New tool empowers users to fight online misinformation

New tool empowers users to fight online misinformation

Most people agree that the spread of online misinformation is a serious problem. But there is much less consensus on what to do about it.

Many proposed solutions focus on how social media platforms can or should moderate content their users post, to prevent misinformation from spreading.

“But this approach puts a critical social decision in the hands of for-profit companies. It limits the ability of users to decide who they trust. And having platforms in charge does nothing to combat misinformation users come across from other online sources,” says Farnaz Jahanbakhsh SM ’21, PhD ’23, who is currently a postdoc at Stanford University.

She and MIT Professor David Karger have proposed an alternate strategy. They built a web browser extension that empowers individuals to flag misinformation and identify others they trust to assess online content.

Their decentralized approach, called the Trustnet browser extension, puts the power to decide what constitutes misinformation into the hands of individual users rather than a central authority. Importantly, the universal browser extension works for any content on any website, including posts on social media sites, articles on news aggregators, and videos on streaming platforms.

Through a two-week study, the researchers found that untrained individuals could use the tool to effectively assess misinformation. Participants said having the ability to assess content, and see assessments from others they trust, helped them think critically about it.

“In today’s world, it’s trivial for bad actors to create unlimited amounts of misinformation that looks accurate, well-sourced, and carefully argued. The only way to protect ourselves from this flood will be to rely on information that has been verified by trustworthy sources. Trustnet presents a vision of how that future could look,” says Karger.

Jahanbakhsh, who conducted this research while she was an electrical engineering and computer science (EECS) graduate student at MIT, and Karger, a professor of EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), detail their findings in a paper presented this week at the ACM Conference on Human Factors in Computing Systems.

Fighting misinformation

This new paper builds off their prior work about fighting online misinformation. The researchers built a social media platform called Trustnet, which enabled users to assess content accuracy and specify trusted users whose assessments they want to see.

But in the real world, few people would likely migrate to a new social media platform, especially when they already have friends and followers on other platforms. On the other hand, calling on social media companies to give users content-assessment abilities would be an uphill battle that may require legislation. Even if regulations existed, they would do little to stop misinformation elsewhere on the web.

Instead, the researchers sought a platform-agnostic solution, which led them to build the Trustnet browser extension.

Extension users click a button to assess content, which opens a side panel where they label it as accurate, inaccurate, or question its accuracy. They can provide details or explain their rationale in an accompanying text box.

Users can also identify others they trust to provide assessments. Then, when the user visits a website that contains assessments from these trusted sources, the side panel automatically pops up to show them.

In addition, users can choose to follow others beyond their trusted assessors. They can opt to see content assessments from those they follow on a case-by-case basis. They can also use the side panel to respond to questions about content accuracy.

“But most content we come across on the web is embedded in a social media feed or shown as a link on an aggregator page, like the front page of a news website. Plus, something we know from prior work is that users typically don’t even click on links when they share them,” Jahanbakhsh says.

To get around those issues, the researchers designed the Trustnet Extension to check all links on the page a user is reading. If trusted sources have assessed content on any linked pages, the extension places indictors next to those links and will fade the text of links to content deemed inaccurate.

One of the biggest technical challenges the researchers faced was enabling the link-checking functionality since links typically go through multiple redirections. They were also challenged to make design decisions that would suit a variety of users.

Differing assessments

To see how individuals would utilize the Trustnet Extension, they conducted a two-week study where 32 individuals were tasked with assessing two pieces of content per day.

The researchers were surprised to see that the content these untrained users chose to assess, such as home improvement tips or celebrity gossip, was often different from content assessed by professionals, like news articles. Users also said they would value assessments from people who were not professional fact-checkers, such as having doctors assess medical content or immigrants assess content related to foreign affairs.

“I think this shows that what users need and the kinds of content they consider important to assess doesn’t exactly align with what is being delivered to them. A decentralized approach is more scalable, so more content could be assessed,” Jahanbakhsh says.

However, the researchers caution that letting users choose whom to trust could cause them to become trapped in their own bubble and only see content that agrees with their views.

This issue could be mitigated by identifying trust relationships in a more structured way, perhaps by suggesting a user follow certain trusted assessors, like the FDA.

In the future, Jahanbakhsh wants to further study structured trust relationships and the broader implications of decentralizing the fight against misinformation. She also wants to extend this framework beyond misinformation. For instance, one could use the tool to filter out content that is not sympathetic to a certain protected group.

