Automated hacking, deepfakes are going to be major cybersecurity threats in 2020

Artificial intelligence used to carry out automated, targeted hacking is set to be one of the major threats to look out for in 2020, according to a cybersecurity expert.

The tools and knowledge for developing malicious AI and machine learning codes are becoming more mainstream and there is a lot more data out there for hackers to gather and use, Etay Maor, chief security officer at cyberintelligence company IntSights, told CNBC.

“We will see the adoption of AI tools for targeted and automated attacks,” Maor said.

The idea of a computer program learning to attack things by itself and expanding its knowledge base to become more sophisticated is scary. But, it is a serious consideration given how the cyber-threat landscape has evolved in recent years and is seen as a major risk for the global economy.

In the past, defacing or taking down websites and stealing credit card information were considered major instances of cyberattacks. But, those attacks were costly because they required attackers to devote more time and resources to carry out. With AI, an attacker can carry out multiple and repeated attacks on a network by programming a few lines of code to do most of the work.

Deepfakes are a threat

Related to AI is the rise in the spread of disinformation and deepfakes, especially since 2020 is an election year in the  United States, according to Maor.

Deepfakes are images and videos created using computers and machine learning software to make them seem real, even though they are not. Experts predict that this technology could be used to cause confusion and propagate disinformation, especially in the context of global politics, and may become extremely hard to detect.

“These will be difficult to combat as attribution is becoming harder and harder and the technology, means, and infrastructure becomes more and more accessible for the attackers,” Maor said.

Other security experts agree. In an October blog post, Forrester Principal Analyst, Jeff Pollard, wrote that costs related to deepfake scams will exceed $250 million in 2020. Media reports suggest that some companies are already being tricked into wiring large amounts of money to scammers.

“Now that a precedent exists showing economic gains from AI-backed deepfake technology, expect more to follow,” Pollard wrote. “Expect the development of more deepfake-based attacks fabricating convincing audio and video at a fraction of the cost.”

Cybersecurity company Forcepoint predicted that cybercriminals could use deepfake technology to generate compromising photos and videos of individuals and threaten to release them if their ransom demands are not met.

“At the organizational level, deepfakes will also be used to impersonate high-level targets at organizations to scam employees by transferring funds into fraudulent accounts,” Alvin Rodrigues, senior director and security strategist for Asia Pacific at Forcepoint, told CNBC.

“In the political arena, we can expect deepfakes to be leveraged as a tool to discredit electoral candidates and push inaccurate falsehoods to voters via social media,” he added.

Related to deepfakes, cybersecurity firm Check Point said in October that a new cold war between Western and Eastern powers is taking place online due to a growing divergence in their technologies and intelligence.

“Cyber-attacks will increasingly be used as proxy conflicts between smaller countries, funded and enabled by large nations looking to consolidate and extend their spheres of influence,” the company said in a blog post. It pointed to the U.S. carrying out a secret cyber operation against Iran after the latter’s attacks on Saudi oil facilities.

Other threats

Beyond AI and deepfakes, there are a number of growing threats that security experts have predicted for 2020:

Supply chain and third-party attacks — IntSights’ Maor said that as large companies invest heavily in cybersecurity measures, attackers are likely to switch their focus on easier, smaller and less-funded targets: essentially those firms that supply the large organizations. He predicted that these types of attacks are likely to happen in areas such as health care, automotive and broadcasting. “This is a concern because there is only so much an enterprise can do to force security on its vendors,” he said.

5G will make it easier to steal data — Forcepoint’s Rodrigues told CNBC that wider adoption of the next generation of high-speed mobile internet , known as 5G, would allow cybercriminals to transfer large volumes of data from one server to another online at faster speeds. “With the roll-out of 5G continuing in 2020, we can expect to see an increase in the volume and speed of data theft,” he said.

Attacks on critical infrastructures will increase — Criminals attacking utilities and critical infrastructure will continue to grow next year, Check Point predicted in its blog post. “In many cases, critical power and water distribution infrastructure uses older technology that is vulnerable to remote exploitation because upgrading it risks service interruptions and downtime,” the firm wrote.

Geopolitics to drive cyber espionage and nation-state attacks — Cybersecurity company FireEye said in its 2020 prediction report  that geopolitical tensions are often a “significant driver of intrusions and disruptive attacks.” Nation-state activities are expected to continue developing and the firm said it has observed operations linked to Russia, China, Iran, and Venezuela to spread certain kinds of information. “While not limited to issues around elections, we often observe these activities to be particularly intense around elections,” the company said, pointing to various elections due in 2020 in places like Taiwan, South Korea, France, Poland, and the U.S.


How artificial intelligence is transforming the future of digital marketing

From smart search options and personalised messaging to being used in campaigns and marketing, AI and machine learning are increasingly being used in digital marketing.

