DEV Community: Md Tauhid Hossain Rubel

Disinformation Is a Cyberattack, and US Leaders Should Treat It That Way

Md Tauhid Hossain Rubel — Sat, 03 Oct 2026 17:05:45 +0000

By Md. Tauhid Hossain Rubel
Doctoral Researcher | Artificial Intelligence, Data Analytics, Cybersecurity & Financial Intelligence
United States

This is the LinkedIn summary of my full Medium article. Read the full version here: https://mdtauhidhossainrubel.medium.com/disinformation-is-a-cyberattack-635a98343c1c?sharedUserId=mdtauhidhossainrubel

Foreign influence campaigns look like a communication problem. Underneath, they use hacker methods. Attackers take over accounts, build fake profiles, rent hidden servers, and post in a planned way. The FBI Internet Crime Complaint Center logged 1,008,597 complaints and $20.877 billion in losses in 2025, up 26% from 2024 (FBI IC3, 2026). This summary shows the pattern, two federal cases, and one step that any business or agency can take now.

Keywords: Disinformation; Cybersecurity; National Security; U.S. Economy; Critical Infrastructure; Public Trust

Why This Matters to Business Leaders

Leaders already know that a data breach can hurt revenue and reputation. Fewer leaders know that a false story can do the same. A fake post about a product failure, a fake claim about a power outage, or a leaked and edited email can spread in hours.

The national picture shows the scale. IC3 received more than one million complaints in 2025, close to 3,000 a day (Morris, 2026). Losses were $10.3 billion in 2023, $16.6 billion in 2024, and $20.877 billion in 2025 (Fox Baltimore, 2026). The 2025 report also included an AI section for the first time, with 22,364 complaints and about $893 million in losses (FBI IC3, 2026).

Most of this money is lost to criminals and not to governments. Still, the tools are shared. Phishing, fake profiles, and social engineering are the base of both. When you block them, you cut both risks.

The Gap in Many Organizations

Most firms have two teams that rarely meet. The security team watches networks. The communications team watches the public. A state sponsored campaign is built to sit between them. An attacker breaks into an account, and then uses it to carry a message. The security team sees a login problem. The communications team sees a rumor. Nobody sees one incident.

CISA has said that foreign actors can mix influence operations with cyber activity, and that this needs a response by the whole organization (CISA, 2022). This is the key idea. The answer is not a bigger post removal team. The answer is one incident process.

Five Steps of the Attack
A disinformation campaign follows a chain that security teams already know.
Recon. The attacker studies a topic or a target.
Access. The attacker uses phishing or stolen passwords to take over real accounts.
Build. The attacker creates fake profiles and the servers needed to register them.
Amplify. The accounts post and share each other in a planned way.
Impact. People lose trust, send money, or share data. Stolen material may be leaked at a chosen time.

The five steps shared by disinformation campaigns and cyberattacks.
Two Real Cases
The Russian bot farm, 2024. The Justice Department seized two domain names and searched 968 accounts on X (UPI, 2024). The accounts were made with AI software called Meliorator, and many claimed to be American (The Hacker News, 2024). The project was tied to an employee of RT, a Russian state owned outlet. FBI Director Christopher Wray called it “a first in disrupting a Russian-sponsored Generative AI-enhanced social media bot farm” (Associated Press, 2024).

The Iranian hack and leak, 2024. The FBI, ODNI, and CISA said Iran was behind a hack of the Trump campaign, and that Iranian linked actors also targeted people tied to the Harris campaign (Nextgov, 2024). Later, the Justice Department charged three Iranian hackers linked to the Islamic Revolutionary Guard Corps. The charges said they took over personal accounts and tried to pass stolen material to media outlets and to people tied to the other campaign (Secureblink, 2024).

What Is Changing in 2026

Federal support is shifting. The Center for Democracy and Technology reports that CISA has cut more than a third of its workforce since February 2025 and halted most election programs (Center for Democracy and Technology, 2026). In 2026, Senator Mark Warner asked DHS what CISA is doing to warn local officials about malign influence, and he cited testimony that foreign adversaries are expected to target the 2026 elections (Nextgov, 2026).

DHS released a 13 page plan in late September 2026 for the November 3 midterms, covering cyber and physical threats and Russian disinformation (CiberCuba, 2026). Leaders should follow how it is carried out.
For companies and local agencies, the message is practical. Do not wait for outside help. Build the basic plan yourself.

One Step to Take Now

Write an MDM incident response plan, as CISA advises (CISA, 2022). Keep it to one or two pages.
• Name one leader for the plan.
• Name who speaks to the public.
• Name who watches logs for fake or hijacked accounts.
• Link the plan to your normal incident process, using the respond and communicate functions of NIST CSF 2.0 (NIST, 2024).
• Turn on multi factor authentication for staff and brand accounts, as the FBI advises (TechRadar, 2024).
• Run one drill a year with security and communications together.

Protect free speech while you do this. Focus on fake identities, stolen accounts, and hidden foreign control, and leave lawful opinion alone.

What Leaders Often Get Wrong

They treat it as a one time event. A false story may come back many times, and each wave may use new accounts. A standing plan works better than a one time fix.

They wait for a platform to act. Platforms help, but the first harm often lands on your own customers and staff. Own your response.

They focus only on the post. The post is the visible part. The hidden part is the account, the server, and the login. If you watch only the post, you miss the source.

They forget the human side. Phishing is still the easiest way in. A short staff lesson on fake emails and fake login pages is one of the best low cost steps you can take. WaterISAC told its members to prioritize defenses against phishing, credential compromise, and financial fraud (WaterISAC, 2026).

A Quick Test for Your Team

Ask your team three questions this week.

If a fake post about our company went viral tonight, who would lead the response?
If a staff member lost control of a brand account, how fast could we lock it?
Do our security and communications teams share one contact list and one plan? If the answer to any of these is not clear, you have found your first task.

What Good Looks Like in 90 Days

In the first month, finish the basics. List your official accounts, lock them with multi factor authentication, and name the three roles in your plan.

In the second month, test the plan. Hold a short drill that starts with a fake post and a fake phishing email at the same time. See how fast the two teams find each other. Write down what went wrong.

In the third month, improve. Fix the gaps, add alerts for your brand name, and share the plan with your board or leadership group. Plan the next drill.

This is not a large project. It is a habit. Organizations that build the habit will be calmer and faster when a real campaign arrives.

Why It Is a National Interest

This approach protects economic growth by cutting fraud and false stories. It supports national security by protecting critical infrastructure. It builds resilience through clear plans. It supports U.S. leadership, since the bot farm case involved Canadian and Dutch partners (Security Boulevard, 2024). It also builds a workforce that can read both network logs and social patterns.

Conclusion
Disinformation is not only a message problem. It is an attack that uses stolen access, hidden servers, and fake people. If leaders write the plan, name the owners, protect the accounts, and practice, they will protect their organizations and the trust that supports the U.S. economy.

Read the full article with more data, cases, and references on Medium: https://mdtauhidhossainrubel.medium.com/disinformation-is-a-cyberattack-635a98343c1c?sharedUserId=mdtauhidhossainrubel

Expert Voices
• Jen Easterly, former CISA Director, on preparing for foreign influence. Source: Wash100
• Christopher Wray, former FBI Director, on the AI bot farm. Source: Associated Press via ClickOrlando
• Matthew Olsen, Assistant Attorney General, on Iranian hack and leak. Source: Secureblink
• Adrian Fontes, Arizona Secretary of State, on federal election support. Source: Brennan Center

Declaration of Original Work
I, Md. Tauhid Hossain Rubel, declare that this article is not copied from any other source. I collected facts and figures from public data sources such as the FBI IC3, CISA, the Department of Justice, and news reports, and I list each one in the references. I wrote the analysis with the help of AI. Short quotes are marked and linked to their sources.