“Less attention has been paid to decentralized approaches because some people think individuals can’t assess content,” she says. “Our studies have shown that is not true. But users shouldn’t just be left helpless to figure things out on their own. We can make fact-checking available to them, but in a way that lets them choose the content they want to see.”

Scientists use generative AI to answer complex questions in physics

Scientists use generative AI to answer complex questions in physics

When water freezes, it transitions from a liquid phase to a solid phase, resulting in a drastic change in properties like density and volume. Phase transitions in water are so common most of us probably don’t even think about them, but phase transitions in novel materials or complex physical systems are an important area of study.

To fully understand these systems, scientists must be able to recognize phases and detect the transitions between. But how to quantify phase changes in an unknown system is often unclear, especially when data are scarce.

Researchers from MIT and the University of Basel in Switzerland applied generative artificial intelligence models to this problem, developing a new machine-learning framework that can automatically map out phase diagrams for novel physical systems.

Their physics-informed machine-learning approach is more efficient than laborious, manual techniques which rely on theoretical expertise. Importantly, because their approach leverages generative models, it does not require huge, labeled training datasets used in other machine-learning techniques.

Such a framework could help scientists investigate the thermodynamic properties of novel materials or detect entanglement in quantum systems, for instance. Ultimately, this technique could make it possible for scientists to discover unknown phases of matter autonomously.

“If you have a new system with fully unknown properties, how would you choose which observable quantity to study? The hope, at least with data-driven tools, is that you could scan large new systems in an automated way, and it will point you to important changes in the system. This might be a tool in the pipeline of automated scientific discovery of new, exotic properties of phases,” says Frank Schäfer, a postdoc in the Julia Lab in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-author of a paper on this approach.

Joining Schäfer on the paper are first author Julian Arnold, a graduate student at the University of Basel; Alan Edelman, applied mathematics professor in the Department of Mathematics and leader of the Julia Lab; and senior author Christoph Bruder, professor in the Department of Physics at the University of Basel. The research is published today in Physical Review Letters.

Detecting phase transitions using AI

While water transitioning to ice might be among the most obvious examples of a phase change, more exotic phase changes, like when a material transitions from being a normal conductor to a superconductor, are of keen interest to scientists.

These transitions can be detected by identifying an “order parameter,” a quantity that is important and expected to change. For instance, water freezes and transitions to a solid phase (ice) when its temperature drops below 0 degrees Celsius. In this case, an appropriate order parameter could be defined in terms of the proportion of water molecules that are part of the crystalline lattice versus those that remain in a disordered state.

In the past, researchers have relied on physics expertise to build phase diagrams manually, drawing on theoretical understanding to know which order parameters are important. Not only is this tedious for complex systems, and perhaps impossible for unknown systems with new behaviors, but it also introduces human bias into the solution.

More recently, researchers have begun using machine learning to build discriminative classifiers that can solve this task by learning to classify a measurement statistic as coming from a particular phase of the physical system, the same way such models classify an image as a cat or dog.

The MIT researchers demonstrated how generative models can be used to solve this classification task much more efficiently, and in a physics-informed manner.

The Julia Programming Language, a popular language for scientific computing that is also used in MIT’s introductory linear algebra classes, offers many tools that make it invaluable for constructing such generative models, Schäfer adds.

Generative models, like those that underlie ChatGPT and Dall-E, typically work by estimating the probability distribution of some data, which they use to generate new data points that fit the distribution (such as new cat images that are similar to existing cat images).

However, when simulations of a physical system using tried-and-true scientific techniques are available, researchers get a model of its probability distribution for free. This distribution describes the measurement statistics of the physical system.

A more knowledgeable model

The MIT team’s insight is that this probability distribution also defines a generative model upon which a classifier can be constructed. They plug the generative model into standard statistical formulas to directly construct a classifier instead of learning it from samples, as was done with discriminative approaches.

“This is a really nice way of incorporating something you know about your physical system deep inside your machine-learning scheme. It goes far beyond just performing feature engineering on your data samples or simple inductive biases,” Schäfer says.

This generative classifier can determine what phase the system is in given some parameter, like temperature or pressure. And because the researchers directly approximate the probability distributions underlying measurements from the physical system, the classifier has system knowledge.

This enables their method to perform better than other machine-learning techniques. And because it can work automatically without the need for extensive training, their approach significantly enhances the computational efficiency of identifying phase transitions.

At the end of the day, similar to how one might ask ChatGPT to solve a math problem, the researchers can ask the generative classifier questions like “does this sample belong to phase I or phase II?” or “was this sample generated at high temperature or low temperature?”

