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Securing the energy revolution and IoT future

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3 years ago

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Securing the energy revolution and IoT future


In early 2021, Americans living on the East Coast got a sharp lesson on the growing importance of cybersecurity in the energy industry. A ransomware attack hit the company that operates the Colonial Pipeline—the major infrastructure artery that carries almost half of all liquid fuels from the Gulf Coast to the eastern United States. Knowing that at least some of their computer systems had been compromised, and unable to be certain about the extent of their problems, the company was forced to resort to a brute-force solution: shut down the whole pipeline.

Leo Simonovich is vice president and global head of industrial cyber and digital security at Siemens Energy.

The interruption of fuel delivery had huge consequences. Fuel prices immediately spiked. The President of the United States got involved, trying to assure panicked consumers and businesses that fuel would become available soon. Five days and untold millions of dollars in economic damage later, the company paid a $4.4 million ransom and restored its operations.

It would be a mistake to see this incident as the story of a single pipeline. Across the energy sector, more and more of the physical equipment that makes and moves fuel and electricity across the country and around the world relies on digitally controlled, networked equipment. Systems designed and engineered for analogue operations have been retrofitted. The new wave of low-emissions technologies—from solar to wind to combined-cycle turbines—are inherently digital tech, using automated controls to squeeze every efficiency from their respective energy sources.

Meanwhile, the covid-19 crisis has accelerated a separate trend toward remote operation and ever more sophisticated automation. A huge number of workers have moved from reading dials at a plant to reading screens from their couch. Powerful tools to change how power is made and routed can now be altered by anyone who knows how to log in.

These changes are great news—the world gets more energy, lower emissions, and lower prices. But these changes also highlight the kinds of vulnerabilities that brought the Colonial Pipeline to an abrupt halt. The same tools that make legitimate energy-sector workers more powerful become dangerous when hijacked by hackers. For example, hard-to-replace equipment can be given commands to shake itself to bits, putting chunks of a national grid out of commission for months at a stretch.

For many nation-states, the ability to push a button and sow chaos in a rival state’s economy is highly desirable. And the more energy infrastructure becomes hyperconnected and digitally managed, the more targets offer exactly that opportunity. It’s not surprising, then, that an increasing share of cyberattacks seen in the energy sector have shifted from targeting information technologies (IT) to targeting operating technologies (OT)—the equipment that directly controls physical plant operations. 

To stay on top of the challenge, chief information security officers (CISOs) and their security operations centers (SOCs) will have to update their approaches. Defending operating technologies calls for different strategies—and a distinct knowledge base—than defending information technologies. For starters, defenders need to understand the operating status and tolerances of their assets—a command to push steam through a turbine works well when the turbine is warm, but can break it when the turbine is cold. Identical commands could be legitimate or malicious, depending on context.

Even collecting the contextual data needed for threat monitoring and detection is a logistical and technical nightmare. Typical energy systems are composed of equipment from several manufacturers, installed and retrofitted over decades. Only the most modern layers were built with cybersecurity as a design constraint, and almost none of the machine languages used were ever meant to be compatible.

For most companies, the current state of cybersecurity maturity leaves much to be desired. Near-omniscient views into IT systems are paired with big OT blind spots. Data lakes swell with carefully collected outputs that can’t be combined into a coherent, comprehensive picture of operational status. Analysts burn out under alert fatigue while trying to manually sort benign alerts from consequential events. Many companies can’t even produce a comprehensive list of all the digital assets legitimately connected to their networks.

In other words, the ongoing energy revolution is a dream for efficiency—and a nightmare for security.

Securing the energy revolution calls for new solutions equally capable of identifying and acting on threats from both physical and digital worlds. Security operations centers will need to bring together IT and OT information flows, creating a unified threat stream. Given the scale of data flows, automation will need to play a role in applying operational knowledge to alert generation—is this command consistent with business as usual, or does context show it’s suspicious? Analysts will need broad, deep access to contextual information. And defenses will need to grow and adapt as threats evolve and businesses add or retire assets.

