Is your cyber security strategy mature enough to handle a sophisticated threat? Many organizations would likely say no. Skimming and phishing were among the most prevalent hacking techniques. Despite the rising numbers of organizations that are impacted by sophisticated threats, many IT leaders aren't planning to change their security strategies this year. Don't let yourself become another statistic waiting for an attack to happen.

The drones fell out of the sky over China Lake, California, like a colony of bats fleeing a cave in the night. Over 100 of them dropped from the bellies of three Boeing F/A-18 Super Hornet fighter jets, their sharp angles cutting across the clear blue sky. As they encircled their target, the mechanical whir of their flight sounded like screaming. This was the world's largest micro-drone swarm test. Conducted in October 2016 by the Department of Defense's Strategic Capabilities Office and the Navy's Air Systems Command, the test was the latest step in what could be termed a swarm-drone arms race.

Just days later, we got our first hint Buffett himself was buying Apple (NASDAQ:AAPL)! Followed shortly after by that bombshell CNBC interview (here's the transcript), where he revealed an $18 billion plus Apple investment*, the vast majority bought by him in January and recent months (and the rest by Todd and Ted in early-2016). What better confirmation of my assertion that value investors - even the greatest of them all, at 86 years of age - would be wise to pose such a question to themselves? In fact, I wonder if it's the largest stock investment ever made by a single investor (again, in terms of cost)? Sure, Todd, Ted, and Charlie did provide some inspiration and feedback here, but we can be sure Buffett never buys anything until he makes his own mind up! Thoughts?] Was I confident I'd see Buffett talking up an Apple position just weeks later? And maybe I chose it specifically as the largest, most obvious and controversial value stock out there. Not to mention, the head fake I pulled: While I did summarize its attractive fundamentals and valuation, my post clearly wasn't intended to be a detailed thesis. But hey, it was still the right question at the right time, so I'll take the kudos!

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow). Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loans, to managing assets, to assessing risks. Yet, few technically-savvy professionals have an accurate view of just how many ways machine learning finds its way into their daily financial lives.

Artificial intelligence that can quickly identify patterns of risky behavior may be the only viable solution to protect health systems against an influx of ransomware attacks. The use of AI in the clinical environment has been well-documented as more health systems are turning to machine learning to improve oncology care, fight physician burnout, boost patient engagement and even reverse diabetes. But healthcare needs to use the power of machine learning to combat cybersecurity threats, according to a report (PDF) released by the Institute for Critical Infrastructure Technology. James Scott, a senior fellow at ICIT who authored the report, didn't mince words regarding the urgent need to protect patient information against cyberattacks, particularly ransomware, which has emerged as a critical threat to the industry over the past year. Scott noted that the healthcare industry "demonstrates lackadaisical cyber hygiene, finagled and Frankensteined networks, virtually unanimous absence of security operations teams and good ol' boys club bureaucratic board members flexing little more than smoke and mirror, cybersecurity theatrics as their organizational defense."

A lot of us have been looking at the recent WikiLeaks drop of Central Intelligence Agency (CIA) files related to hacking Internet of Things (IoT) and personal technology devices. It includes a lot of data, so it is easy to get lost in the woods with regard to what these tools do. But apparently they can break into devices and cast blame for it on the Russians, which I believe is problematic in a whole number of different ways. I think the real problem with this is the CIA's risk assessment process. I think this decision process problem is far larger than just the CIA and reflects on security in general. I also think that a deep learning tool like IBM's Watson could, if placed in the decision process, help prevent bad decisions like this.

Apple, Amazon, and Google are at war over the future of personal computing, and now Amazon is taking the battle to its rivals' home turf: the smartphone. All three are convinced that voice-controlled virtual assistants have the potential to transform how we interact with our devices. Apple has Siri, Google has Google Assistant, and Amazon has Alexa. They believe that in the future, you won't swipe or manually fiddle with your device -- you'll just talk to it, as you would another human. Whoever gets ahead now has the potential to define the next major era of computing.

Distributed planning, communication, and control algorithms for autonomous robots make up a major area of research in computer science. But in the literature on multirobot systems, security has gotten relatively short shrift. In the latest issue of the journal Autonomous Robots, researchers from MIT's Computer Science and Artificial Intelligence Laboratory and their colleagues present a new technique for preventing malicious hackers from commandeering robot teams' communication networks. The technique could provide an added layer of security in systems that encrypt communications, or an alternative in circumstances in which encryption is impractical. "The robotics community has focused on making multirobot systems autonomous and increasingly more capable by developing the science of autonomy. In some sense we have not done enough about systems-level issues like cybersecurity and privacy," says Daniela Rus, an Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and senior author on the new paper.

Do you know where the next cyber attack will strike or when it's likely to happen? Using machine learning techniques and data analysis, it's now possible to forecast cyber attacks with a decent degree of accuracy. Mind you, we can't pinpoint the exact time of an attack, but like weather forecasters, we can review the available data, look for digital fingerprints and behavior patterns and assess whether a specific target is in the crosshairs of cybercriminals. Like sharks circling prey, attackers exhibit certain behaviors and characteristics when they're planning to strike, and we're getting better at detecting the early stages of their kill chain. In some recent cases, Level 3 Threat Research Labs identified behavior signaling an imminent attack and notified the targets.

A new report has proposed AI and Machine learning as a potential cure to the "Ransomware pandemic" making its way through the healthcare sector. The Institute of Critical Infrastructure Technology (ICIT) recently released a report called "How to Crush the Health Sector's Ransomware Pandemic". James Scott, senior fellow at ICIT and the author of the paper offers a plain solution to the worrying rash of cyber-attack on hospitals and healthcare providers that have held patient safety to ransom. He notes the proliferation of not only dynamic and adaptive malware, but the sheer number of adversaries that can find their way around defences no matter how resilient or well-resourced. But against this gloomy landscape, says Scott in a defiantly optimistic tone, what if healthcare organisations could use machine learning to overcome these threats?