DEV Community: Tuna Turacan

The Data Hunter Economy: When Your Identity Becomes Someone Else's Business

Tuna Turacan — Fri, 30 Oct 2026 20:49:35 +0000

I think we are currently in the most beneficial and rosy era of technology. We share what we want, we sign up for most sites we come across and give away our private information, and most importantly, we don't care about most things.

I believe a major shift in technology and the digital world will happen sooner or later.

The first danger I want to mention is that a company could emerge that makes money by selling people's private information for a paltry price. When this first happens, almost nobody will care; most people won't sell their information for such a low price but, that's not the real point of this company.

The company may create a huge archive of the information it buys, and people may pay to access this archive, so it's like legally selling information to other people. But this information isn't just things like phone numbers and email addresses; it can include photos, videos, and even other verifiable evidence.

This phenomenon will snowball in a few years as the accumulated knowledge within the company grows and both the number of people selling their knowledge and those buying it will increase. That's not the only problem; there will also be those who sell other people's information and photos, without their knowledge or consent, in order to make money.

As this situation progresses, it's also possible for people to secretly take photos and videos of individuals they only know by name and try to sell them. The company will most likely pay a very small amount but since collecting and selling information is easy, accumulating it will also be easy. Just like throwing a plastic water bottle into a recycling machine and earning money.

People will realize the danger as time goes on and when they see content about themselves without selling or sharing their own information. Although currently this issue seems to be limited to some companies selling their customers' information, there are actually similar things happening even now. They are very limited and still in their infancy.
I don't want to say anything definitively because this might not lead to any legal consequences in the future. But even if it doesn't become a legal matter, people will inevitably find out, they will want to look into other people's information out of curiosity and they will become involved in this rapidly growing phenomenon.

Even if such a business model were initially permitted under certain legal conditions, stricter regulations would likely emerge as its risks became more apparent. However, completely preventing unauthorized data collection could remain extremely difficult.

At least, that's my prediction. Because of the personal data economy, you might not even want to share photos on social media. It may seem too early to talk about many things but I think now is the perfect time; preventing something before it happens is the best approach.

Fighting AI with AI: How Can We Counter AI-Powered Cyberattacks?

Tuna Turacan — Tue, 27 Oct 2026 22:06:02 +0000

Today, artificial intelligence plays a major role in cybersecurity. It can perform rapid threat detection, big data analysis, and automated response. Moreover, it is not limited to these alone. However, attackers also use advanced artificial intelligence. They can carry out personalized, flawless phishing, autonomously infiltrate systems quickly, and become a weapon for attackers in many other areas. Fortunately, it is possible to disrupt the attackers' artificial intelligence, and this can be one of the strategic moves of today's cyber warfare.

So how can we neutralize the artificial intelligence used by attackers or identify its weaknesses?

🚨1. Data Poisoning

While attackers are training their artificial intelligence or collecting open data on the internet, defenders can intervene in these data pools.

How Does It Work?
Companies or security researchers deliberately inject "corrupted" or mislabeled data (trap data) into training data collected from open sources.

Result: The attacker's model is trained with this data, and if the data is used to an extent that affects the model's behavior, misclassifications or incorrect responses to certain inputs may occur. However, this result is not guaranteed. For example, a phishing artificial intelligence begins to mistake legitimate emails for malicious ones or constantly makes errors while scanning the target network.

🚨2. Adversarial Perturbation

Artificial intelligence models can produce incorrect results due to small data changes that appear insignificant to humans.

How Does It Work?
Small changes are added to an image, text, or other data to mislead the artificial intelligence's perception. For example, by changing certain pixels in an image prepared for an image recognition model, the model can be made to classify a cat as a dog.

Result: The model may misinterpret data it would normally recognize correctly. By examining such weaknesses, defenders can strengthen artificial intelligence systems against similar misleading attempts.

Note: Such changes do not work on every model; the result depends on the model's structure and the input used.