This month, Siemens Energy unveiled a monitoring and detection platform aimed at resolving the core technical and capability challenges for CISOs tasked with defending critical infrastructure. Siemens Energy engineers have done the legwork needed to automate a unified threat stream, allowing their offering, Eos.ii, to serve as a fusion SOC that’s capable of unleashing the power of artificial intelligence on the challenge of monitoring energy infrastructure.

AI-based solutions answer the dual need for adaptability and persistent vigilance. Machine learning algorithms trawling huge volumes of operational data can learn the expected relationships between variables, recognizing patterns invisible to human eyes and highlighting anomalies for human investigation. Because machine learning can be trained on real-world data, it can learn the unique characteristics of each production site, and can be iteratively trained to distinguish benign and consequential anomalies. Analysts can then tune alerts to watch for specific threats or ignore known sources of noise.

Extending monitoring and detection into the OT space makes it harder for attackers to hide—even when unique, zero-day attacks are deployed. In addition to examining traditional signals like signature-based detection or network traffic spikes, analysts can now observe the effects that new inputs have on real-world equipment. Cleverly disguised malware would still raise red flags by creating operational anomalies. In practice, analysts using the AI-based systems have found that their Eos.ii detection engine was sensitive enough to predictively identify maintenance needs—for example, when a bearing begins to wear out and the ratio of steam in to power out begins to drift.

Done right, monitoring and detection that spans both IT and OT should leave intruders exposed. Analysts investigating alerts can trace user histories to determine the source of anomalies, and then roll forward to see what else was changed in a similar timeframe or by the same user. For energy companies, increased precision translates to dramatically reduced risk – if they can determine the scope of an intrusion, and identify which specific systems were compromised, they gain options for surgical responses that fix the problem with minimal collateral damage—say, shutting down a single branch office and two pumping stations instead of a whole pipeline.

As energy systems continue their trend toward hyperconnectivity and pervasive digital controls, one thing is clear: a given company’s ability to provide reliable service will depend more and more on their ability to create and sustain strong, precise cyber defenses. AI-based monitoring and detection offers a promising start.

To learn more about Siemens Energy’s new AI-based monitoring and detection platform, check out their recent white paper on Eos.ii.

Learn more about Siemens Energy cybersecurity at Siemens Energy Cybersecurity.

This content was produced by Siemens Energy. It was not written by MIT Technology Review’s editorial staff.

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Why I became a TechTrekker

Published

8 months ago

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22 August 2023

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group jumps into the air with snowy mountains in the background


My senior spring in high school, I decided to defer my MIT enrollment by a year. I had always planned to take a gap year, but after receiving the silver tube in the mail and seeing all my college-bound friends plan out their classes and dorm decor, I got cold feet. Every time I mentioned my plans, I was met with questions like “But what about school?” and “MIT is cool with this?”

Yeah. MIT totally is. Postponing your MIT start date is as simple as clicking a checkbox. 

Sofia Pronina (right) was among those who hiked to the Katla Glacier during this year’s TechTrek to Iceland.

COURTESY PHOTO

Now, having finished my first year of classes, I’m really grateful that I stuck with my decision to delay MIT, as I realized that having a full year of unstructured time is a gift. I could let my creative juices run. Pick up hobbies for fun. Do cool things like work at an AI startup and teach myself how to create latte art. My favorite part of the year, however, was backpacking across Europe. I traveled through Austria, Slovakia, Russia, Spain, France, the UK, Greece, Italy, Germany, Poland, Romania, and Hungary. 

Moreover, despite my fear that I’d be losing a valuable year, traveling turned out to be the most productive thing I could have done with my time. I got to explore different cultures, meet new people from all over the world, and gain unique perspectives that I couldn’t have gotten otherwise. My travels throughout Europe allowed me to leave my comfort zone and expand my understanding of the greater human experience. 

