Software developers and testers must be sick of hearing security nuts rant, “Beware SQL injection! Monitor for cross-site scripting! Watch for hijacked session credentials!” I suspect the developers tune us out. Why? Because we’ve been raving about the same defects for most of their careers. Truth is, though, the same set of major security vulnerabilities persists year after year, decade after decade.

The industry has generated newer tools, better testing suites, Agile methodologies, and other advances in writing and testing software. Despite all that, coders keep making the same dumb mistakes, peer reviews keep missing those mistakes, test tools fail to catch those mistakes, and hackers keep finding ways to exploit those mistakes.

One way to see the repeat offenders is to look at the OWASP Top 10. That’s a sometimes controversial ranking of the 10 primary vulnerabilities, published every three or four years by the Open Web Application Security Project.

The OWASP Top 10 list is not controversial because it’s flawed. Rather, some believe that the list is too limited. By focusing only on the top 10 web code vulnerabilities, they assert, it causes neglect for the long tail. What’s more, there’s often jockeying in the OWASP community about the Top 10 ranking and whether the 11th or 12th belong in the list instead of something else. There’s merit to those arguments, but for now, the OWASP Top 10 is an excellent common ground for discussing security-aware coding and testing practices.

Note that the top 10 list doesn’t directly represent the 10 most common attacks. Rather, it’s a ranking of risk. There are four factors used for this calculation. One is the likelihood that applications would have specific vulnerabilities; that’s based on data provided by companies. That’s the only “hard” metric in the OWASP Top 10. The other three risk factors are based on professional judgement.

It boggles the mind that a majority of top 10 issues appear across the 2007, 2010, 2013, and draft 2017 OWASP lists. That doesn’t mean that these application security vulnerabilities have to remain on your organization’s list of top problems, though—you can swat those flaws.

Read more in my essay, “The OWASP Top 10 is killing me, and killing you!“

Despite Elon Musk’s warnings this summer, there’s not a whole lot of reason to lose any sleep worrying about Skynet and the Terminator. Artificial Intelligence (AI) is far from becoming a maleficent, all-knowing force. The only “Apocalypse” on the horizon right now is an over reliance by humans on machine learning and expert systems, as demonstrated by the deaths of Tesla owners who took their hands off the wheel.

Examples of what currently pass for “Artificial Intelligence” — technologies such as expert systems and machine learning — are excellent for creating software. AI software is truly valuable help in contexts that involve pattern recognition, automated decision-making, and human-to-machine conversations. Both types of AI have been around for decades. And both are only as good as the source information they are based on. For that reason, it’s unlikely that AI will replace human beings’ judgment on important tasks requiring decisions more complex than “yes or no” any time soon.

Expert systems, also known as rule-based or knowledge-based systems, are when computers are programmed with explicit rules, written down by human experts. The computers can then run the same rules but much faster, 24×7, to come up with the same conclusions as the human experts. Imagine asking an oncologist how she diagnoses cancer and then programming medical software to follow those same steps. For a particular diagnosis, an oncologist can study which of those rules was activated to validate that the expert system is working correctly.

However, it takes a lot of time and specialized knowledge to create and maintain those rules, and extremely complex rule systems can be difficult to validate. Needless to say, expert systems can’t function beyond their rules.

By contrast, machine learning allows computers to come to a decision—but without being explicitly programmed. Instead, they are shown hundreds or thousands of sample data sets and told how they should be categorized, such as “cancer | no cancer,” or “stage 1 | stage 2 | stage 3 cancer.”

Read more about this, including my thoughts on machine learning, pattern recognition, expert systems, and comparisons to human intelligence, in my story for Ars Technica, “Never mind the Elon—the forecast isn’t that spooky for AI in business.”

Modern medical devices increasingly leverage microprocessors and embedded software, as well as sophisticated communications connections, for life-saving functionality. Insulin pumps, for example, rely on a battery, pump mechanism, microprocessor, sensors, and embedded software. Pacemakers and cardiac monitors also contain batteries, sensors, and software. Many devices also have WiFi- or Bluetooth-based communications capabilities. Even hospital rooms with intravenous drug delivery systems are controlled by embedded microprocessors and software, which are frequently connected to the institution’s network. But these innovations also mean that a software defect can cause a critical failure or security vulnerability.

In 2007, former vice president Dick Cheney famously had the wireless capabilities of his pacemaker disabled. Why? He was concerned “about reports that attackers could hack the devices and kill their owners.” Since then, the vulnerabilities caused by the larger attack surface area on modern medical devices have gone from hypothetical to demonstrable, in part due to the complexity of the software, and in part due to the failure to properly harden the code.

In October 2011, The Register reported that “a security researcher has devised an attack that hijacks nearby insulin pumps, enabling him to surreptitiously deliver fatal doses to diabetic patients who rely on them.” The insulin pump worked because the pump contained a short-range radio that allow patients and doctors to adjust its functions. The researcher showed that, by using a special antenna and custom-written software, he could locate and seize control of any such device within 300 feet.