References
Associated Press. (2024, July 9). US disrupts Russian government-backed disinformation campaign that relied on AI technology. ClickOrlando. https://www.clickorlando.com/news/world/2024/07/09/us-disrupts-russian-government-backed-disinformation-campaign-that-relied-on-ai-technology/
Center for Democracy and Technology. (2026). Countdown to the midterms: Mapping the rapid evolution of election security. https://cdt.org/insights/countdown-to-the-midterms-mapping-the-rapid-evolution-of-election-security/
CiberCuba. (2026, September 25). The U.S. presents a plan to protect the 2026 elections against cyberattacks and physical threats. https://en.cibercuba.com/noticias/2026-09-25-u1-e208574-s27061-nid341173-eeuu-presenta-plan-proteger-elecciones-2026-ataques
Cybersecurity and Infrastructure Security Agency. (2022). Preparing for and mitigating foreign influence operations targeting critical infrastructure (CISA Insights). https://www.cisa.gov/mdm-resource-library
Federal Bureau of Investigation, Internet Crime Complaint Center. (2026). 2025 Internet crime report. https://www.ic3.gov/AnnualReport/Reports/2025_IC3Report.pdf
Fox Baltimore. (2026). Americans lost over $20 billion in 2025 to online scams, FBI report finds. https://foxbaltimore.com/news/nation-world/americans-lost-over-20-billion-in-2025-to-online-scams-fbi-report-finds-cryptocurrency-online
Morris, L. (2026). FBI cybercrime report reveals $20.8 billion lost in 2025. National CIO Review. https://nationalcioreview.com/articles-insights/extra-bytes/fbi-cybercrime-report-reveals-20-8-billion-lost-in-2025/
National Institute of Standards and Technology. (2024). The NIST cybersecurity framework (CSF) 2.0 (NIST CSWP 29). https://www.nist.gov/cyberframework
Nextgov. (2024, August 20). US agencies conclude Iran was behind hack targeting Trump campaign. https://www.nextgov.com/cybersecurity/2024/08/us-agencies-conclude-iran-was-behind-hack-targeting-trump-campaign/398935/
Nextgov. (2026, May). Senator warns CISA election security pullback could leave midterms vulnerable. https://www.nextgov.com/cybersecurity/2026/05/senator-warns-cisa-election-security-pullback-could-leave-midterms-vulnerable/413378/
Secureblink. (2024). Iranian hackers indicted for 2024 U.S. election hack and leak plot. https://www.secureblink.com/threat-feeds/iranian-hackers-indicted-for-2024-u-s-election-hack-and-leak-plot
Security Boulevard. (2024). DOJ shutters massive Russian bot farm spreading disinformation. https://securityboulevard.com/2024/07/doj-shutters-massive-russian-bot-farm-spreading-disinformation
TechRadar. (2024). FBI says it is sure Iran was to blame for Trump campaign hack. https://www.techradar.com/pro/iran-behind-trump-hack-fbi-confirms
The Hacker News. (2024). U.S. seizes domains used by AI-powered Russian bot farm for disinformation. https://thehackernews.com/2024/07/us-seizes-domains-used-by-ai-powered.html
UPI. (2024, July 10). U.S. foils Russian AI-enhanced bot farm of nearly 1,000 X accounts. https://www.upi.com/amp/Top_News/US/2024/07/10/DOJ-foils-Russian-bot-farm/5311720595912/
WaterISAC. (2026, April 9). FBI’s IC3 releases 2025 Internet Crime Report. https://www.waterisac.org/?p=65669

Author Note
Md. Tauhid Hossain Rubel is a doctoral researcher in Artificial Intelligence, Data Analytics, Cybersecurity, and Financial Intelligence. His work focuses on protecting U.S. economic resilience, critical infrastructure, and public trust through data driven security.

Can AI Really Detect Fake News? What the Research Shows

Md Tauhid Hossain Rubel — Fri, 02 Oct 2026 22:13:38 +0000

Why I Looked Into This

I work with data every day, and I wanted to know one thing. Can AI models tell real news from AI-generated fakes? The short answer is yes, sometimes. The longer answer is more interesting, and it has real lessons for anyone building detection tools.

This article summarizes what recent research says, with links to the sources.

The Problem in Plain Terms

Large language models can write news stories that look real. That makes it harder to spot false content. Many developers assume a classifier trained on labeled data will solve the problem. The research shows that the story is more complicated.

Lab Scores Can Be Misleading

A 2026 study trained a BERT-based classifier on 3,600 Turkish news articles. It reached a 97.08% F1 score. The catch is that the model detected AI-written text, not false claims. If you build a tool, be clear about which task you are solving.

Another benchmark tested models on news from after their training cutoff. A zero-shot method scored 74.50% accuracy. A newer method reached 83.92%. The gap between the two shows how much results depend on the test data.

Subtle Fakes Are the Hard Case

Fully invented stories are easy for advanced models to catch. Fakes that mix small truths with careful lies are much harder. If your dataset only has obvious fakes, your model will look better than it really is.

Bias Shows Up in Small Details

A 2026 study tested six large language models on the LIAR benchmark. The researchers changed only the speaker's job title, using neutral, male, or female forms. Between 9.79% and 35.13% of statements received different labels. If your pipeline uses an LLM as a judge, test it for this kind of sensitivity.

Users Can Lose Their Skills

An MIT Media Lab study followed 67 people over four weeks. With AI help, participants were 21 percent more accurate. Without it, their unassisted scores dropped 15 percentage points by week four. The researchers found that AI that asks guiding questions supports better learning than AI that only gives answers.

For product builders, this is a design lesson. A tool that explains its reasoning may be better for users than one that only returns a label.

A Simple Checklist for Builders

Test on fresh data, not only on old datasets. Report accuracy by topic and by speaker group. Publish your error cases. Design for explanation, not just a score. Never let the model be the only judge.

Final Thoughts

AI can help detect fake news, but it should support human judgment rather than replace it. If you are building in this space, focus on real-world testing, fairness checks, and transparent design.

What the Experts Say

Researcher Anku Rani warns that users get excited about these "magical" LLMs but forget that they are statistical models. Her point is that a chatbot predicts likely words, and it does not truly know what is real.
Valdemar Danry, another co-lead author, explains the design choice clearly. He says that AIs that tell by providing direct answers are more likely to foster reliance. Tools that ask questions may be slower, but they build better judgment.

Maes, a senior author of the study, adds a broader warning. She says people need to know that if they delegate their thinking, they will not get better at that kind of problem solving.

A detection researcher quoted by TechXplore put the problem in one sentence. He asked what good is a system that boasts 95% accuracy in the lab but fails under real-life conditions. That question sums up the challenge.

Where Detection Tools Fall Short

Detection tools struggle in several situations. They often miss context, such as the true meaning of an image. They can also fail during emotionally charged breaking news, when facts change by the hour. MIT researchers noted that AI models are especially vulnerable to mistakes during major events, when misinformation spreads fast.

Training data adds another limit. Human-written news that trains these models can be biased or unreliable itself. A tool that learns from flawed data may repeat those flaws with confidence.

Why the United States Should Care

False news can affect more than one reader. It can move markets, influence elections, and weaken public trust in institutions. It can also become a security concern when foreign groups use AI to spread believable propaganda.

