them all much more relevant and central to the business.
Security is becoming more about business enablement and risk mitigation, as evidenced by the recent Disclosure Guidance on Cybersecurity issued by the SEC – a big step towards the widespread realization that for many orgs, IT and the business are one.
Optimized Security Strategies
Going into 2012, we will need to stop narrowing our focus on just stopping bad things from happening – we need to also focus on improving other business units to support this goal. For example, reducing the overhead for an organization’s help desk, and integrating IT and security as early on as possible. We need to see security as a business enabler that will allow us to take advantage of new market opportunities, without taking on inflated levels of risk.
What are your thoughts on this list – anything trends for 2012 that you would add or take away? Let us know here in the blog, or on Twitter at @McAfeeBusiness, where we regularly update our followers on McAfee news, happenings and events.
By: Brian Contos
Monday, January 9, 2012
Friday, January 6, 2012
Big Data Analytics: The New Corporate "Six Sigma"?
If you've been around for a while, you'll remember how a giant wave of Six Sigma investments crashed over corporations around the world in an effort to improve competitiveness.
At EMC, we all went to Six Sigma class, and learned how to DMAIC key processes, and then DFSS. My personal favorite was the statapult exercise -- big fun if you've never done it.
Over time, an army of Six Sigma green belts were developed throughout EMC's ranks, augmented by the ultimate masters: the Six Sigma Black Belts.
Why did so many companies make such a large investment in Six Sigma?
The answer is painfully simple: it quickly became the new competitive ante.
You either invested in getting good at Six Sigma, or you had better be prepared to suffer at the hands of competitors who had wisely made that investment.
I think we're seeing the opening scenes of a similar movie: a need for investing in broad-based skills in big data analytics proficiency.
The case for this particular observation is strengthed by recent results from EMC's Data Science survey -- practitioners point to the lack of these broad based skills as one of the major things holding them back.
This time around, the motivation is also simple: invest in learning to use multiple data sources and the newer tools to better predict the future, or be prepared to suffer at the hands of those who have made that investment.
And today, EMC is announcing a key component of our investments to help our customers and partners get better at this new and important skill set: an associate-level one week course and certification in big data analytics techniques.
I think it's going to be popular :)
History Can Always Teach Us Lessons If you're in a competitive industry (and who isn't?), a lot of leadership time is spent thinking about new ways to create a competitive edge. Everything is fair game: better versions of existing products, new ways of engaging with customers, investments in entering new markets, a focus on creating an innovative culture, tools to make better decisions, and so on.
The story of Six Sigma is instructive in this regard. My impression is that Motorola figured out a way to improve quality processes and ended up kicking serious patootie on everyone else in their sector at the time. Although Six Sigma had its roots in semiconductor manufacturing, the framework proved broadly applicable to all manners of business processes.
EMC's motivations -- at an executive level -- were likely quite simple.
This Six Sigma stuff looks like it creates a meaningful competitive advantage for those that seriously adopt it.
We do business in an incredibly competitive industry.
Ergo, we have no choice but to enthusiastically embrace Six Sigma proficiency throughout EMC, so let's get started.
(Quick note: this was the same line of thinking that was behind our investment in social media proficiency five years ago, not to mention other similar corporate initiatives).
The executive team started broadcasting the priority loud and clearly. A Six Sigma program office was formed and staffed to drive engagement. As a member of the management team, I was "strongly encouraged" to take a few days of training. A few of my people were really interested in the whole topic, and ended up going down the green-belt-leads-to-black-belt path. Communications on progress and business results were consistently frequent.
And then, one day, we were all sort of done with the heavy lifting.
We understood the problems, the tools and the methodologies. We had successfully applied the methodologies to broad portions of our business, and the results were plainly obvious to all. Six sigma had simply become part of our culture and the way we did business.
The envisioned change had happened.
History Is Repeating Itself
Done well, big data analytics enables organizations to create models that can help predict future outcomes.
Traditional business intelligence was mostly about understanding what had happened in the past using limited data sets; the new wave is clearly focused on understanding underlying relationships between ostensibly disparate data sets and using them to make predictions about likely outcomes.
