A researcher explores how AI systems can sound confident while being dangerously wrong, examining security vulnerabilities in automated code repairs, agentic AI systems, and benchmark evaluations across domains.
Influential Woman · Academia
Manar Alsaid
Assistant Professor, East Texas A&M University
Commerce, TX 75428
8Years experience
1Article published
5Awards received
Her Story
About Manar
Manar Alsaid serves as an Assistant Professor at East Texas A&M University. She specializes in cybersecurity, AI, and data science, with her research published in reputable journals. Alsaid earned her PhD in Information Science with a concentration in Data Science from the University of North Texas in 2024. She previously completed a master's degree at the same university and holds a bachelor's degree in Computer Science from Al-Balqa Applied University in Jordan. Her academic journey includes serving as a teaching assistant for two years and as a teaching fellow and instructor for an additional two years while pursuing her doctorate. She also holds an AI Fundamentals Certificate from Microsoft and an Oracle Certificate. Alsaid attributes her success to balancing her PhD studies and research with raising four children, resulting in publications in strong journals and a position at a top-ranked university system.
Her Interview
Ten minutes with Manar
01What do you attribute your success to?
Persistence and consistency. I started my career as a computer lab supervisor in Jordan and completed a PhD in Texas while raising four children. Neither happened quickly. Several of my grant proposals were rejected before one was awarded, and I submitted the next one anyway. Mentors mattered too; three years as an associate editor at the Journal of Information & Knowledge Management taught me more about research than any course could. Mostly, though, my children are the reason I keep going, not the reason it was hard.
Staying curious has mattered as much as working hard. For example, my field moves quickly. Cybersecurity, AI, and data science are constantly evolving, and the only way to remain useful to students is to keep learning in public, attending workshops, earning certifications, serving on review panels, and presenting work that is not yet finished. I never assumed the PhD was the end of my training.
02What’s the best career advice you’ve ever received?
ake the opportunity before you feel qualified for it. Almost everything meaningful in my career arrived earlier than I felt prepared to teach a course I had not taught, to present at my first international conference, to develop a new degree program, and to serve on an NSF review panel. If I had waited until I felt fully ready, I would still be waiting. Readiness is something you acquire on the way, not a prerequisite you satisfy in advance
03What advice would you give to young women entering your industry?
Someone told me early on to treat rejection as information rather than judgment. That has been the most useful thing I have carried. I have submitted grant proposals that were declined, some after reaching final review. Each one taught me something about how reviewers read, and the next proposal was stronger for it. The award I eventually received came after several that did not. In academia, the people who last are not the ones who avoid rejection. They are the ones who read it carefully and resubmit it.
Three things. First, learn to be visible. Good work does not announce itself in this field; present it, publish it, put it where people can find it. Second, find people who will advocate for you when you are not in the room, and be that person for someone else. Almost every opportunity I have had came through someone who thought of me. Third, do not wait to feel qualified. Also, keep learning after the degree. At the end, do not accept the idea that you must choose between a family and this career. I finished my doctorate while raising four children. It was hard, but my children were never the reason I nearly stopped.
04What are the biggest challenges or opportunities in your field right now?
The biggest challenge is that we are deploying AI faster than we can verify it. My recent research examines large language models tasked with fixing security flaws in infrastructure code, and we found they often produce fixes that pass automated scanners while leaving the vulnerability in place. The tool reports success; the system is still exposed. That gap between apparent correctness and real correctness runs through everything right now, code, medical decision support, and the systems filtering information for millions of people. It is not only a technical problem. It is a question of trust.
The opportunity is that this work no longer belongs solely to computer science. I collaborate with colleagues in various fields such as medicine, engineering, and public health because deciding what a system should do is not a question that engineers can answer on their own.
05What values are most important to you in your work and personal life?
The values that guide me most are integrity, curiosity, perseverance, and service. In academia, I believe integrity means being honest in my research, fair in my work with students and colleagues, and taking responsibility for the impact of the knowledge and technologies I help create.
Curiosity keeps me learning. In a field like computer science and AI, where technology changes so quickly, I believe you cannot stop learning if you want to remain relevant. Perseverance is equally important because meaningful research and academic growth rarely happen without setbacks. I also value service and giving back. I believe that success is not only about what you achieve individually, but also about the people you help along the way. Whether through teaching, research, mentoring, or professional service, I try to create opportunities for others to learn, grow, and contribute.
In both my professional and personal life, I try to lead with respect, responsibility, and a commitment to continuous growth. These values shape not only the work I do, but also the kind of example I want to set for the students and young women who are building their own futures.
Her Content Hub
Articles by Manar
Women Shaping the Future of Technology Era: Why Our Voices Matter
How one researcher is bridging the gap between AI confidence and correctness—and opening doors for the next generation.