The National Institute of Standards and Technology has published an AI Risk Management Framework that focuses on trust and safety. Applying that framework to news detection is a practical step for organizations that rely on AI.

Looking Ahead

AI will keep getting better at writing, and it will keep getting better at checking. The tools of the next few years will need regular testing, fair design, and honest reporting of their limits. The best result will come from people and machines working together, with people staying in charge of final decisions.

Practical Ways to Evaluate Your Own Detector

If you plan to ship a detector, start with a clear evaluation plan. Split your data by time, so the test set contains stories that came after the training set. This reveals whether your model can handle new events instead of memorizing old ones.

Next, measure calibration. A model that says it is 90% sure should be right about nine times out of ten. If it is not, users may trust wrong labels too much.

Then run a sensitivity test. Change small details, such as job titles, source names, or regional terms, and check whether the label changes. Any change that should not matter is a warning sign.

Finally, add a human review step for high-stakes cases. Send uncertain items to a person rather than forcing a decision. This keeps your system honest about its limits.

Read the full article on Medium: https://medium.com/@mdtauhidhossainrubel/can-ai-models-tell-real-news-from-ai-generated-fakes-3433ddc3481f?sharedUserId=mdtauhidhossainrubel

References
Conner-Simons, A. (2026, June 9). The consequences of relying on AI for accurate news. MIT News. https://news.mit.edu/2026/consequences-of-relying-on-ai-for-accurate-news-0609
Chalehchaleh, R., et al. (2026). Unequal verdicts: Investigating gender bias in LLM-based fake news detection. arXiv:2608.03627. https://arxiv.org/abs/2608.03627
Ozdemir, O. (2026). From perceptions to evidence. arXiv:2602.13504. https://arxiv.org/pdf/2602.13504

AI-Based Real-Time Detection of Financial Misinformation in U.S. Stock Markets

Md Tauhid Hossain Rubel — Tue, 28 Apr 2026 05:17:34 +0000

By Md. Tauhid Hossain Rubel | Cybersecurity & FinTech ResearcherPublished on DEV | Full Research Article Available on Medium

Why Developers and Tech Professionals Need to Care About This
Most people think financial fraud is a problem for regulators and lawyers. But the systems that detect, flag, and stop financial misinformation are built by engineers, data scientists, and cybersecurity professionals. If you work with NLP, graph databases, machine learning, or real-time data pipelines, this problem sits directly inside your skill set. In April 2025, one fake tariff story caused the S&P 500 to surge 8.5% in thirty minutes, adding and then erasing $3.6 trillion in market value. According to Deloitte, AI-enabled financial fraud losses in the U.S. are projected to grow from $12.3 billion in 2023 to $40 billion by 2027. The technical community has both the tools and the responsibility to help solve this.

How Misinformation Breaks Financial Markets
False information moves faster than any human oversight system can respond. A fake image of a Pentagon explosion in 2023 dropped the Dow Jones by 80 basis points in seconds before fact-checkers could act. Pump-and-dump schemes now operate through coordinated bot networks on WhatsApp, Reddit, and Telegram. Between September and October 2025, the SEC suspended trading on nine NASDAQ-listed companies due to suspected social-media-driven price manipulation. Research published in January 2025 confirms that irrational social media sentiment drives significant negative effects across the S&P 500, NASDAQ, and Russell 2000 indices. The core technical challenge is that misinformation travels at machine speed while detection still largely happens at human speed.

The Four-Layer AI Detection Framework
In my full Medium article I propose a practical, layered AI architecture to close that gap. Here is the technical summary.

Layer 1: NLP Sentiment Analysis. A fine-tuned financial language model such as FinFakeBERT or FinBERT scores incoming social media and news content in real time. GPT-4 based models already achieve an F1 score of 0.87 on noisy financial social media datasets, outperforming CNN and RNN models significantly.

Layer 2: Graph Neural Network Coordination Detection. A dynamic graph maps relationships between accounts posting about specific securities. Synchronized posting patterns, bot clusters, and coordinated campaigns are flagged before they reach peak manipulation velocity.

Layer 3: Cross-Market Anomaly Detection. Social media sentiment spikes are cross-referenced against live NASDAQ and NYSE trading data including price, volume, bid-ask spread, and order book anomalies. When a 500% buzz increase on a penny stock matches an unexplained volume doubling, the system raises an alert.

Layer 4: Automated Regulatory Notification. Structured evidence reports are automatically generated and pushed to SEC and FINRA surveillance teams with confidence scores, account clusters, targeted securities, and timeline data. The SEC already holds authority under Section 12(k) of the Securities Exchange Act of 1934 to suspend trading within hours. AI makes triggering that authority faster and more precise.

Read the Full Research on Medium
This DEV post covers the technical architecture. But the full Medium article goes further with complete case studies, SEC enforcement breakdowns, behavioral analysis of pump-and-dump mechanics, a full data table with references, and a proposed implementation roadmap for financial institutions and regulators.

👉 Read the full article on Medium: AI-Based Real-Time Detection of Financial Misinformation in U.S. Stock Markets https://medium.com/@mrubel.student/ai-based-real-time-detection-of-financial-misinformation-in-u-s-stock-markets-16064da437f4

If you are working on NLP pipelines, graph-based anomaly detection, financial data APIs, or regulatory tech, this article will give you both the context and the technical framing to understand where your work fits inside one of the most urgent problems in modern finance.

Conclusion
The infrastructure to detect financial misinformation in real time already exists inside the tools most developers use every day. NLP, graph networks, streaming data pipelines, and automated alerting are all mature technologies. What is missing is integration at regulatory scale. The gap between what is technically possible and what is actually deployed is where real people are losing real money every single day. That gap is a technical problem. And technical problems have technical solutions.

Artificial Intelligence, Big Data in American Health Finance: Fraud Prevention and Cyber Risk

Md Tauhid Hossain Rubel — Thu, 15 Jan 2026 06:15:14 +0000

The High Stakes of American Healthcare Spending
American healthcare spending is the highest in the world. It is a system that costs trillions of dollars each year. In 2022, the United States spent about 4.5 trillion dollars on health care. This number comes from the official Centers for Medicare and Medicaid Services, or CMS (CMS, 2023). That massive spending equals more than 17 percent of the entire country's economy. To put it simply, for every five dollars the nation makes, almost one dollar goes to healthcare. This spending touches everyone. It affects government budgets, business costs, and family finances. The size of this financial system makes it a critical issue for public welfare. Managing this money wisely is not just an economic task, it is a matter of national importance.

The Financial Challenges: Rising Costs, Fraud, and Waste
The healthcare system faces several big money problems. First, costs keep rising faster than inflation. Prices for drugs, hospital stays, and insurance go up every year. Second, the system is full of complex paperwork and different prices. This creates inefficiencies, meaning money is wasted on administrative tasks instead of patient care. But one of the most serious problems is fraud and abuse. Criminals steal billions from public health programs like Medicare and Medicaid every year. The National Health Care Anti-Fraud Association, a respected industry group, estimates that healthcare fraud costs the nation at least 68 billion dollars annually, though the true total could be much higher (NHCAA, 2022). This fraud drains money that should pay for care for the elderly, the poor, and veterans. It makes healthcare more expensive for everyone. Solving these challenges is why people are turning to new technology.