🚨3. AI Honeypots

Attackers' artificial intelligences automatically search for vulnerabilities and try to infiltrate the system. Defenders can turn this weakness into an advantage.

How Does It Work?
Defenders place fake servers, data, and services in their networks that imitate real systems. When the attacker's artificial intelligence interacts with these traps, the methods it uses and its attack behaviors are observed.

Result: By analyzing the techniques used by the attacker, defenders can detect new threats, gather intelligence about attack methods, and improve security systems.

Note: The main purpose of honeypot systems is not to disrupt the attacker's artificial intelligence, but to observe it and obtain information about attacks.

🚨4. Model Behavior Analysis and Weakness Detection

How Does It Work?
By analyzing the decision mechanism and behavior of the attacker's artificial intelligence, defenders try to determine which inputs it makes errors on and which weaknesses it has.

Result: By identifying these weaknesses, the situations that can cause the attacker's artificial intelligence to make incorrect decisions are understood, and this information is used to develop defense strategies.

Note: This method does not directly neutralize the attacker's artificial intelligence; it enables understanding its behavior and being prepared against attacks.

What Are the Challenges of This Struggle?

Although disrupting the attacker's artificial intelligence is a great idea, it has some practical difficulties:

Double-Sided Risk: While trying to disrupt the attackers' AI, we may also inadvertently manipulate our own defensive artificial intelligence.
Continuous Evolution: Both attackers and defenders improve their methods. Making artificial intelligence models robust against misleading inputs and different attack scenarios is an important part of this struggle.

Ultimately, the future of cybersecurity will not depend solely on building smarter AI, but also on understanding its weaknesses and learning how to turn them into a defensive advantage. The real question is: Who will stay one step ahead in this evolving AI-driven cyber battle?

Are We Becoming More Like AI the More We Talk to It?

Tuna Turacan — Mon, 26 Oct 2026 09:04:58 +0000

Don’t close friends start to become more like each other over time? And if so, are we beginning to resemble our new “close friend,” artificial intelligence too?

First of all, we need to answer the question of whether humans can learn to think like artificial intelligence.

The answer is yes. For example, when you tell an artificial intelligence about a problem, you usually see the following approach:
It listens to you first, tries to understand the situation without immediately judging, investigates the causes of the problem, evaluates different possibilities, tries to find a solution or a way out.

When you experience this approach over and over again, you may eventually start using the same method in your own life.
For instance, when someone offended you in the past, you immediately get angry, and then you can ask yourself:
“Why might this person have behaved this way? What is in my control? How can I solve this situation?”
This could mean incorporating some of AI’s thinking methods into your own way of thinking.

Secondly, can you start using the words that the artificial intelligence uses?
Yes, that is also possible.
Over time you may use the following expressions more frequently:

“I understand you, but let's look at the situation from another perspective.”
“Let's try to understand what the real problem is here.”
“I must focus on what is within my control.”
“I want to assess the situation before reacting.”
“What can I learn from this situation?”

You can not only imitate these, but also begin to truly embrace them. Because using an expression frequently can sometimes reinforce the way of thinking that the expression represents.

So, can a person become a better person?
I think this is the most important part of the question.

Over time, interacting with artificial intelligence may help a person become more patient, more understanding, more solution-oriented, and better at expressing themselves. However, this does not happen automatically. These qualities develop when we put what we learn into practice in our real-life relationships.

Moreover, there is a nice detail here. Artificial intelligence can remind you to stay calm and try to understand someone. You too can make these things a part of your own character over time. However, it is also not necessary to necessarily try to find solutions to every problem. Sometimes a person's need is not a solution, but only to be understood and be able to experience their feelings.

But this does not mean that you are thinking exactly the same as artificial intelligence or that you are turning into artificial intelligence. You interpret them with your own experience, change them and adapt them to yourself.
I think the main issue here is not to look like artificial intelligence; to be able to transform into a more advanced state of yourself thanks to the communication you establish with it.