“In Iceland there’s less focus on hustle culture, and this relaxed approach to work-life balance ends up fostering creativity. This was a wild revelation to a bunch of MIT students.”

When I became a full-time student last fall, I realized that StartLabs, the premier undergraduate entrepreneurship club on campus, gives MIT undergrads a similar opportunity to expand their horizons and experience new things. I immediately signed up. At StartLabs, we host fireside chats and ideathons throughout the year. But our flagship event is our annual TechTrek over spring break. In previous years, StartLabs has gone on TechTrek trips to Germany, Switzerland, and Israel. On these fully funded trips, StartLabs members have visited and collaborated with industry leaders, incubators, startups, and academic institutions. They take these treks both to connect with the global startup sphere and to build closer relationships within the club itself.

Most important, however, the process of organizing the TechTrek is itself an expedited introduction to entrepreneurship. The trip is entirely planned by StartLabs members; we figure out travel logistics, find sponsors, and then discover ways to optimize our funding. 

two students soaking in a hot spring in Iceland

COURTESY PHOTO

In organizing this year’s trip to Iceland, we had to learn how to delegate roles to all the planners and how to maintain morale when making this trip a reality seemed to be an impossible task. We woke up extra early to take 6 a.m. calls with Icelandic founders and sponsors. We came up with options for different levels of sponsorship, used pattern recognition to deduce the email addresses of hundreds of potential contacts at organizations we wanted to visit, and all got scrappy with utilizing our LinkedIn connections.

And as any good entrepreneur must, we had to learn how to be lean and maximize our resources. To stretch our food budget, we planned all our incubator and company visits around lunchtime in hopes of getting fed, played human Tetris as we fit 16 people into a six-person Airbnb, and emailed grocery stores to get their nearly expired foods for a discount. We even made a deal with the local bus company to give us free tickets in exchange for a story post on our Instagram account. 

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The Download: spying keyboard software, and why boring AI is best

Published

8 months ago

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22 August 2023

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🧠


This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How ubiquitous keyboard software puts hundreds of millions of Chinese users at risk

For millions of Chinese people, the first software they download onto devices is always the same: a keyboard app. Yet few of them are aware that it may make everything they type vulnerable to spying eyes. 

QWERTY keyboards are inefficient as many Chinese characters share the same latinized spelling. As a result, many switch to smart, localized keyboard apps to save time and frustration. Today, over 800 million Chinese people use third-party keyboard apps on their PCs, laptops, and mobile phones. 

But a recent report by the Citizen Lab, a University of Toronto–affiliated research group, revealed that Sogou, one of the most popular Chinese keyboard apps, had a massive security loophole. Read the full story. 

—Zeyi Yang

Why we should all be rooting for boring AI

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. It hopes they could improve intelligence and operational planning. 

But those might not be the right use cases, writes our senior AI reporter Melissa Heikkila. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases. 

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. The DoD’s best bet is to apply generative AI to more mundane things like Excel, email, or word processing. Read the full story. 

This story is from The Algorithm, Melissa’s weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

The ice cores that will let us look 1.5 million years into the past

To better understand the role atmospheric carbon dioxide plays in Earth’s climate cycles, scientists have long turned to ice cores drilled in Antarctica, where snow layers accumulate and compact over hundreds of thousands of years, trapping samples of ancient air in a lattice of bubbles that serve as tiny time capsules. 

By analyzing those cores, scientists can connect greenhouse-gas concentrations with temperatures going back 800,000 years. Now, a new European-led initiative hopes to eventually retrieve the oldest core yet, dating back 1.5 million years. But that impressive feat is still only the first step. Once they’ve done that, they’ll have to figure out how they’re going to extract the air from the ice. Read the full story.