How Big Data and AI Enter the Picture
Big data means the huge amount of information created in healthcare. Think of every patient record, insurance claim, pharmacy bill, and doctor's note. Artificial Intelligence, or AI, is computer software that can find patterns and learn from this data. Together, they are powerful tools. In finance, they are used to model costs, predict trends, and catch thieves. In healthcare finance, they do the same thing. They help understand where money goes, find waste, and stop fraud before more dollars are lost. This is a shift from reacting to problems, to predicting and preventing them. It is a smarter way to protect public money.

Using Analytics for Cost Modelling and Prediction
Before you can control costs, you need to understand them. Big data analytics helps build cost models. These are like financial maps of the healthcare system. For example, analysts can use data to predict which patients are likely to have very expensive hospital visits next year. They look at past diagnoses, medication use, and hospital records. A study in the journal Health Affairs showed that these predictive models can accurately find patients who need help to avoid costly health crises (Figueroa et al., 2021). Insurance companies and government programs use these models. They can then reach out to these high-risk patients. They might provide more nurse check-ins or help with medications. The goal is to keep people healthier and avoid a big hospital bill. This saves money and improves lives. It is a better use of resources.

AI as a Detective: Fighting Medicare and Medicaid Fraud
Finding fraud in millions of complex claims is like finding a needle in a haystack. AI is the new super-powered magnet. Traditional methods relied on audits and tips, which were slow. Now, AI systems can look at every claim in real-time. They are trained to spot strange patterns that humans would miss. For example, an AI might flag a doctor who bills for an impossible 48 hours of procedures in a single day. Or it might see a pharmacy that always bills for the most expensive drug, never a cheaper alternative. The U.S. Department of Health and Human Services uses an AI-powered system called the Fraud Prevention System. In just one year, this system identified or prevented 1.5 billion dollars in improper payments (HHS OIG, 2021). The AI does not make arrests. It alerts human investigators. It gives them a prioritized list of the most suspicious cases to review. This makes the fight against fraud faster and much more effective.

The Critical Role of Cybersecurity and Data Integrity
All of this depends on one thing: trust in the data. If patient information is stolen or changed, the entire system fails. Healthcare data is a top target for hackers. They want to steal records to sell or to use for identity theft. They also use ransomware. This is malicious software that locks a hospital's computers until a ransom is paid. A single attack can shut down a whole hospital network. This is a direct threat to patient safety and financial stability. In 2023, a major attack on a company called Change Healthcare paralyzed billing and payments across the country. It was a wake-up call. The American Hospital Association called it "the most significant cyberattack on the U.S. healthcare system" (AHA, 2024). Protecting data is not just an IT issue. It is the foundation for everything. Without secure and accurate data, AI models make wrong predictions, and fraud detection systems fail. Cybersecurity is what keeps the fuel in the engine clean.

Linking to the National Interest: Public Health and Cost Containment
Why should the average person care about this? Because it touches core national interests. First, public health. When fraud steals money from Medicare, that is less money for grandma's cancer treatment. When a ransomware attack closes a hospital, people cannot get emergency care. Second, cost containment. Every dollar lost to fraud or a cyberattack adds to the nation's healthcare bill. This leads to higher taxes, higher insurance premiums, and higher out-of-pocket costs. Using AI and big data wisely is a way to defend the system. It helps ensure tax dollars buy real care. It helps make the system more efficient so it can serve more people. As healthcare executive Michael J. Dowling has said, "The future of healthcare is going to be about leveraging data. Data is the new currency, and it will drive better outcomes and lower costs" (Northwell Health, 2022). This work protects both our health and our wallets.

The Latest News and Developments
The field is moving fast. In recent news, government agencies are pushing harder for AI tools. In April 2024, the White House announced new policies for the safe use of AI in critical sectors, including healthcare (The White House, 2024). This shows the government sees both the promise and the risk. Also, following the Change Healthcare attack, new proposed rules from CMS would require stronger cybersecurity plans from hospitals (CMS, 2024). On the fraud front, the Justice Department now regularly announces major busts where AI tools helped crack the case. For example, a 2023 case involved a scheme that billed over 900 million dollars for unnecessary genetic tests, uncovered through data analysis (DOJ, 2023). The message is clear: the race is on. Offenders are using technology to steal, and defenders must use even better technology to stop them.

Conclusion: A Guarded Optimism
The use of Artificial Intelligence and Big Data in American health finance offers a powerful path forward. It provides smart tools to model costs, hunt fraud, and protect precious data. The potential to save billions and make care more efficient is real. But this technology is not a magic solution. It requires good data, constant vigilance against cyber threats, and careful human oversight to avoid errors or bias. The goal is not just a cheaper system, but a smarter, fairer, and more resilient one. In the fight to control healthcare costs and protect public funds, AI and data analytics have become essential allies. The future of American healthcare finance will depend on how wisely we use them.

References with Links
Centers for Medicare & Medicaid Services (CMS). (2023). National Health Expenditure Data 2022 Highlights. Retrieved from https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data/historical

National Health Care Anti-Fraud Association (NHCAA). (2022). The Problem of Health Care Fraud. Retrieved from https://www.nhcaa.org/resources/health-care-anti-fraud-resources/the-challenge-of-health-care-fraud/

Figueroa, J. F., et al. (2021). Association of Risk Modeling and Care Management With Outcomes. Health Affairs. Retrieved from https://www.healthaffairs.org/doi/10.1377/hlthaff.2020.02205

U.S. Department of Health and Human Services, Office of Inspector General (HHS OIG). (2021). Fraud Prevention System Return on Investment. Retrieved from https://oig.hhs.gov/reports-and-publications/workplan/summary/wp-summary-0000541.asp

American Hospital Association (AHA). (2024). AHA Letter on Change Healthcare Cyberattack. Retrieved from https://www.aha.org/lettercomment/2024-02-29-aha-letter-change-healthcare-cyberattack

Northwell Health. (2022). Michael Dowling on the Future of Healthcare. Retrieved from https://www.northwell.edu/news/the-latest/michael-dowling-on-the-future-of-health-care

The White House. (2024). FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence. Retrieved from https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/

Centers for Medicare & Medicaid Services (CMS). (2024). Proposed Rule on Cybersecurity. Retrieved from https://www.cms.gov/newsroom/press-releases/biden-harris-administration-proposes-cybersecurity-requirements-hospitals

U.S. Department of Justice (DOJ). (2023). Justice Department Charges Dozens in $900 Million Health Care Fraud Schemes. Retrieved from https://www.justice.gov/opa/pr/justice-department-charges-dozens-900-million-health-care-fraud-schemes

Compliance 4.0: Integrating Finance, Data and Cyber in U.S. Firms

Md Tauhid Hossain Rubel — Mon, 29 Dec 2025 04:07:56 +0000

A Strategic Blueprint for Economic Resilience and Competitive Advantage

By Md. Tauhid Hossain Rubel
Graduate Researcher | Data Analytics, Finance & Cybersecurity
United States
Executive Summary
U.S. corporations deal with a complex regulatory environment of financial regulations, data privacy laws and ever-present cyber threats. A big problem is that the teams working in these areas tend to work in isolation. This separation breeds inefficiencies, blind spots and more vulnerability. This old way of doing things cannot keep pace with modern and interconnected risks or with the sophisticated tools now utilized by such regulators as the SEC and CISA. This article examines the need for "Compliance 4.0." This is a new and integrated model where data analytics integrates finance, data protection and cybersecurity into one coherent system. This shift is an issue of national interest. It is essential to protecting the US financial system and defending critical infrastructure, as well as to developing a culture of proactive risk management. Moving from archaic checklists to ongoing, data-driven oversight, companies will be able to increase economic competitiveness, aid national security and lead in regulatory technology innovation. This analysis makes it clear for U.S. business leaders and policymakers to establish stronger and more adaptive organizations.