Digital Fingerprint. They Can Track You Even If You Delete Cookies

Tuna Turacan — Mon, 19 Oct 2026 19:46:24 +0000

Many think that deleting cookies will help them avoid targeting, but even if cookies are deleted, it will be possible to redefine you by combining the technical features of your browser. This method is called browser fingerprinting.

Cookies remain on the browser, and it may seem that you can delete them to avoid tracking. Unlike cookies, browser fingerprinting can still be used to track or distinguish your browser even after cookies are deleted. However, instead of leaving files on your computer, Canvas/WebGL fingerprinting reads technical signals that your browser sends back to websites. These signals are screen resolution, operating system, language, fonts, hardware, and Canvas/WebGL. Each signal individually is not unique, but the sum of these signals will most likely be unique for millions of users.

This will allow ad networks to tie the same user across different sites. Opening a hidden tab or browsing from a different browser is not entirely safe, as browser fingerprinting does not rely solely on cookies. Users are not aware that these trackers can see the sum of the devices and software they use, not just their browser. Users tend to think that by deleting cookies, they have eliminated tracking cookies entirely; however, browser fingerprinting may have already recorded the unique configuration of their machine. There is also no guarantee of total protection, as some signals must be allowed for the device to operate.

Privacy browsers and add-ons can help prevent these trackers, as well as updates to browsers and software. The main thing is to teach users that cookies are not the only way of tracking on the Internet. In conclusion, browser fingerprinting is an invisible and effective way of tracking on the network. Cookies are safe to delete, but the technical specifications of the device may be much more revealing than just cookies. Thus, in terms of security and privacy, it is necessary to consider not only cookies but also the full interaction with the device in general, since sometimes the most indestructible fingerprint is the one that does not leave any marks at all.

The Invisible Killer of Cybersecurity: Why Even the Best Tools Collapse Against Human Fatigue

Tuna Turacan — Fri, 02 Oct 2026 17:25:11 +0000

We’ve spent the last decade pouring money into shiny security stacks. SIEM platforms that ingest terabytes of logs. EDR agents that watch every process. Zero-trust architectures that assume breach by default. Threat intelligence feeds that update in real time. And yet, major breaches keep happening—not because the tools failed, but because the people staring at those tools were running on empty.

I call it the “Fatigue Defense” syndrome. It’s the quiet, unglamorous reason why sophisticated security programs still get blindsided. And almost nobody is talking about it honestly.

Alert Fatigue Is Only the Surface

We love to talk about “alert fatigue” like it’s a simple volume problem: too many alerts, not enough eyes. But the real issue runs deeper. It’s decision fatigue meeting circadian biology meeting the modern security operations center.

Our brains are not designed to maintain high-stakes vigilance for eight hours straight, let alone overnight. After a certain point, as fatigue accumulates, the cognitive functions required for sustained attention, working memory, inhibition and flexible decision-making can deteriorate. We default to heuristics. “This looks like the last one.” “I’ve seen this signature a hundred times.” “If it was real, someone else would have escalated it by now.”

Security tools are optimized for detection. Humans are optimized for survival under conditions of scarcity and rest. Those two systems are currently in open conflict inside most SOCs.

And the operational environment makes this problem harder. Security teams operate around the clock, while fatigue and sleep loss can degrade the cognitive functions required for sustained attention, working memory and inhibition. They don't need a zero-day when defenders are already operating under degraded cognitive conditions.

The Tools That Make It Worse

Here’s the uncomfortable part: many of the tools we celebrate are actively contributing to the problem.

Every new detection rule, every additional data source, every “high-fidelity” alert that still requires human judgment adds weight to the cognitive load. We’ve created environments where the people responsible for protecting the organization are among the most overloaded knowledge workers in the building. Then we act surprised when they miss things.

Worse, the metrics we use to measure success often reward the wrong behavior. Number of alerts triaged. Mean time to respond. Ticket closure rates. These numbers look good in dashboards and board presentations. They say almost nothing about whether the humans doing the work are still capable of careful thought by the end of their shift.