Scientists could also use this approach to solve different binary classification tasks in physical systems, possibly to detect entanglement in quantum systems (Is the state entangled or not?) or determine whether theory A or B is best suited to solve a particular problem. They could also use this approach to better understand and improve large language models like ChatGPT by identifying how certain parameters should be tuned so the chatbot gives the best outputs.

In the future, the researchers also want to study theoretical guarantees regarding how many measurements they would need to effectively detect phase transitions and estimate the amount of computation that would require.

This work was funded, in part, by the Swiss National Science Foundation, the MIT-Switzerland Lockheed Martin Seed Fund, and MIT International Science and Technology Initiatives.

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2024 MIT Supply Chain Excellence Awards given to 35 undergraduates

2024 MIT Supply Chain Excellence Awards given to 35 undergraduates

The MIT Supply Chain Management Master’s Program has recognized 35 exceptional students from eight renowned undergraduate programs specializing in supply chain management and engineering across the United States.

Presented annually, the MIT Supply Chain Excellence Awards honor undergraduate students who have demonstrated outstanding talent in supply chain management or industrial engineering. These students originate from institutions that have partnered with the MIT Center for Transportation and Logistics’ Supply Chain Management master’s program to expand opportunities for graduate study and advance the field of supply chain and logistics.

In this year’s awards, the MIT SCM Master’s Program has provided over $900,000 in fellowship funding to 35 deserving recipients. These students come from respected schools like Arizona State University, the University of Illinois at Urbana-Champaign, Lehigh University, Michigan State University, Monterrey Institute of Technology and Higher Education in Mexico, Penn State University, Purdue University, and Texas A&M University.

Recipients can use their awards by applying to the MIT SCM program after gaining two to five years of professional experience post-graduation. The fellowship funds can be applied toward tuition fees for the SCM master’s program at MIT or at MIT Supply Chain and Logistics Excellence (SCALE) network centers in Spain, Malaysia, Luxembourg, or China.

Founded in 1973, MIT CTL is one of the world’s leading supply chain education and research centers. MIT CTL coordinates more than 100 supply chain research efforts across the MIT campus and around the globe. The center also educates students and corporate leaders in the essential principles of supply chain management and helps organizations to increase productivity and improve their environmental performance.

Founded in 1998 by the MIT CTL, MIT SCM attracts a diverse group of talented and motivated students from across the globe. Students work directly with researchers and industry experts on complex and challenging problems in all aspects of supply chain management. MIT SCM students propel their classroom and laboratory learning straight into industry. They graduate from our programs as thought leaders ready to engage in an international, highly competitive marketplace.

Q&A: Exploring ethnic dynamics and climate change in Africa

Q&A: Exploring ethnic dynamics and climate change in Africa

Evan Lieberman is the Total Professor of Political Science and Contemporary Africa at MIT, and is also director of the Center for International Studies. During a semester-long sabbatical, he’s currently based at the African Climate and Development Initiative at the University of Cape Town.

In this Q&A, Lieberman discusses several climate-related research projects he’s pursuing in South Africa and surrounding countries. This is part of an ongoing series exploring how the School of Humanities, Arts, and Social Sciences is addressing the climate crisis.

Q: South Africa is a nation whose political and economic development you have long studied and written about. Do you see this visit as an extension of the kind of research you have been pursuing, or a departure from it?

A: Much of my previous work has been animated by the question of understanding the causes and consequences of group-based disparities, whether due to AIDS or Covid. These are problems that know no geographic boundaries, and where ethnic and racial minorities are often hardest hit. Climate change is an analogous problem, with these minority populations living in places where they are most vulnerable, in heat islands in cities, and in coastal areas where they are not protected. The reality is they might get hit much harder by longer-term trends and immediate shocks.

In one line of research, I seek to understand how people in different African countries, in different ethnic groups, perceive the problems of climate change and their governments’ response to it. There are ethnic divisions of labor in terms of what people do — whether they are farmers or pastoralists, or live in cities. So some ethnic groups are simply more affected by drought or extreme weather than others, and this can be a basis for conflict, especially when competing for often limited government resources.

In this area, just like in my previous research, learning what shapes ordinary citizen perspectives is really important, because these views affect people’s everyday practices, and the extent to which they support certain kinds of policies and investments their government makes in response to climate-related challenges. But I will also try to learn more about the perspectives of policymakers and various development partners who seek to balance climate-related challenges against a host of other problems and priorities.