In essence, you're investing in creating the proverbial crystal ball. Being able to predict future outcomes using statistically validated models seems like a handy thing to have in the corporate tool belt, if you ask me :)
While the ideas behind big data analytics and associated data scientists aren't really all that new, their broader applicability is certainly new. New, rich data sources are popping up everywhere, and they're getting easier to acquire. The costs associated with the supporting tools and infrastructure are droppping like rocks (insert obligatory EMC product technology plug here). Core business processes that are enabled with real-time predictive analytical insights can clearly be shown to perform far better than those that are not.
And more and more business leaders are realizing that -- yes -- big data analytics is the next competitive ante. Whether they got there by themselves -- or are seeing their erstwhile competitors doing it -- really doesn't matter.
For the newer business models that were "born digital", they already get it, and are well along their way. Feed them cool technology (and lots of data sources), and they'll be just fine.
The real interesting action is in traditional business models that look very different when augmented by big data analytics.
I'm starting to see more executive teams "get it" (just like they did with Six Sigma), invest in corporate-level program offices (just like they did with Six Sigma), driving broad-based training to create a cadre of data science green belts and black belts, and creating newer self-service large-scale analytics environments where the new skills can be developed and practiced.
For me, history is starting to repeat itself. Again.
EMC Education Is Investing In Skills Creation
If you're with me so far, you probably have a good understanding how EMC Education's new EMC Proven Professional Data Science Associate coursework and certification fits in (EMCDSA for short).
We think these skills are going to be incredibly important: now, and in the future. We believe it, and our customers are telling us the exact same thing.
While this specific new educational offering won't make you a bona-fide, card-carrying data science rockstar in a week, what it does do very well is take someone with the natural skills and inclinations, and gives them the background and experience to work as part of a larger data science team.
People who are interested in data science and this whole area are generally fun people to work with.
We've constructed a sample persona, and -- based on my personal experience -- it's pretty accurate. It's a nice mix of left-brain and right-brain skills: from the quantitative to the collaborative to the creative.
If you're a regular reader of this blog, your personality probably lines up in many of these regards, as does mine :)
The model I'm seeing over and over again is a small team of hard-core data scientists, augmented by a much larger audience of people who understand what they do, and how they do it.
Whether these people are co-workers, managers, helpers, business partners, etc. etc. -- this course is targeted at people who (a) see themselves working more with data scientists -- and data science -- in the future, or (b) see themselves evolving into a rock-star data scientist over time.
Either way, I think there's a large audience for this sort of coursework.
The Coursework
As you can see from the attached graphic, it's a week well-spent, in my humble opinion.
The first day is about context: why this is important, why it's different, the intended role that's being fulfilled, and so on.
The next two days are deep dives in classical analytics using modern tools. The fourth day branches out to unstructured data (e.g. Hadoop) as well as the powerful capabilities of in-database analytics.
And the fifth day is mostly about the all-important communication and storytelling aspects.
Lots of labs with real-world data sets, modern tools and large-scale infrastructure, plus the opportunity to meet and work with like-minded people. Like other EMC Education offerings, I'm sorely tempted to clear a full week and go have some fun :)
Wait, I'm In IT -- Why Should I Care?
When I talk about this subject to career IT professionals, they're interested, but they're not exactly sure how these skills might apply to them in their chosen profession. I think there's a deeper connection than most may realize.
First (and most obviously) if you're going to have an organization with progressively more big data analytics types, it pays to understand a bit about who they are, what they're doing -- and what they need from IT.
More directly, it appears that many IT disciplines will incorporate big data analytics skills in the near future. Consider, just for a moment, what capacity planning or performance management might look like in a few short years, especially in a world of large-scale variable IT service consumption. You're going to want to get pretty good at predicting the future :)
Indeed, the next wave of security thinking is already leaning towards predictive analytical models using an incredibly wide variety of data sources.
The message is simple: big data analytics will not only be a new use case for IT, it will likely transform many of IT's key processes as well.