What Actually Helps

The solutions are less glamorous, which is probably why they get less attention than the latest AI detection model.

First, treat cognitive capacity as a finite resource that must be protected the same way we protect privileged credentials. That means designing shifts and escalation paths around human biology instead of pretending it doesn’t exist. Some organizations are experimenting with shorter, more intense shifts followed by real recovery time. Others are building “quiet hours” into their detection logic—automatically reducing low-value noise while preserving escalation paths for anomalous or high-confidence activity.

Second, stop measuring people by how many alerts they close. Start measuring the quality of decisions under load. Track how often analysts reverse earlier decisions once they’ve had rest. Look at the correlation between time-on-shift and false negative rates. The data is usually uncomfortable.

Third, design the tools differently. Adaptive alerting that considers the operational load on the team–queue depth, time on shift, alert complexity and escalation availability. Interfaces that reduce rather than increase decision load. Systems that are honest about uncertainty instead of presenting every detection as a binary “investigate or ignore.”

This is also where AI earns its place—not as a replacement for the analyst, but as a load-balancer. A system that knows the queue is deep, the shift is late, and the alert is ambiguous should say so, and should route accordingly. Human-AI teaming, in this framing, isn't about automating judgment. It's about protecting the conditions under which human judgment still works.

And finally, culture. The organizations that handle this best are the ones where it’s acceptable to say, “I’m cooked, I need someone else to look at this,” without it being treated as a personal failure. That cultural permission is rarer than it should be.

The Real Competitive Advantage

In a world where every company can buy roughly the same detection technology, the remaining edge is human. Not the mythical “rockstar” analyst who never sleeps, but teams that are deliberately protected from the conditions that destroy judgment.

The next major wave of breaches may not always come from a missing a CVE or failure to implement MFA. They may also be enabled by something much less technical: smart, well-intentioned people who were simply too tired to notice the pattern that didn’t quite fit.

We keep adding more sensors, more rules, more intelligence. Maybe it’s time we started subtracting the conditions that make those investments worthless.

Because the most advanced security stack in the world is still only as good as the most exhausted person looking at it.

Tariq, S., Chhetri, M. B., Nepal, S., & Paris, C. (2025). Alert Fatigue in Security Operations Centres: Research Challenges and Opportunities. ACM Computing Surveys, 57(9), Article 224.

Cao, Y., Xie, T., & Ma, N. (2025). The impairments of sleep loss on core executive functions: General and task-specific effects. Sleep Medicine Reviews, 84, 102163.

Uetz, R., Bönninghausen, P., Hackländer-Jansen, L., & Henze, M. (2026). Can Risk-Based Alerting Mitigate Cybersecurity Alert Fatigue? preprint arXiv:2609.02465.

Chhetri, M. B., Tariq, S., Singh, R., Jalalvand, F., Paris, C., & Nepal, S. (2024). Towards Human-AI Teaming to Mitigate Alert Fatigue in Security Operations Centres. ACM Transactions on Internet Technology, 24(3), Article 12.

A New Cybersecurity Concept: Digital Shadow Security System

Tuna Turacan — Wed, 09 Sep 2026 15:27:56 +0000

An attacker can obtain the password, and even with added security, we can inadvertently grant malicious individuals access. Therefore, a system could be created that learns your behavior within the system over time and see if your behavior within the system aligns with your behavior.

For example, it can learn your typing speed, the times you generally use the system, and your mouse movements, and then display a behavioral match score every time you log in.

People don't behave the same way every day, of course, so the system creates a profile for you over a certain period, and if your behavior doesn't largely match, it requests verification with an authenticator.

If your behavior is not consistent with your previous behavior by 0% to 40%, there will be no restrictions, if the level is between 40% and 70%, continue monitoring and collect additional behavioral signals, if it's between 70-90 percent, sensitive access may be restricted and if the value is even higher, additional verification may be requested directly.