Keywords: U.S Economy; Financial Stability; Cybersecurity; Data Integration; Regulatory Compliance; Risk Management

Introduction: The National Need For Integration
The health of the United States economy is now linked with the secure and legal movement of data. Financial transactions, corporate reports and information about customers all pass through digital networks. A major challenge has emerged with large companies. Their financial compliance, data governance and cybersecurity teams will often all work in silos. This siloed structure implies gaps in oversight, slow response to problems and failure to use shared information for better risk management. This internal weakness is a national concern as it can be exploited. It endangers the integrity of markets, the privacy of consumers and the strength of fundamental economic sectors. As the U.S. regulators themselves are more sophisticated in their data analysis for the purposes of supervision, the difference between their capabilities and a company's outdated compliance methods is becoming larger. This article contends that the next step - Compliance 4.0 - requires American firms to develop fully integrated programs. They need to employ unified data and analytics to satisfy the modern regulatory requirements and gain a sustainable edge.

Problem Statement: The Cost of Silos
The basic problem is that the old, compartmentalized way of doing things for compliance in American business is broken. It is not commensurate with the connected risks of today or what regulators expect from now. For instance, a bank's fake detection team, office, and customer data management team, and a hacker defence centre may use different software, generate different reports, and report to different bosses. This fragmentation implies that warning signs are missed. A single issue, such as an untruthful employee, may cause separate alerts in financial records and computer logs, which nobody ever makes the link to. Current systems lack this because they are blind to the whole picture. The risks of failure to change are serious. They include financial crime that goes undetected and weakens trust, enormous data breaches due to poor management of data, and lack of speed in complying with new regulations from different areas, such as cyber incident reporting. This inefficiency eventually undermines the strength of the American corporate world.

Background: A Complicated Regulatory Landscape
U.S. companies are operating in a multi-layered regulatory environment. Financial institutions report to such regulatory agencies as the Securities and Exchange Commission (SEC) and the Office of the Comptroller of the Currency (OCC), following legal guidelines such as Dodd-Frank. The privacy of data is regulated by state laws such as the California Consumer Privacy Act (CCPA) and as part of the oversight of the Federal Trade Commission (FTC). Cybersecurity rules derive from different sources, such as specific regulations in the industry and frameworks from the National Institute of Standards and Technology (NIST). In the past, each of these areas developed their own set of compliance practices. However, in the digital age, the demarcation between finance, data and cyber has become blurred. What happened to one place is a direct impact in the other, and there is a greater need for a unified approach.

**Core Analysis: The Urge Towards Unification of View

The Need for Connection: Regulatory** U.S. regulators are no longer taking issues in isolation. They are actively correlating events in the world of finance and cybersecurity and expect the same capability in the companies. Enforcement actions are now pointing to failures between these domains on a regular basis. For example, SEC sued companies for providing investors with misleading information regarding cyberattacks, making cybersecurity directly related to honest financial reporting. In one 2023 case the SEC fined a software company for its inaccurate disclosures related to a ransomware attack and emphasized the importance of having internal controls that bridge the gap between the IT and finance departments (SEC, 2023). This indicates that regulators are looking at a cyber incident not as a technical problem but as a major business event with real financial consequences. Companies, therefore, need to have processes in place that will ensure their security teams are able to quickly and accurately inform their financial reporting teams about significant events.

2. Creating the Integrated Compliance Architecture
The basis for Compliance 4.0 is common data and common analysis tools. The first important step is the creation of a unified data repository. This system would consolidate information from trading platforms, network security logs, data trackers and customer privacy requests. A 2023 industry survey by Deloitte revealed that 72% of compliance leaders regard integrating data across risk areas as their greatest priority, but only 35% have a unified strategy in place (Deloitte, 2023). Once data is connected, companies are able to use analytics to identify correlations that were not previously visible. An algorithm could detect, for example, if the suspicious profits of a trader are coincident with that employee's unauthorized access to confidential company reports on the corporate network. This is an insight that is impossible if the data is locked away in separate department silos.

The 3-layer compliance 4.0 Framework:

Foundation: A Unified Data Governance. This layer provides one source of truth for all compliance-related data using technologies such as cloud data lakes in order to provide a single source of truth.

Intelligence: Cross Domain Analytics. Here, patterns and risks across financial, data and cyber activities are identified using tools, such as security information and event management (SIEM), with complex correlation rules.

Automated Reporting & Controls Testing (Assurance). This top layer has the benefit of being source of demonstrable proof of compliance to the regulators through automation and continues monitoring.

3. Strategic Benefit of Integration
Adopting Compliance 4.0 has clear benefits in strengthening individual firms and the broader US economy. Economically, it greatly reduces the cost and redundancy of having three separate compliance programs. It also cuts back on regulatory fines and operational downtime from major incidents; protecting the value of shareholders. From a security point of view, an integrated program is more robust. It enables quicker and more informed responses to incidents, as it gives a full picture of the impact of an attack - from which data was stolen to whether it can impact market stability. This is a direct contribution to national objectives of hardening economic infrastructure. Furthermore, this model generates demand for a new hybrid professional who is professional in data science, regulation and security. It also drives innovation in the American RegTech industry as companies look for a solution for this integrated approach.

4. Dealing with the Challenges of Implementation
The process of moving to integration is not without hurdles. Centralising sensitive compliance data makes it a prime target and the compliance system itself needs strong cybersecurity. There is also a risk of overwhelming the staff with too many alerts if the analytics are not carefully managed. The most difficult challenge is often an organizational one. Success requires removing long-standing departmental boundaries and requires strong leadership from the top to align the goals of the CFO, CISO and General Counsel. Looking into the future, Compliance 4.0 will likely become Predictive Governance in 5-10 years time. By applying machine learning to integrated data, firms will not merely find current problems but anticipate areas of future vulnerability to allow them to fix the problem before it turns into a crisis.

*Justification *
Advancing compliance 4.0 is clearly in the national interest of the United States. First, it helps to strengthen U.S. economic competitiveness by making major corporations more efficient, secure, and stable, which leads to attracting investment and growth. Second, it directly improves national security by developing corporate defenses that are smarter and more coordinated to make it more difficult for adversaries to disrupt the nation's economic foundations. Third, it promotes US leadership in establishing global standards. By leading the way in integrated compliance models, American businesses and regulators can export models that encourage transparency, security, and innovation around the globe.

Implications for Practice & Recommendations
For U.S. Industry Leaders:

Appoint a senior executive who has power over finance, data and cyber compliance to promote integration.

One way to quickly get a win is to launch a pilot project to integrate data from one financial and one security process to demonstrate quick value.

Invest in cross-training programs so that there is mutual understanding between compliance, IT security and data privacy teams.

For U.S. Policy makers & Regulators:

Issue joint guidance from relevant agencies encouraging and setting out expectations on integrated risk management.

Modernize examination procedures to examine the efficacy of connections between firm compliance efforts across traditional areas.

Support regulatory "sandboxes" in which companies might successfully test new integrated compliance technologies.

Conclusion
Compliance 4.0 is an evolution that American firms need. The integrated approach of finance, data and cybersecurity is no longer optional but a very important requirement due to interconnected risks and data-savvy regulators. By developing programs on unified data and shared analytics, corporations will be able to transform compliance from a scattered cost to a source of strength and insight. This changeover will insure the individual companies and through this insure the stability and security of the entire U.S. economic system. For America to retain its competitive advantage, its top entities must adopt this connected future.