Why This Matters
Competing through big data analytics is quickly becoming the new ante in so many industries that I encounter. Sooner or later, most business leaders will realize they have to invest in these proficiencies, or suffer at the hands of those that have.
Once these leadership teams make their decisions -- and start to organize for success -- there will be a real and immediate need for increased proficiency across the broader organization.
I think all the bright people who think they might be involved with this have a decision to make.
Do they wait for the time when they're told to attend a specific course?
Or do they decide to get ahead of the curve -- ahead of the inevitable wave?
At EMC, we all went to Six Sigma class, and learned how to DMAIC key processes, and then DFSS. My personal favorite was the statapult exercise -- big fun if you've never done it.
Over time, an army of Six Sigma green belts were developed throughout EMC's ranks, augmented by the ultimate masters: the Six Sigma Black Belts.
Why did so many companies make such a large investment in Six Sigma?
The answer is painfully simple: it quickly became the new competitive ante.
You either invested in getting good at Six Sigma, or you had better be prepared to suffer at the hands of competitors who had wisely made that investment.
I think we're seeing the opening scenes of a similar movie: a need for investing in broad-based skills in big data analytics proficiency.
The case for this particular observation is strengthed by recent results from EMC's Data Science survey -- practitioners point to the lack of these broad based skills as one of the major things holding them back.
This time around, the motivation is also simple: invest in learning to use multiple data sources and the newer tools to better predict the future, or be prepared to suffer at the hands of those who have made that investment.
And today, EMC is announcing a key component of our investments to help our customers and partners get better at this new and important skill set: an associate-level one week course and certification in big data analytics techniques.
I think it's going to be popular :)
History Can Always Teach Us Lessons If you're in a competitive industry (and who isn't?), a lot of leadership time is spent thinking about new ways to create a competitive edge. Everything is fair game: better versions of existing products, new ways of engaging with customers, investments in entering new markets, a focus on creating an innovative culture, tools to make better decisions, and so on.
The story of Six Sigma is instructive in this regard. My impression is that Motorola figured out a way to improve quality processes and ended up kicking serious patootie on everyone else in their sector at the time. Although Six Sigma had its roots in semiconductor manufacturing, the framework proved broadly applicable to all manners of business processes.
EMC's motivations -- at an executive level -- were likely quite simple.
This Six Sigma stuff looks like it creates a meaningful competitive advantage for those that seriously adopt it.
We do business in an incredibly competitive industry.
Ergo, we have no choice but to enthusiastically embrace Six Sigma proficiency throughout EMC, so let's get started.
(Quick note: this was the same line of thinking that was behind our investment in social media proficiency five years ago, not to mention other similar corporate initiatives).
The executive team started broadcasting the priority loud and clearly. A Six Sigma program office was formed and staffed to drive engagement. As a member of the management team, I was "strongly encouraged" to take a few days of training. A few of my people were really interested in the whole topic, and ended up going down the green-belt-leads-to-black-belt path. Communications on progress and business results were consistently frequent.
And then, one day, we were all sort of done with the heavy lifting.
We understood the problems, the tools and the methodologies. We had successfully applied the methodologies to broad portions of our business, and the results were plainly obvious to all. Six sigma had simply become part of our culture and the way we did business.
The envisioned change had happened.
History Is Repeating Itself
Done well, big data analytics enables organizations to create models that can help predict future outcomes.
Traditional business intelligence was mostly about understanding what had happened in the past using limited data sets; the new wave is clearly focused on understanding underlying relationships between ostensibly disparate data sets and using them to make predictions about likely outcomes.
In essence, you're investing in creating the proverbial crystal ball. Being able to predict future outcomes using statistically validated models seems like a handy thing to have in the corporate tool belt, if you ask me :)
While the ideas behind big data analytics and associated data scientists aren't really all that new, their broader applicability is certainly new. New, rich data sources are popping up everywhere, and they're getting easier to acquire. The costs associated with the supporting tools and infrastructure are droppping like rocks (insert obligatory EMC product technology plug here). Core business processes that are enabled with real-time predictive analytical insights can clearly be shown to perform far better than those that are not.