Q: You recently published “Until We Have Won Our Liberty,” which examines the difficult transition of South Africa from apartheid to a democratic government, scrutinizing in particular whether the quality of life for citizens has improved in terms of housing, employment, discrimination, and ethnic conflicts. How do climate change-linked issues fit into your scholarship?

A: I never saw myself as a climate researcher, but a number of years ago, heavily influenced by what I was learning at MIT, I began to recognize more and more how important the issue of climate change is. And I realized there were lots of ways in which the climate problem resonated with other kinds of problems I had tackled in earlier parts of my work.

There was once a time when climate and the environment was the purview primarily of white progressives: the “tree huggers.” And that’s really changed in recent decades as it has become evident that the people who’ve been most affected by the climate emergency are ethnic and racial minorities. We saw with Hurricane Katrina and other places [that] if you are Black, you’re more likely to live in a vulnerable area and to just generally experience more environmental harms, from pollution and emissions, leaving these communities much less resilient than white communities. Government has largely not addressed this inequity. When you look at American survey data in terms of who’s concerned about climate change, Black Americans, Hispanic Americans, and Asian Americans are more unified in their worries than are white Americans.

There are analogous problems in Africa, my career research focus. Governments there have long responded in different ways to different ethnic groups. The research I am starting looks at the extent to which there are disparities in how governments try to solve climate-related challenges.

Q: It’s difficult enough in the United States taking the measure of different groups’ perceptions of the impact of climate change and government’s effectiveness in contending with it. How do you go about this in Africa?

A: Surprisingly, there’s only been a little bit of work done so far on how ordinary African citizens, who are ostensibly being hit the hardest in the world by the climate emergency, are thinking about this problem. Climate change has not been politicized there in a very big way. In fact, only 50 percent of Africans in one poll had heard of the term.

In one of my new projects, with political science faculty colleague Devin Caughey and political science doctoral student Preston Johnston, we are analyzing social and climate survey data [generated by the Afrobarometer research network] from over 30 African countries to understand within and across countries the ways in which ethnic identities structure people’s perception of the climate crisis, and their beliefs in what government ought to be doing. In largely agricultural African societies, people routinely experience drought, extreme rain, and heat. They also lack the infrastructure that can shield them from the intense variability of weather patterns. But we’re adding a lens, which is looking at sources of inequality, especially ethnic differences.

I will also be investigating specific sectors. Africa is a continent where in most places people cannot take for granted universal, piped access to clean water. In Cape Town, several years ago, the combination of failure to replace infrastructure and lack of rain caused such extreme conditions that one of the world’s most important cities almost ran out of water.

While these studies are in progress, it is clear that in many countries, there are substantively large differences in perceptions of the severity of climate change, and attitudes about who should be doing what, and who’s capable of doing what. In several countries, both perceptions and policy preferences are differentiated along ethnic lines, more so than with respect to generational or class differences within societies.

This is interesting as a phenomenon, but substantively, I think it’s important in that it may provide the basis for how politicians and government actors decide to move on allocating resources and implementing climate-protection policies. We see this kind of political calculation in the U.S. and we shouldn’t be surprised that it happens in Africa as well.

That’s ultimately one of the challenges from the perch of MIT, where we’re really interested in understanding climate change, and creating technological tools and policies for mitigating the problem or adapting to it. The reality is frustrating. The political world — those who make decisions about whether to acknowledge the problem and whether to implement resources in the best technical way — are playing a whole other game. That game is about rewarding key supporters and being reelected.

Q: So how do you go from measuring perceptions and beliefs among citizens about climate change and government responsiveness to those problems, to policies and actions that might actually reduce disparities in the way climate-vulnerable African groups receive support?

A: Some of the work I have been doing involves understanding what local and national governments across Africa are actually doing to address these problems. We will have to drill down into government budgets to determine the actual resources devoted to addressing a challenge, what sorts of practices the government follows, and the political ramifications for governments that act aggressively versus those that don’t. With the Cape Town water crisis, for example, the government dramatically changed residents’ water usage through naming and shaming, and transformed institutional practices of water collection. They made it through a major drought by using much less water, and doing it with greater energy efficiency. Through the government’s strong policy and implementation, and citizens’ active responses, an entire city, with all its disparate groups, gained resilience. Maybe we can highlight creative solutions to major climate-related problems and use them as prods to push more effective policies and solutions in other places.