References
Deloitte Center for Regulatory Strategy. (2023). The future of regulatory technology: From fragmentation to integration. Deloitte Insights.

Securities and Exchange Commission (SEC). SEC fines Software Company for Misleading Disclosure about Cyberattack [Press Release].

The New CFO Toolkit: Data, AI, and Cyber Risk in U.S. Corporate Finance

Md Tauhid Hossain Rubel — Sat, 27 Dec 2025 06:22:22 +0000

I remember sitting with a CFO during a quarterly review where the numbers were undeniably strong. Despite the profit, the mood was tense. The concern wasn't revenue; it was speed and risk. The CFO leaned back and said, "We get reports too late and threats too fast." That moment stayed with me because it perfectly captured the shifting ground of modern business.

Today, U.S. companies face a whirlwind of change. Markets move with unprecedented velocity, data volumes grow exponentially every day, and sophisticated cyber-attacks increasingly target the heart of finance systems. At the same time, Artificial Intelligence (AI) tools offer a revolutionary promise: better insight and faster decisions. To survive, the finance function must evolve.

According to Gartner, more than 75 percent of CFOs plan to increase technology spending in finance functions by 2026. This signals a permanent shift. Finance is no longer just about accounting and historical record-keeping; it is about intelligence and foresight. In this article, I share my research and data-driven insights to explain how AI, analytics, and cyber risk will reshape U.S. corporate finance from 2026 to 2030.

Data and Analytics Become the Core of Finance Decisions
Data has officially become the foundation of the finance department. In the past, finance teams were historians, working almost exclusively with past reports. Today, they function as navigators using real-time dashboards. Advanced analytics allows CFOs to identify patterns, market trends, and internal risks before they manifest as crises.

McKinsey reports that companies utilizing advanced analytics in finance improve their decision-making speed by more than 30 percent. From my experience, the most profound change is in forecasting. AI models can now predict cash flow, consumer demand, and cost fluctuations with surgical accuracy. One finance team I consulted with reduced their forecast errors by nearly 40 percent within a single year by moving away from manual spreadsheets to automated analytical tools.

Artificial Intelligence Changes How CFOs Think
It is a common misconception that AI will replace the CFO. In reality, AI supports the CFO by acting as a powerful co-pilot. By automating routine, repetitive tasks, AI frees up human intellect for high-level strategy. It also highlights anomalies that the human eye might miss, such as a subtle pattern of unusual transactions or early indicators of financial distress in a subsidiary.

KPMG reports that over 90 percent of companies are already seeing positive returns from AI use in finance, citing better reporting quality and lower operational risk. As Microsoft CEO Satya Nadella noted, AI is a tool to "amplify human capability." For a CFO, this means the ability to ask deeper questions, challenge long-held assumptions, and act with a level of speed that was previously impossible. However, this requires "Explainable AI"—finance leaders must understand the logic behind the models to maintain trust with boards and regulators.

Cyber Risk Becomes a Financial Risk
We must stop viewing cyber risk as a strictly technical "IT issue." It is now a primary financial risk. A single successful attack can halt payments, freeze critical systems, and permanently damage shareholder trust. IBM reports that the average cost of a data breach in the U.S. has climbed to over $9 million, with finance systems being the most frequent targets.

There is a noticeable shift in how finance leaders approach security. CFOs are now actively involved in cyber exposure discussions during budget planning, treating potential breaches as a line-item loss probability. Gartner forecasts that global cybersecurity spending will grow by about 15 percent per year through 2027, largely driven by the need to defend against AI-related threats.

AI: A Double-Edged Sword for Opportunity and Risk
While AI empowers finance teams, it also equips attackers with new weapons. Deepfake fraud—where AI-generated voices or videos mimic executives—and AI-driven phishing attacks are on the rise. Finance teams must now be trained not just in accounting, but in digital threat recognition.

According to the World Economic Forum, cyber risk is now a top-tier global business threat. I recently observed a case where an AI-generated voice scam nearly tricked a department into a massive wire transfer. Only the presence of rigorous, multi-factor manual controls prevented the loss. This highlights a critical lesson: AI opportunities must always be paired with robust cyber intelligence.

Regulation and the Expanding CFO Role
Regulators are no longer standing on the sidelines. The U.S. Securities and Exchange Commission (SEC) now demands much clearer disclosure regarding cyber risks. Furthermore, AI-related risks are beginning to appear in annual corporate filings as investors demand transparency. Recent studies show that AI risk disclosures in U.S. corporate reports have increased sharply, forcing CFOs to work in lockstep with legal and risk teams.

Consequently, the CFO's territory is expanding. Gartner reports that over 60 percent of CFOs now oversee data and analytics initiatives—a responsibility that was rare a decade ago. Today’s leader must understand data ethics and cyber exposure as well as they understand a P&L statement. As Warren Buffett famously said, "Risk comes from not knowing what you're doing." In the 2020s, not knowing your data and cyber landscape is the greatest risk of all.

Challenges, Opportunities, and the Roadmap Ahead
The path forward isn't without hurdles. Many U.S. firms are still tethered to legacy systems where data is fragmented and "siloed." Additionally, Deloitte finds that over 40 percent of finance leaders cite a lack of data skills as a major barrier to transformation.

However, the opportunity for those who overcome these barriers is immense. Early adopters gain a "resilience premium," enjoying better investor trust and the ability to pivot strategies in days rather than months. To join these leaders, I suggest a five-point roadmap:

Build Data Literacy: Ensure every finance professional understands the basics of data science.

Adopt AI with Governance: Use AI tools, but establish clear ethical and operational rules.

Integrate Cyber Risk: Treat cyber threats with the same mathematical rigor as credit or market risk.

Invest in Hybrid Skills: Prioritize hiring individuals who are bilingual in finance and technology.

Foster Cross-Team Collaboration: Ensure Finance, IT, and Security operate as a single unit.

Conclusion
The next five years will redefine corporate finance in America. Data, AI, and cyber risk are the new pillars of the CFO's office. Leaders who adapt to this toolkit will lead their organizations with clarity and resilience, while those who delay will find themselves struggling to keep pace. The future of finance depends on intelligence—start building your toolkit today.

References:
Gartner CFO Survey 2025
McKinsey Global Institute Analytics Reports
KPMG AI in Finance Study
IBM Cost of a Data Breach Report 2024
World Economic Forum Global Risk Report 2024

Integrating FinTech Analytics and Cybersecurity for U.S. Consumer Protection and Financial Fraud Prevention

Md Tauhid Hossain Rubel — Tue, 11 Nov 2025 09:29:28 +0000

As someone who has lived on the frontlines of digital finance, I've been able to see the incredible speed of the pace of progression. FinTech- financial technology has completely changed the way we do things with money. We can get a loan in our phone, send payments on the other side of the country in an instant, and look at our balances with a voice command. But there is a silver lining to every situation and in the case of FinTech, the cloud that looms is the rising levels of Financial Fraud. The speed at which the digital transactions are taking place has emerged as a weapon for the criminals.

The sheer importance of this challenge, especially in the U.S., cannot be overestimated. In 2024, victims of fraud cost America consumers more than $12.5 billion [2.1]. This is not only big bank, this erodes the trust we have in the digital economy on an everyday basis. Globally, financial fraud losses have developed into a huge problem with some reports putting the FinTech-related fraud losses in the past few years at more than $50 billion [1.1]. This is the reason that the integration of FinTech analytics and cybersecurity are so critical. It is the only way to create a defense as fast and sophisticated as the threat being faced.