A report published by Independent Security Evaluators (ISE) shows the danger. This report examined 12 hospitals, the organization concluded “that remote adversaries can easily deploy attacks that manipulate records or devices in order to fully compromise patient health” (p. 25). Later in the report, the researchers show how they demonstrated the ability to manipulate the flow of medicine or blood samples within the hospital, resulting in the delivery of improper medicate types and dosages (p. 37)–and do all this from the hospital lobby. They were also able to hack into and remotely control patient monitors and breathing tubes – and trigger alarms that might cause doctors or nurses to administer unneeded medications.

Read more in my blog post for Parasoft, “What’s the Cure for Software Defects and Vulnerabilities in Medical Devices?”

Las Vegas, January 2017 — “Alexa, secure the enterprise against ransomware.” Artificial intelligence is making tremendous headway, as seen at this year’s huge Consumer Electronics Show (CES). We’re seeing advances that leverage AI in everything from speech recognition to the Internet of Things (IoT) to robotics to home entertainment.

Not sure what type of music to play? Don’t worry, the AI engine in your cloud-based music service knows your taste better than you do. Want to read a book whilst driving to the office? Self-driving cars are here today in limited applications, and we’ll see a lot more of them in 2017.

Want to make brushing your teeth more fun, all while promoting good dental health? The Ara is the “1st toothbrush with Artificial Intelligence,” claims Kolibree, a French company that introduced the product at CES 2017.

Gadgets dominate CES. While crowds are lining up to see the AI-powered televisions, cookers and robots, the real power of AI is hidden, behind the scenes, and not part of the consumer context. Unknown to happy shoppers exploring AI-based barbecues, artificial intelligence is keeping our networks safe, detecting ransomware, helping improve the efficiency of advertising and marketing, streamlining business efficiencies, diagnosing telecommunication faults in undersea cables, detecting fraud in banking and stock-marketing transactions, and even helping doctors track the spread of infectious diseases.

Medical applications capture the popular imagination because they’re so fast and effective. The IBM Watson AI-enabled supercomputer, for example, can read 200 million pages of text in three seconds — and understand what it reads. An oncology application running on Watson analyzes a patient’s medical records, and then combines attributes from the patient’s file with clinical expertise, external research, and data. Based on that information, Watson for Oncology identifies potential treatment plans for a patient. This means doctors can consider the treatment options provided by Watson when making decisions for individual patients. Watson even offers supporting evidence in the form of administration information, as well as warnings and toxicities for each drug.

Doctor AI Can Cure Cybersecurity Ills

Moving beyond medicine, AI is proving essential for protecting computer networks — and their users against intrusion. The traditional non-AI-based anti-virus and anti-malware products can’t protect against advanced threats, and that’s where companies like Cylance come in. They can use neural networks and other machine-learning techniques to study millions of malicious files, from executables to documents to PDFs to images. Using pattern recognition, Cylance have developed a revolutionary machine learning platform that can identify suspicious files that might be seen on websites or as email attachments, even if it’s never seen that particular type of malware before. Nothing but AI can get the job done, not in an era when over a million new pieces of malware, ranging from phishing to ransomware, appear every single day.

Menlo Security is another network-protection company that leverages artificial intelligence. The Menlo Security Isolation Platform uses AI to prevent Internet-based malware from ever reaching an endpoint, such as a desktop or mobile device, because email and websites are accessed inside the cloud — not on the client’s computer. Only safe, malware-free rendering information is sent to the user’s endpoint, eliminating the possibility of malware reaching the user’s device. An artificial intelligence engine constantly scans the Internet session to provide protection against spear-phishing and other email attacks.

What if a machine does become compromised? It’s unlikely, but it can happen — and the price of a single breech can be incredible, especially if a hacker can take full control of the compromised device and use it to attack other assets within the enterprise, such as servers, routers or executives’ computers. If a breach does occur, that’s when the AI technology of Javelin Networks leaps into action, detecting that the attack is in progress, alerting security teams, isolating the device from the network — while simultaneously tricking the attackers into believing they’ve succeeded in their attack, therefore keeping them “on the line” while real-time forensics gather information needed to identify the attacker and help shut them down for good.

Socializing Artificial Intelligence

There’s a lot more to enterprise-scale AI than medicine and computer security, of course. QSocialNow, an incredibly innovative company in Argentina, uses AI-based Big Data and Predictive Analytics to watch an organization’s social media account — and empower them to not only analyze trends, but respond in mere seconds in the case of an unexpected event, such as a rise in customer complaints, the emergence of a social protest, even a physical disaster like an earthquake or tornado. Yes, humans can watch Twitter, Facebook and other networks, but they can’t act as fast as AI — or spot subtle trends that only advanced machine learning can observe through mathematics.

Robots can be powerful helpers for humanity, and AI-based toothbrushes can help us and our kids keep our teeth healthy. While the jury may be out on the implications of self-driving cars on our city streets, there’s no doubt that AI is keeping us — and our businesses — safe and secure. Let’s celebrate the consumer devices unveiled at CES, and the artificial intelligence working behind the scenes, far from the Las Vegas Strip, for our own benefit.