In the MIT Global Diversity Lab, along with political science faculty colleague Volha Charnysh, political science doctoral student Jared Kalow, and Institute for Data, Systems and Society doctoral student Erin Walk, we are exploring American perspectives on climate-related foreign aid, asking survey respondents whether the U.S. should be giving more to people in the global South who didn’t cause the problems of climate change but have to suffer the externalities. We are particularly interested in whether people’s desire to help vulnerable communities rests on the racial or national identity of those communities.

From my new seat as director of the Center for International Studies (CIS), I hope to do more and more to connect social science findings to relevant policymakers, whether in the U.S. or in other places. CIS is making climate one of our thematic priority areas, directing hundreds of thousands of dollars for MIT faculty to spark climate collaborations with researchers worldwide through the Global Seed Fund program. 

COP 28 (the U.N. Climate Change Conference), which I attended in December in Dubai, really drove home the importance of people coming together from around the world to exchange ideas and form networks. It was unbelievably large, with 85,000 people. But so many of us shared the belief that we are not doing enough. We need enforceable global solutions and innovation. We need ways of financing. We need to provide opportunities for journalists to broadcast the importance of this problem. And we need to understand the incentives that different actors have and what sorts of messages and strategies will resonate with them, and inspire those who have resources to be more generous.

John Joannopoulos receives 2024-2025 Killian Award

John Joannopoulos receives 2024-2025 Killian Award

John Joannopoulos, an innovator and mentor in the fields of theoretical condensed matter physics and nanophotonics, has been named the recipient of the 2024-2025 James R. Killian Jr. Faculty Achievement Award.

Joannopoulos is the Francis Wright Davis Professor of Physics and director of MIT’s Institute for Soldier Nanotechnologies. He has been a member of the MIT faculty for 50 years.

“Professor Joannopoulos’s profound and lasting impact on the field of theoretical condensed matter physics finds its roots in his pioneering work in harnessing ab initio physics to elucidate the behavior of materials at the atomic level,” states the award citation, which was announced at today’s faculty meeting by Roger White, chair of the Killian Award Selection Committee and professor of philosophy at MIT. “His seminal research in the development of photonic crystals has revolutionized understanding of light-matter interactions, laying the groundwork for transformative advancements in diverse fields ranging from telecommunications to biomedical engineering.”

The award also honors Joannopoulos’ service as a “legendary mentor to generations of students, inspiring them to achieve excellence in science while at the same time facilitating the practical benefit to society through entrepreneurship.”

The Killian Award was established in 1971 to recognize outstanding professional contributions by MIT faculty members. It is the highest honor that the faculty can give to one of its members.

“I have to tell you, it was a complete and utter surprise,” Joannopoulos told MIT News shortly after he received word of the award. “I didn’t expect it at all, and was extremely flattered, honored, and moved by it, frankly.”

Joannopoulous has spent his entire professional career at MIT. He came to the Institute in 1974, directly after receiving his PhD in physics at the University of California at Berkeley, where he also earned his bachelor’s degree. Starting out as an assistant professor in MIT’s Department of Physics, he quickly set up a research program focused on theoretical condensed matter physics.

Over the first half of his MIT career, Joannopoulos worked to elucidate the fundamental nature of the electronic, vibrational, and optical structure of crystalline and amorphous bulk solids, their surfaces, interfaces, and defects. He and his students developed numerous theoretical methods to enable tractable and accurate calculations of these complex systems.

In the 1990s, his work with microscopic material systems expanded to a new class of materials, called photonic crystals — materials that could be engineered at the micro- and nanoscale to manipulate light in ways that impart surprising and exotic optical qualities to the material as a whole.

“I saw that you could create photonic crystals with defects that can affect the properties of photons, in much the same way that defects in a semiconductor affect the properties of electrons,” Joannopoulos says. “So I started working in this area to try and explore what anomalous light phenomena can we discover using this approach?”

Among his various breakthroughs in the field was the realization of a “perfect dielectic mirror” — a multilayered optical device that reflects light from all angles as normal metallic mirrors do, and that can also be tuned to reflect and trap light at specific frequencies. He and his colleagues saw potential for the mirror to be made into a hollow fiber that could serve as a highly effective optical conduit, for use in a wide range of applications. To further advance the technology, he and his colleagues launched a startup, which has since developed the technology into a flexible, fiber-optic “surgical scalpel.”

Throughout his career, Joannopoulos has helped to launch numerous startups and photonics-based technologies.

“His ability to bridge the gap between academia and industry has not only advanced scientific knowledge but also led to the creation of dozens of new companies, thousands of jobs, and groundbreaking products that continue to benefit society to this day,” the award citation states.