AI in digital marketing

Digital marketing relies on leveraging insights from the copious amounts of data that gets created every time a customer interacts with a digital asset. Algorithms optimise various factors and data points that influence digital marketing success. In 2020, we anticipate a significant uptick in the mainstreaming of AI and machine learning use cases in digital marketing across several areas.

Search will get very smart

In the past year, online search has had several AI and machine learning developments. Google is leading the pack with exciting applications in information retrieval. For example, Google’s BERT technology can process a word in the context of all the other terms in a sentence, rather than one-by-one in order. BERT also enables anyone to train their own state-of-the-art question answering system. Customisation of search results and the results page based on learnings from past interactions and preferences of a user is another application of machine learning used in search.

AI-driven personalisation of messaging

Several adtech companies have been focussing on using AI and machine learning to find the right audience to write better ads than humans, and to increase conversion rates and engagement with the target audience. There are also several AI-led developments in the area of creating dynamic ads and landing pages to personalise marketing messages on the fly. AI has an application in content creation in terms of determining the logic of personalisation as also curating content specific to an individual, using techniques such as natural language generation (NLG).

Use of machine learning in campaign operations

Platforms such as Google and Facebook have been at the forefront of AI/ML applications in marketing. Starting from smart bidding and smart campaigns to auto-generated ads, Google is making it easy for advertisers. Smart bidding options such as TROAS, TCPA, and others use advanced machine learning algorithms to train on data at a vast scale to make accurate predictions about how different bid amounts might impact conversion or conversion value and assist advertisers in optimising without getting into too many details. Google factors in a wide range of contextual signals (through search data) to predict user behaviour and to influence auction time bidding as per the goal set by advertisers. Facebook has also incorporated machine learning across campaign planning and execution, as also in ad placements and ad delivery. Similarly, on the organic search side, machine learning-based product ALPS reverse engineers Google’s ranking algorithm, and is able to accurately quantify ranking drivers, provide precise recommendations for changes, and predicts the impact of SEO actions before they are implemented. Similar technology to drive improved ad copy testing in digital marketing exists. These help in evaluating ad copies and landing pages on various parameters like relevancy, use of action promoters/inhibitors, urgency inducers, page layout, load times, etc., to gauge the impact on ad relevance, expected CTR, and landing page experience.

Future trends

AI will also have additional application in digital marketing with the uptick in the adoption of technologies such as VR and AR, as commercial use cases of these technologies find wider adoption in retail and other sectors. Many retailers are also testing AI and VR/AR technologies together to make the user experience personalised to an individual. Other areas of impact include voice search. We will increasingly see ads about things which we just said or talked about, but haven’t searched for yet. Similarly, image search is also being used by many brands for their consumers to match patterns and identify products using image search.


AI-powered marketing needs interpretability – and collaboration

The business world is gearing up for an artificial intelligence (AI) revolution, as the budding technology is expected to grow into a $118.5bn industry by 2025.

Many organisations are already embracing AI’s predictive abilities to automate repetitive tasks. For example, AI allows systems to process increasingly more complex types of input data, like natural language and images. Handwriting recognition uses image recognition to eliminate boring data entry tasks. The Alexa assistant you have at home can understand the intent of your request from the soundwaves of your voice and answer appropriately – and for much cheaper than a real-life assistant.

While computers have obvious strengths, the ability to draw on gigabytes of data for example, humans have many unique capabilities which machines currently struggle with. The natural ability of humans to communicate and collaborate currently dominate that of machines, and this is particularly important in situations where humans and machines must work together, such as where a human has a legal duty to understand outcomes, e.g. law or medicine. Other knowledge work, where human decision making cannot be replaced, but may be augmented by machine insight, poses similar challenges for current AI systems. This inability to collaborate is holding back wider AI adoption and innovation.

Marketing and AI

Let’s consider this problem through the lens of marketing, a normally tech-savvy industry which, in a lot of ways, is being surprisingly slow to innovate with AI.

Automation is already being used very effectively for repetitive tasks such as ads targeting and bidding. The real-time matching of adverts to viewers is facilitated by machine intelligence at some of the most innovative companies in the world, dynamically predicting click through rates and optimising ad selections on the fly.

But when it comes to more strategic, creative decisions – such as what ad creatives the team should use – AI struggles to be as effective because it can’t easily explain it’s decisions.

To put it bluntly, AI does not seem to be a very good teammate.

The black box of AI

Normally, the better at predicting an algorithm is, the more unintelligible it’s workings are. This lack of interpretability is what we call the ‘black box’ of AI and it has deep implications for how marketers stand to benefit from the technology.

For example, imagine that you had an AI model which was trained to predict whether your next ad was going to appeal to the audience that you’d selected for it. This could be useful, say, to show to the client that the creative treatment is a winner in an objective way, or to test new creative elements quickly and cheaply. But if the model says your creative is going to underperform, but can’t explain why it’s making that judgement, how are you going to make the ad better?