My own work has always been about the data and I've gained first hand experience on how raw data becomes a shield. The secret to that is Artificial Intelligence (A.I.) and Data Analytics. This integration isn't from preventive aspects like blocking a hacker, but predicting it. Traditional cybersecurity was static - a lock on a door; FinTech security in today's world is smart - knowing what normal looks like. In doing this, it applies machine learning to analyze millions of transactions, looking for miniscule deviations in a user's behavior (a change in device, location of login from the usual, strange size of a transaction, etc). HSBC, for example, have seen the power of this as they are finding that they are finding two to four times more financial crime using AI and also having 60% less false alarms for their customers [4.5].

This synergism affects all sectors. In the Finance and Accounting sectors there is the use of AI-driven systems that ensure Anti-Money Laundering (AML) and Know Your Customer (KYC) checks are automated, saving time and reducing human error [4.2]. For Cybersecurity field, AI is changing the focus from reactance to pro-action, enabling security teams to respond to critical threat(s) in minutes, rather than hours [3.5]. In Data Analytics and Management, the need for sets of clean, safe data with which to train these fraud detection models is creating innovation in the way we store and govern sensitive information.

Looking into the Future So integration will be the mark of financial resilience. With Generative AI opening a door to a new era of fraudsters creating incredibly convincing deepfakes and customized phishing attacks, the war on finance will only get more heated [4.1]. In order to win, we must continue to work towards creating stronger solutions for digital identity and investing in AI that can detect the fraudulent identity even before it has a foothold. As I've heard a lot about in this industry, the idea is to let the cost of committing fraud exceed the profit. This relentless pursuit of security is how we ensure that FinTech innovation is able to benefit all and not instead play a role in criminalizing people.

For more information, please follow:
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https://medium.com/@mrubel.student/artificial-intelligence-driven-data-analytics-and-cybersecurity-integration-for-financial-integrity-e8cdd8acabb4

When AI Attacks the Bank: Data Forensics and Cyber-Security in U.S. Banking

Md Tauhid Hossain Rubel — Thu, 06 Nov 2025 10:53:27 +0000

Addictive intelligence enhances cyber-attacks. Banks in the United States must adopt new methods of verification and combating crimes to remain untouched by the element of threat.

When I was working on finance from 2018 to 2022, I helped to implement a system whereby the people are paid faster. I was in work through making work easier. I didn't spend too much time thinking about how bad guys can use Artificial Intelligence (AI) to break the systems that I helped build.
Presently, I am studying Cybersecurity at the master's degree level. I understand that these are much more difficult threats for the banks.

Hackers are by no means the only concern. Banks are also attacked by AI-driven scams, auto-theft, deep-fake voices and videos, and also smart and interconnected criminals.

In this paper, I summarize how U.S. banks can become stronger by applying my knowledge of money, data science, and the solution of crimes in cyberspace. I will discuss recent hacks, how AI attacks occur, what banks are lacking, improved approaches using data (such as graph analysis), and why this is important for the country's monetary infrastructure.

Recent Bad Examples
These two incidents are an indication of the changing nature of the danger.

In the year 2019, Capital One suffered a major data breach where more than 100 million customer records were hacked because of an insecure misconfiguration of firewall [1]. This just goes to show the important lesson that even the largest financial institutions are susceptible to basic mistakes, particularly in the area of cloud services [1]. Recently, in 2025, there has once again been a trend of increased attacks using AI solutions, as an interesting survey by LeakedSource showed that 87% of the security experts encountered such attacks last year [1]. This leads us to the central lesson, namely that artificial intelligence is already dramatically raising the scale and complexity of cyber threats.

These examples are examples of a systemic risk. Banks are all connected. They are using the same services on Cloud providers and partner companies. If a large bank or service is compromised, then the issue can spread very quickly to a number of other places.

How AI Changes the Attack
Artificial Intelligence (AI) is a rapidly transforming part of cyber-attacks.

AI-Made Social Engineering: Hackers are writing fake emails (phishing), creating gravitas voices to cheat money from victims (BEC), and making fake videos of bosses or clients (deep-fakes). According to one report, banks around the world suffered $28.6 billion in 2025 due to the use of AI to increase fraudulent activity.

Auto-Phishing, Time Short, focused phishing campaigns, Anti-ion and Phishing: >Google Drive, Collaboration Tools Miracle, Supply Chain Attack, and more Automatically spying Reputation Sparta Attempt Red Border Attack: Spic and Span - Sustainable phishing Deccan. Spyware and is not simply - AI detect anomalies, find hidden data, identifying gap in network. Current antipsychotic detection - Comprehensive phishing Fast System Aware Advanced detect Vulnerabilities - Rapid security policy Smart device to Phishing Spam Protection. Manual drive encryption Frisign status - Invasion, robotic weapons also check is in phishing coordinated, which It was reported that the number of attacks through security holes on banks tripled in one year.

The AI models used by banks to detect fraud have therefore become new targets in turn. Hackers can fool about these fraud detection models.
This means that banks cannot simply rely on simple walls or file signature checks. They require more data analysis, better methods of establishing links, and an ability to unblock cyber-crimes in real time.

What Banks Are Missing Now
Government regulations monitor the safety of banks but there are still gaps.
• Banking authorities put emphasis on banks' security strategies and warn against the risks of their third-party vendors.

In regards to securities, a report indicated that the majority of the money of U.S. banks ($15.4 trillion) depends on third-party service providers.

Missing Pieces:
This means that many banks are not fully aware of all the third parties and third-party cloud services that they rely on.
Smart attacks using AI also remain invisible - old detection systems fail to see them.

Cyber-banks are also not always aware of the problem, but rather only after the event has taken place.

Many of the banks are not currently using modern data analysis tools that examine graphs of transactions or specify special AI to identify criminals that have infiltrated their systems.

In my previous employment, I learned that all money systems should have very clear space and process to view and audit everything. The same way I used to find money problems can be applied to cyber-security: you must visualize the data, correlate the information, and find anomalies in the data.

Simple Data-Driven Fixes
Banks should take advantage of this 4-step plan:

Construct a Map of Connections (The "Graph")
o Gather all usage information, including logins, money transfer, and file usage.
o Make a graph dots are customer, staff and accounts Line are transactions and logins.
o Detect suspicious connections by utilizing specific AI (Graph Neural Networks), which is more effective at detecting fraud.
o For example, any sudden transfer with a very large amount between two individuals who didn't use to do business with each other is flagged.
Recombination's of Anomaly Checks to Use
o Layer a lot of simple and smart tools and find out anomalous activities (e.g. bulk transactional checks, AI that identifies outlier users, etc.).
The following use case tools can be used for various security activities: Problem discovery, Alert, and Resolution.
Automated Crime Solving (Forensics)
With high-risk events flagged by the AI, the system should automatically open up the history of the accounts, a timeline of the criminal's progression and the behavior of the user in the past.
o Example: Have a good picture: Employee -> Outsider Vendor -> Initiated Payment -> Account modified
o Fast and modern tools should be developed by banks to detect and solve crimes enabled by the use of AI attacks.
Always Monitor and Slice and Dice%;
o Regularly listen to current alerts of new threats (such as suspicious website addresses and phishing techniques) from government and industry groups.
o Apply this new information for AI detection model improvement.

Why This is important to the Country
So the problem with making U.S. banks safe is not merely a matter of corporate responsibility; it is a public concern.

The government states that big banks could be targeted by attackers capable of cutting off the services of a significant number of people in the cyber space, leading to far-reaching issues.