In 2006, Joannopoulos accepted the position as director of MIT’s Institute for Soldier Nanotechnologies (ISN), a collaboration between MIT researchers, industry partners, and military defense experts, who seek innovations to protect and enhance soldiers’ survivability in the field. In his role as ISN head, Joannopoulos has worked across MIT, making connections and supporting new projects with researchers specializing in fields far from his own.

“I get a chance to explore and learn fascinating new things,” says Joannopoulos, who is currently overseeing projects related to hyperspectral imaging, smart and responsive fabrics, and nanodrug delivery. “I love that aspect of really getting to understand what people in other fields are doing. And they’re doing great work across many, many different fields.”

Throughout his career at MIT, Joannopoulos has been especially inspired and motivated by his students, many of whom have gone on to found companies, lead top academic and research institutions, and make significant contributions to their respective fields, including one student who was awarded the Nobel Prize in Physics in 1998.

“One’s proudest moments are the successes of one’s students, and in that regard, I’ve been extremely lucky to have had truly exceptional students over the years,” Joannopolous says.

His many contributions to academia and industry have earned Joannopoulos numerous honors and awards, including his election to both the National Academy of Sciences and the American Academy of Arts and Sciences. He is also a fellow of both the American Physical Society and the American Association for the Advancement of Science.

“The Selection Committee is delighted to have this opportunity to honor Professor John Joannopoulos: a visionary scientist, a beloved mentor, a great believer in the goodness of people, and a leader whose contributions to MIT and the broader scientific community are immeasurable,” the award citation concludes.

Lost in translation | Abbreviations and acronyms in cyber security – CyberTalk

Lost in translation | Abbreviations and acronyms in cyber security – CyberTalk

By Antoinette Hodes, a Check Point Global Solutions Architect and Evangelist with the Check Point Office of the CTO.

Cyber security professionals commonly throw around industry-specific acronyms in a bid to simplify communication and to save time. But linguistic shortcuts have their disadvantages, especially when it comes to communicating across teams…

In the way that a poorly executed baseball pitch can result in a weak hit or near-miss, sloppy linguistic use can also result in near cyber security misses or flagrant failures. The impact on an organization can be tremendous, leading to millions of dollars in damages.

Don’t believe it? Keep reading. In this article, we’ll explore best practices to mitigate the surprisingly extensive risk associated with cyber security linguistic shortcuts.

Miscommunication as cyber threat

More than half of top-tier managers (62%) admit that a miscommunication with the IT department or IT security team has resulted in at least one cyber security incident in their organization.

Deliberately using abbreviations and acronyms can become a sort of “security through obscurity”. No one has the exact formula to deduce the cost of miscommunication, but it’s known that miscommunications can heavily impact your bottom line, recovery time and reputation in a negative way.

The loss of context and meaning challenge

A real threat is the business language barrier between cyber security and other teams. If communications are unclear between the security and network teams, for example, the result could be a critical delay in reaction time, which could lead to outsized cyber security consequences.

Miscommunication cyber lingo example

Consider this line: While the ZTA and ZTNA models ensure secure network access, but leave applications defenseless, the ZTAA model prioritizes secure application access.

Meaning: While the Zero Trust Access and Zero Trust Network Access models ensure secure network access, but leave applications defenseless, the Zero Trust Application Access model prioritizes secure application access (Stakeholders mistakenly interchanged the terms ZTNA and ZTAA, leading to confusion about the specific security controls being discussed).

More acronym-based cyber lingo examples

  • IoMT can mean Internet of Medical Things or Internet of Military Things.
  • BIoT can mean Battlespace Internet of Things or Blockchain Internet of Things
  • CIoT can mean Consumer IoT or Cognitive Internet of Things

Prioritize communication clarity

In preparation for a cyber security incident, it’s especially important to maintain a jargon and acronym free communication plan. For acronyms and abbreviations that are used in such plans, it is recommended to have a high-level technical fact sheet ready. It should describe all abbreviations and acronyms.

Basically, unclear communication, usage of acronyms and abbreviations, represent hidden costs. The financial implications tied to inefficient communication haven’t been quantified — nor the savings efficient communication might bring. However, these are crucial concepts to consider when trying to elevate how your teams work together and the business implications of communication failures.

A good CISO would translate technical jargon to financial examples, maybe even accompanied with infographics and flowcharts (a picture paints a 1,000 words), to simplify complex concepts.

For more cyber security insights from Antoinette Hodes, click here. Lastly, subscribe to the CyberTalk.org newsletter for timely insights, cutting-edge analyses and more, delivered straight to your inbox each week.