Content creation is expensive and time-consuming, so creating 100 ad creatives in the hope that you will find the best-performing one is inefficient. The black-and-white results which machine learning algorithms tend to provide isn’t enough for marketers to glean any actionable insights.

What if AI was interpretable for marketers?

Data-driven systems such as AI can be seen as existing on a scale from descriptive – discussing the past –to predictive – a system that can understand the future – and finally prescriptive – understanding how to change the future.

An AI system which could explain its decisions and outline what the user could change to get a better result would be a prescriptive system and would be a huge step toward finding a common language between human and machine.

That said: it’s really hard! Current interpretability methods are more aimed at researchers than business users. So what can we do to make AI more collaborative?

Collaborative AI

For AI to be a part of creative marketing decision-making, measures need to be made to combine the analytical with the artistic. This means finding a common language between man and machine, so that users can actually action suggestions from the AI.

How AI Helped Predict the Coronavirus Outbreak Before It Happened

Coronavirus has been all over the news for the last couple weeks. A dedicated hospital sprang up in just eight days, the stock market took a hit, Chinese New Year celebrations were spoiled, and travel restrictions are in effect.

But let’s rewind a bit; some crucial events took place before we got to this point.

A little under two weeks before the World Health Organization (WHO) alerted the public of the coronavirus outbreak, a Canadian artificial intelligence company was already sounding the alarm. BlueDot uses AI-powered algorithms to analyze information from a multitude of sources to identify disease outbreaks and forecast how they may spread. On December 31st 2019, the company sent out a warning to its customers to avoid Wuhan, where the virus originated. The WHO didn’t send out a similar public notice until January 9th, 2020.

The story of BlueDot’s early warning is the latest example of how AI can improve our identification of and response to new virus outbreaks.

Predictions Are Bad News

Global pandemic or relatively minor scare? The jury is still out on the coronavirus. However, the math points to signs that the worst is yet to come.

Scientists are still working to determine how infectious the virus is. Initial analysis suggests it may be somewhere between influenza and polio on the virus reproduction number scale, which indicates how many new cases one case leads to.

UK and US-based researchers have published a preliminary paper estimating that the confirmed infected people in Wuhan only represent five percent of those who are actually infected. If the models are correct, 190,000 people in Wuhan will be infected by now, major Chinese cities are on the cusp of large-scale outbreaks, and the virus will continue to spread to other countries.

Finding the Start

The spread of a given virus is partly linked to how long it remains undetected. Identifying a new virus is the first step towards mobilizing a response and, in time, creating a vaccine. Warning at-risk populations as quickly as possible also helps with limiting the spread.

These are among the reasons why BlueDot’s achievement is important in and of itself. Furthermore, it illustrates how AIs can sift through vast troves of data to identify ongoing virus outbreaks.

BlueDot uses natural language processing and machine learning to scour a variety of information sources, including chomping through 100,000 news reports in 65 languages a day. Data is compared with flight records to help predict virus outbreak patterns. Once the automated data sifting is completed, epidemiologists check that the findings make sense from a scientific standpoint, and reports are sent to BlueDot’s customers, which include governments, businesses, and public health organizations.

AI for Virus Detection and Prevention

Other companies, such as Metabiota, are also using data-driven approaches to track the spread of the likes of the coronavirus.

Researchers have trained neural networks to predict the spread of infectious diseases in real time. Others are using AI algorithms to identify how preventive measures can have the greatest effect. AI is also being used to create new drugs, which we may well see repeated for the coronavirus.

If the work of scientists Barbara Han and David Redding comes to fruition, AI and machine learning may even help us predict where virus outbreaks are likely to strike – before they do.

The Uncertainty Factor

One of AI’s core strengths when working on identifying and limiting the effects of virus outbreaks is its incredibly insistent nature. AIs never tire, can sift through enormous amounts of data, and identify possible correlations and causations that humans can’t.

However, there are limits to AI’s ability to both identify virus outbreaks and predict how they will spread. Perhaps the best-known example comes from the neighboring field of big data analytics. At its launch, Google Flu Trends was heralded as a great leap forward in relation to identifying and estimating the spread of the flu—until it underestimated the 2013 flu season by a whopping 140 percent and was quietly put to rest.

Poor data quality was identified as one of the main reasons Google Flu Trends failed. Unreliable or faulty data can wreak havoc on the prediction power of AIs.

In our increasingly interconnected world, tracking the movements of potentially infected individuals (by car, trains, buses, or planes) is just one vector surrounded by a lot of uncertainty.

The fact that BlueDot was able to correctly identify the coronavirus, in part due to its AI technology, illustrates that smart computer systems can be incredibly useful in helping us navigate these uncertainties.

Importantly, though, this isn’t the same as AI being at a point where it unerringly does so on its own—which is why BlueDot employs human experts to validate the AI’s findings.