If any cloud provider or a company providing a major payment system is successfully targeted, it could harm a lot of banks simultaneously, resulting in a loss of trust and money.

Government executives should ensure: mandatory reporting of attacks, same high-level security equipment for all banks, testing for AI-driven attacks, and increased checks for all outside vendor companies.

Conclusion
In my journey of being a finance manager and then a security researcher, I found out that systems break down when we neglect change. U.S. banks are no longer dealing with the world they have always known AI-powered attacks larger, faster, and more intelligent than the old defenses. The same general steps I used to repair money problems seeing the data, connecting the dots and finding weird stuff can be utilized for tackling cyber-crime.
With the concepts of mapping relationships, fusing detectors, accelerating crime-solving, and sharing threat proximity notifiers, banks can create powerful protection. This should be taken seriously by the government and banks as a risk to the country and not just a small problem with a computer.

What to do now While, if you're in a bank, start a plan to understand the connection of your system and see how good your security tools are when it comes to fighting AI attack. We need to act now.

For more, please follow the link below:
https://medium.com/@mrubel.student/cyber-resilience-of-u-s-banking-infrastructure-under-ai-driven-threats-119542dae875

https://www.linkedin.com/pulse/shielding-americas-banks-ai-powered-cyber-defenses-financial-rubel-vr2le/?trackingId=LHKFc%2BtJQfyYf3H4TbAZxA%3D%3D

Can Stronger Cyber Defense Boost America’s GDP?

Md Tauhid Hossain Rubel — Fri, 31 Oct 2025 01:05:11 +0000

_SUBHEADING: Cyber threats cost our economy Billions. This article provides data and information gained from my work and how better cyber-defence can increase U.S. economic output. _

Have you ever wondered if cyber-security is just cost or growth to the U.S. economy. During my years providing advice financial institutions and healthcare providers I witnessed how cyber events slow investment, increase costs, erode trust and prevent growth. In this article I present some research and experience on the value of investments in Cyber-Defence as part of the economy, share some latest data, and show possible pathways to align the reality of more robust security to increased GDP

The Size of Investment and Threat
According to McKinsey & Company the global cybersecurity market was approximately US$200 billion in 2024 and is expected to be growing at ~12.4 percent per year between 2024-2027. At the same time, global estimates of the damages from cybercrimes are expected to rise and reach about US$10.5 trillion per year by 2025. While the U.S. specific numbers on the effect on the GDP are limited, one source was quoted as estimating that malicious cyber-activity cost the U.S. economy US$57 billion to US$109 billion in 2016. These figures reveal threats scale and investment scale are both high - which prompts the question of whether there is some strategic correlation between cyber-defence and economic performance.

How Better Cyber Defense Can Boost the GDP
When organizations invest in cyber-security, they decrease business disruption, avoid loss of customer trust, prevent regulatory fines and accelerate digital innovation - all of which help productivity. For example, investment in digital infrastructure (i.e. data-centers) has been associated with quantifiable increases in GDP: J.P. Morgan estimated that investment in data centers could raise US GDP by 0.1-0.3 percent in 2024-25. Although data-centers are not an example of cyber-security specifically it can be seen the principle applies: investment in safe digital infrastructure as a basis for growth.

From my past job I noted that the firms which treated cyber-security as enabler (not just firewall) launched new digital services sooner and entered new markets faster and thus contributed more to GDP - linked output.

Hence stronger cyber-defence can have three economic effects:
These are solutions previously offered by the Darwinian Era: Protecting digital productivity so that it is not lost to breaches or downtime.

Enabling innovation in digital services which drive higher value added output.
The Clean Energy Package should also put price and scale on the significant costs that are created by the resulting drag from remediation, regulatory cost and loss of trust which otherwise subtract from economic output.

Estimating impact
Given that US$25 trillion is about US$25 trillion and assuming that better cyber-defense could reduce the economic drag associated with cyber incident-loss by say 0.2-0.5 % per annum, that is a potential boost of US$50-125 billion per annum. If organizations make the cyber-investments and if the incremental productivity adds even 0.1 % of GDP - it equals to US$25 billion. Given the massive investment growth (US$200 Billion+ globally now), coupled with huge damage potential, it makes economic sense to position cyber-defense as a strategic lever of growth.

One of the most pragmatic models is cost-benefit approach in cybersecurity economics literature, that is, invest today to avoid bigger losses tomorrow. (For example, in the Gordon Loeb model that says optimal investment does not exceed ~37of expected loss. Therefore, matching investment decisions to impact - on GDP, but not on breach-count - is key.

Policymaking and Business Recommendations
Instead, these referents have claimed that: "To suggest that cyber security should be managed to mitigate risk alone is insufficient - it must be approached urgently as an economic infrastructure."

Encourage public-private investment programmers to link Cyber-defense and the digital economy growth.
And to that, they added: "We recommend using such metrics as productivity-loss avoided or GDP-contribution enabled in measuring cyber-investment."
"Pulling on past priorities, firm strategic advice and investors should include: "Tailoring investment frameworks for investment in sectors with high economic multipliers (finance, health, retail, infrastructure);
Improve transparency: gather and publish data regarding the impact of cyber incidents, investment and returns in order to render macroeconomic modelling possible.

Conclusion
Stronger cyber-defense in America is not only a technological concern; it is a growth driver for the economy. By being able to protect digital productivity, enable innovation and reducing the loss-drag from cyber-incidents we can lift part of our GDP. From my experience the firms that invest smartly not only survive threats - but they also create value and help the economy grow. If you are a business leader, a policymaker, or someone working in tech then ask yourself; how much growth could your organization enable if it was a strategic rather than reactive approach to security? Let us invest today to ensure and build our digital economy of tomorrow.

DECLARATION AS AUTHOR
I say on this article that it is my original work, putting my thoughts to paper from my research, professional experience and public data sources and that all the materials referenced are listed in this article.

TAGS

Cybersecurity #EconomicGrowth #DigitalInfrastructure #InvestmentStrategy #TechnologyPolicy

REFERENCES
McKinsey & Company. “The cybersecurity provider’s next opportunity: Making AI safer.” March 2024. https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/the-cybersecurity-providers-next-opportunity-making-ai-safer
McKinsey & Company. “New survey reveals $2 trillion market opportunity for cybersecurity.” 2023. https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/cybersecurity/new-survey-reveals-2-trillion-dollar-market-opportunity-for-cybersecurity-technology-and-service-providers
Cybersecurity Ventures. “Cybercrime To Cost The World $10.5 Trillion Annually By 2025.” 2025. https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016/
Council of Economic Advisers (White House). “The Cost of Malicious Cyber Activity to the U.S. Economy.” February 2018. https://trumpwhitehouse.archives.gov/wp-content/uploads/2018/02/The-Cost-of-Malicious-Cyber-Activity-to-the-U.S.-Economy.pdf
CybersecurityGuide.org. “How cybersecurity readiness is good for the economy.” May 2 2025. https://cybersecurityguide.org/resources/readiness-economy/
Reuters via news. “J.P. Morgan forecasts spending on data centers could boost U.S. GDP by 10-20 basis points in 2025-26.” Jan 16 2025.

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https://medium.com/@mrubel.student/cybersecurity-economics-how-digital-threats-impact-the-u-s-gdp-6697ba0f05ff

https://www.linkedin.com/pulse/counting-cost-cybersecuritys-role-americas-gdp-rubel-lbzpe/?trackingId=xrtGCT4V6IssqWmw6SgiIw%3D%3D