—Christian Elliott

This story is from the latest edition of our print magazine, set to go live tomorrow. Subscribe today for as low as $8/month to ensure you receive full access to the new Ethics issue and in-depth stories on experimental drugs, AI assisted warfare, microfinance, and more.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 How AI got dragged into the culture wars
Fears about ‘woke’ AI fundamentally misunderstand how it works. Yet they’re gaining traction. (The Guardian) 
+ Why it’s impossible to build an unbiased AI language model. (MIT Technology Review)
 
2 Researchers are racing to understand a new coronavirus variant 
It’s unlikely to be cause for concern, but it shows this virus still has plenty of tricks up its sleeve. (Nature)
+ Covid hasn’t entirely gone away—here’s where we stand. (MIT Technology Review)
+ Why we can’t afford to stop monitoring it. (Ars Technica)
 
3 How Hilary became such a monster storm
Much of it is down to unusually hot sea surface temperatures. (Wired $)
+ The era of simultaneous climate disasters is here to stay. (Axios)
+ People are donning cooling vests so they can work through the heat. (Wired $)
 
4 Brain privacy is set to become important 
Scientists are getting better at decoding our brain data. It’s surely only a matter of time before others want a peek. (The Atlantic $)
+ How your brain data could be used against you. (MIT Technology Review)
 
5 How Nvidia built such a big competitive advantage in AI chips
Today it accounts for 70% of all AI chip sales—and an even greater share for training generative models. (NYT $)
+ The chips it’s selling to China are less effective due to US export controls. (Ars Technica)
+ These simple design rules could turn the chip industry on its head. (MIT Technology Review)
 
6 Inside the complex world of dissociative identity disorder on TikTok 
Reducing stigma is great, but doctors fear people are self-diagnosing or even imitating the disorder. (The Verge)
 
7 What TikTok might have to give up to keep operating in the US
This shows just how hollow the authorities’ purported data-collection concerns really are. (Forbes)
 
8 Soldiers in Ukraine are playing World of Tanks on their phones
It’s eerily similar to the war they are themselves fighting, but they say it helps them to dissociate from the horror. (NYT $)
 
9 Conspiracy theorists are sharing mad ideas on what causes wildfires
But it’s all just a convoluted way to try to avoid having to tackle climate change. (Slate $)
 
10 Christie’s accidentally leaked the location of tons of valuable art 🖼📍
Seemingly thanks to the metadata that often automatically attaches to smartphone photos. (WP $)

Quote of the day

“Is it going to take people dying for something to move forward?”

—An anonymous air traffic controller warns that staffing shortages in their industry, plus other factors, are starting to threaten passenger safety, the New York Times reports.

The big story

Inside effective altruism, where the far future counts a lot more than the present

" "

VICTOR KERLOW

October 2022

Since its birth in the late 2000s, effective altruism has aimed to answer the question “How can those with means have the most impact on the world in a quantifiable way?”—and supplied methods for calculating the answer.

It’s no surprise that effective altruisms’ ideas have long faced criticism for reflecting white Western saviorism, alongside an avoidance of structural problems in favor of abstract math. And as believers pour even greater amounts of money into the movement’s increasingly sci-fi ideals, such charges are only intensifying. Read the full story.

—Rebecca Ackermann

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ Watch Andrew Scott’s electrifying reading of the 1965 commencement address ‘Choose One of Five’ by Edith Sampson.
+ Here’s how Metallica makes sure its live performances ROCK. ($)
+ Cannot deal with this utterly ludicrous wooden vehicle. 
+ Learn about a weird and wonderful new instrument called a harpejji.



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Why we should all be rooting for boring AI

Published

8 months ago

on

22 August 2023

By

Why we should all be rooting for boring AI


This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

I’m back from a wholesome week off picking blueberries in a forest. So this story we published last week about the messy ethics of AI in warfare is just the antidote, bringing my blood pressure right back up again. 

Arthur Holland Michel does a great job looking at the complicated and nuanced ethical questions around warfare and the military’s increasing use of artificial-intelligence tools. There are myriad ways AI could fail catastrophically or be abused in conflict situations, and there don’t seem to be any real rules constraining it yet. Holland Michel’s story illustrates how little there is to hold people accountable when things go wrong.  