Confidence Is Not the Same as Being Right
When I started my doctorate, I was studying people, not machines. The subject was something I called social noise, which is the distortion a message picks up as it moves through social media. A public health message goes out about masking during a pandemic, and by the time it reaches most people, it has been reshaped by argument, bad-faith reposting, and ordinary misunderstanding. I studied masking guidance during COVID-19, the way the Black Lives Matter message changed as it spread, and how people talked about climate during extreme weather. Some of that ran in the Journal of Information Science and the Journal of Information and Knowledge Management, and a paper I gave at the Association for Information Science and Technology on measuring the uncertainty involved was later ranked in the top ten percent of most-viewed papers the journal published that year.
Looking back, I was studying one thing the whole time. A message can arrive sounding completely certain and still be wrong. The confidence and the accuracy come apart.
That gap is now the central problem in cybersecurity, and it is where nearly all of my research has gone.
When the security fix isn't a fix
A lot of organizations have started handing their security problems to language models. You have a vulnerability somewhere in your infrastructure code, so you ask the model to repair it, and it hands back something that looks like a repair. My colleagues and I wanted to know whether those repairs hold up.
Often, they don't, and the way they fail is what worries me. The model returns code that satisfies the automated security scanner while leaving the vulnerability sitting there. Your check comes back clean. Your pipeline moves ahead. The hole is still open. We started calling these deceptive fixes, and we built a layered oracle framework to catch them, along with a taxonomy of the ways this failure shows up, because a scanner was never designed to notice a repair that only looks like one. The work has been accepted at Empirical Software Engineering and at the International Conference on Tools with Artificial Intelligence, and I am now extending it to look at what these AI-generated repairs change in your infrastructure that nobody asked them to change.
I find this problem genuinely serious. Automated repair is being adopted quickly because it saves an enormous amount of time, and the time savings are real. What is also real is that the verification layer we would need in order to trust it does not exist yet. We are building the plane in the air.
Securing systems that act on their own
The next version of this problem is already arriving. Agentic AI systems don't just answer questions; they take actions, call other tools, and chain those actions together without a person approving each step. Every one of those handoffs is a place where something can be injected, misdirected, or quietly corrupted, and the existing security models weren't built with any of it in mind.
I am working on a multi-tiered security framework for agentic AI called AONIX, which received a collaboration award from the Texas A&M Engineering Experiment Station this year. My colleagues and I have also published a meta-review of where cybersecurity is heading over the next several years, pulling together what the literature actually agrees on and where it doesn't. That kind of survey isn't glamorous work, but you cannot defend a field you haven't mapped.
Whether the numbers mean anything
Underneath the security work is a question about evaluation that keeps surfacing. When a model tops a benchmark, what has it actually demonstrated?
We applied a validity-centered evaluation framework to current frontier models and found that leading on one benchmark tells you much less about general capability than the field assumes. This is not an academic quibble. Agencies and companies are choosing which systems to deploy in security-sensitive settings on the strength of exactly these numbers, and if the numbers don't generalize, the deployment decisions inherit that weakness.
I have been asking a version of the same question about misinformation, testing how well AI models detect it when you give them defined social noise constructs to work with, which brings my old research and my current research into the same paper. And in medical imaging, where a confidently wrong answer is more dangerous than an uncertain one, I work on calibration in breast ultrasound segmentation so that a system's confidence tracks how reliable it actually is.
Different domains, one question. The system sounds sure of itself. Somebody has to check whether it should be.
Where the next people come from
The teaching side of this matters as much to me. I teach information security, digital forensics, and big data at East Texas A&M, and I have worked on proposals for a master's in cybersecurity, a master's in information science, and new AI degree programs, because students cannot enter a field their university doesn't teach.
I am also putting together an NSF proposal to build a cybersecurity and AI workforce pipeline aimed at rural regions. The argument is simple. These are among the fastest-growing jobs in the country, the shortage of qualified people is severe, and rural students are almost entirely missing from the routes that lead into them. The ability is there. The pathway isn't.
But building programs is the easy part. What actually changes a student's direction is being treated as a researcher slightly before you have earned the title.
So I put my students on my papers. Two of my students here are co-authors on work submitted to an IEEE conference on classifying movement using pose estimation and neural networks. Another is a co-author on our paper about automating clinical trial enrollment, which I presented in Lisbon. Another presented her own poster at the Texas A&M Global Cyber Research Institute last October, and I stood at the back watching her explain her research to people twenty years further along than she is, visibly nervous, doing it anyway. She was entirely capable of being there. What she needed was for someone to get her into the room.
That is most of what mentoring turns out to be. Advice is easy to come by and worth roughly what you pay for it. Access is harder. It means saying someone's name in a meeting when she isn't there to say it herself, or asking her to take something on a little before she thinks she is ready, because almost nobody feels ready, and the waiting is where careers quietly stop.
I finished my PhD while raising four children, and I started out running a computer lab at a college in Irbid, Jordan. People sometimes tell that back to me as a story about determination. It was partly that. It was also a story about specific people who opened specific doors at moments I could not have engineered on my own, and I try not to be sentimental about how much of it depended on them.
Which is why I open them now. If you are a woman looking at cybersecurity or AI and wondering whether there is room in it, I can tell you there is an enormous amount of technology in the world that nobody has examined carefully, and not nearly enough people willing to do the examining.
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