And more and more business leaders are realizing that -- yes -- big data analytics is the next competitive ante. Whether they got there by themselves -- or are seeing their erstwhile competitors doing it -- really doesn't matter.
For the newer business models that were "born digital", they already get it, and are well along their way. Feed them cool technology (and lots of data sources), and they'll be just fine.
The real interesting action is in traditional business models that look very different when augmented by big data analytics.
I'm starting to see more executive teams "get it" (just like they did with Six Sigma), invest in corporate-level program offices (just like they did with Six Sigma), driving broad-based training to create a cadre of data science green belts and black belts, and creating newer self-service large-scale analytics environments where the new skills can be developed and practiced.
For me, history is starting to repeat itself. Again.
EMC Education Is Investing In Skills Creation
If you're with me so far, you probably have a good understanding how EMC Education's new EMC Proven Professional Data Science Associate coursework and certification fits in (EMCDSA for short).
We think these skills are going to be incredibly important: now, and in the future. We believe it, and our customers are telling us the exact same thing.
While this specific new educational offering won't make you a bona-fide, card-carrying data science rockstar in a week, what it does do very well is take someone with the natural skills and inclinations, and gives them the background and experience to work as part of a larger data science team.
People who are interested in data science and this whole area are generally fun people to work with.
We've constructed a sample persona, and -- based on my personal experience -- it's pretty accurate. It's a nice mix of left-brain and right-brain skills: from the quantitative to the collaborative to the creative.
If you're a regular reader of this blog, your personality probably lines up in many of these regards, as does mine :)
The model I'm seeing over and over again is a small team of hard-core data scientists, augmented by a much larger audience of people who understand what they do, and how they do it.
Whether these people are co-workers, managers, helpers, business partners, etc. etc. -- this course is targeted at people who (a) see themselves working more with data scientists -- and data science -- in the future, or (b) see themselves evolving into a rock-star data scientist over time.
Either way, I think there's a large audience for this sort of coursework.
The Coursework
As you can see from the attached graphic, it's a week well-spent, in my humble opinion.
The first day is about context: why this is important, why it's different, the intended role that's being fulfilled, and so on.
The next two days are deep dives in classical analytics using modern tools. The fourth day branches out to unstructured data (e.g. Hadoop) as well as the powerful capabilities of in-database analytics.
And the fifth day is mostly about the all-important communication and storytelling aspects.
Lots of labs with real-world data sets, modern tools and large-scale infrastructure, plus the opportunity to meet and work with like-minded people. Like other EMC Education offerings, I'm sorely tempted to clear a full week and go have some fun :)
Wait, I'm In IT -- Why Should I Care?
When I talk about this subject to career IT professionals, they're interested, but they're not exactly sure how these skills might apply to them in their chosen profession. I think there's a deeper connection than most may realize.
First (and most obviously) if you're going to have an organization with progressively more big data analytics types, it pays to understand a bit about who they are, what they're doing -- and what they need from IT.
More directly, it appears that many IT disciplines will incorporate big data analytics skills in the near future. Consider, just for a moment, what capacity planning or performance management might look like in a few short years, especially in a world of large-scale variable IT service consumption. You're going to want to get pretty good at predicting the future :)
Indeed, the next wave of security thinking is already leaning towards predictive analytical models using an incredibly wide variety of data sources.
The message is simple: big data analytics will not only be a new use case for IT, it will likely transform many of IT's key processes as well.
Why This Matters
Competing through big data analytics is quickly becoming the new ante in so many industries that I encounter. Sooner or later, most business leaders will realize they have to invest in these proficiencies, or suffer at the hands of those that have.
Once these leadership teams make their decisions -- and start to organize for success -- there will be a real and immediate need for increased proficiency across the broader organization.
I think all the bright people who think they might be involved with this have a decision to make.
Do they wait for the time when they're told to attend a specific course?
Or do they decide to get ahead of the curve -- ahead of the inevitable wave?
By: Chuck Hollis
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