Last year I wrote about how the war in Ukraine kick-started a new boom in business for defense AI startups. The latest hype cycle has only added to that, as companies—and now the military too—race to embed generative AI in products and services. 

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. 

The department sees tons of potential to “improve intelligence, operational planning, and administrative and business processes.” 

But Holland Michel’s story highlights why the first two use cases might be a bad idea. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases.  

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. Everyone agrees that humans should make the final call, but that is made harder by technology that acts unpredictably, especially in fast-moving conflict situations. 

Some worry that the people lowest on the hierarchy will pay the highest price when things go wrong: “In the event of an accident—regardless of whether the human was wrong, the computer was wrong, or they were wrong together—the person who made the ‘decision’ will absorb the blame and protect everyone else along the chain of command from the full impact of accountability,” Holland Michel writes. 

The only ones who seem likely to face no consequences when AI fails in war are the companies supplying the technology.

It helps companies when the rules the US has set to govern AI in warfare are mere recommendations, not laws. That makes it really hard to hold anyone accountable. Even the AI Act, the EU’s sweeping upcoming regulation for high-risk AI systems, exempts military uses, which arguably are the highest-risk applications of them all. 

While everyone is looking for exciting new uses for generative AI, I personally can’t wait for it to become boring. 

Amid early signs that people are starting to lose interest in the technology, companies might find that these sorts of tools are better suited for mundane, low-risk applications than solving humanity’s biggest problems.

Applying AI in, for example, productivity software such as Excel, email, or word processing might not be the sexiest idea, but compared to warfare it’s a relatively low-stakes application, and simple enough to have the potential to actually work as advertised. It could help us do the tedious bits of our jobs faster and better.

Boring AI is unlikely to break as easily and, most important, won’t kill anyone. Hopefully, soon we’ll forget we’re interacting with AI at all. (It wasn’t that long ago when machine translation was an exciting new thing in AI. Now most people don’t even think about its role in powering Google Translate.) 

That’s why I’m more confident that organizations like the DoD will find success applying generative AI in administrative and business processes. 

Boring AI is not morally complex. It’s not magic. But it works. 

Deeper Learning

AI isn’t great at decoding human emotions. So why are regulators targeting the tech?

Amid all the chatter about ChatGPT, artificial general intelligence, and the prospect of robots taking people’s jobs, regulators in the EU and the US have been ramping up warnings against AI and emotion recognition. Emotion recognition is the attempt to identify a person’s feelings or state of mind using AI analysis of video, facial images, or audio recordings. 

But why is this a top concern? Western regulators are particularly concerned about China’s use of the technology, and its potential to enable social control. And there’s also evidence that it simply does not work properly. Tate Ryan-Mosley dissected the thorny questions around the technology in last week’s edition of The Technocrat, our weekly newsletter on tech policy.

Bits and Bytes

Meta is preparing to launch free code-generating software
A version of its new LLaMA 2 language model that is able to generate programming code will pose a stiff challenge to similar proprietary code-generating programs from rivals such as OpenAI, Microsoft, and Google. The open-source program is called Code Llama, and its launch is imminent, according to The Information. (The Information) 

OpenAI is testing GPT-4 for content moderation
Using the language model to moderate online content could really help alleviate the mental toll content moderation takes on humans. OpenAI says it’s seen some promising first results, although the tech does not outperform highly trained humans. A lot of big, open questions remain, such as whether the tool can be attuned to different cultures and pick up context and nuance. (OpenAI)

Google is working on an AI assistant that offers life advice
The generative AI tools could function as a life coach, offering up ideas, planning instructions, and tutoring tips. (The New York Times)

Two tech luminaries have quit their jobs to build AI systems inspired by bees
Sakana, a new AI research lab, draws inspiration from the animal kingdom. Founded by two prominent industry researchers and former Googlers, the company plans to make multiple smaller AI models that work together, the idea being that a “swarm” of programs could be as powerful as a single large AI model. (Bloomberg)

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