This protection system is an additional system and should not be used alone. Since, user behavior may change over time, someone else may use the user's computer, AI can make behavior mimicry easier and keyboard/mouse data may create privacy problems.

If this project is implemented, password, MFA, digital trust, and shadow security systems can be used together to provide stronger security.

Is Simply Sharing Photos Now A Danger?

Tuna Turacan — Mon, 07 Sep 2026 12:59:51 +0000

Today, billions of people actively use social media. Some people post many photos, videos, and stories publicly. In our digital age, there are many people who are influencers, make a living from social media or are content creators. But how safe is it to constantly share photos with everyone?

Even if we don’t share it constantly or at all, we can be at risk just with our profile picture. This is because deepfake videos can be created very well using only your photo. Sharing just one photo can trigger a chain reaction and put you in a difficult situation.

Nowadays, most photos and videos are not accepted as evidence in any crime unless they are from official or security cameras. This is because the videos that are prepared are so easy and professional. They can do whatever they want with just one photo of yours, publish it, and then demand money to have it removed. They make it so incomprehensible that you might say, "Did I really have a video like that?" If you’re thinking, "Everyone’s sharing their photos, nothing’s happening, so why would they use mine?", well, that’s entirely up to them. Because they don’t need to be hackers or anything like that to create deepfake videos.

In 2024, an employee in Hong Kong was tricked by a deepfake video of the company’s CFO into approving a $25 million wire transfer. Even though this event happened a few years ago, we are now facing far more advanced technology, and privacy is the most important thing protecting us.

Between 2023 and 2024 alone, deepfake scams increased by more than 200% worldwide. According to a report by cybersecurity firm Trend Micro, attackers can now carry out these attacks using free and easily accessible platforms that don’t require expertise.

Don’t share very clear photos of yourself with everyone. When an unknown number calls, don’t speak before the other person can intervene, as your voice could also be used. Perhaps in a few years, people will be afraid to even share their photos, and even the simplest photos will be manipulated and spread on the internet simply because it’s easy to do so.

Moreover, now scammers are creating fictional characters and using them. Criminals create new identities by combining real information belonging to different people. This realistic, AI-generated identity is being used in bank loan applications and money laundering operations. This threat is so significant that, for example, the Saudi Arabian Central Bank has temporarily suspended remote bank account openings after identifying more than 4.8 million suspicious identities.

Unfortunately, there is still no way to protect our photos, audio recordings, and videos other than not sharing them or only sharing them with our closest friends and family. Moreover, 84% of people no longer feel that a convincing video is real evidence. And one in ten people report that fake deepfake images of them have been created without their consent. It has been determined that deepfake photos of young girls are being used for blackmail and revenge purposes even in schools.

From now on, approaching every photo and video with skepticism, not trusting anyone directly, and protecting our privacy are the most important steps we can take. We are entering an era in the digital world where we cannot trust our eyes and ears; therefore, we must find new ways to establish trust.

Instagram Content Moderation: User-level observational study

Tuna Turacan — Thu, 03 Sep 2026 16:20:17 +0000

Observation period: August 2026- September 2026
Duration: 2 weeks

Due to the enormous amount of user-generated content shared on Instagram, effective content moderation is a crucial element for online safety and security. This study adopts a user-level observational approach to examine Instagram's content moderation and reporting mechanisms.

Objective: The aim of this study is to observe how Instagram responds to potentially inappropriate types of content and whether moderation results vary depending on the content's format. The study also aims to examine the effectiveness of Instagram's user reporting mechanism and the feedback provided to users following reports.

Research Questions

1)Does Instagram's observable moderation response differ between images, videos, and direct messages?
2)How often do user reports result in visible enforcement actions?
3)What types of enforcement actions are observed following reports?
4)How consistently does Instagram provide
feedback following a report?

Methodology: Over a period of 2 weeks, I reported over 50 pieces of content shared via Instagram Stories, over 20 Reels videos that I considered potentially inappropriate, and over 20 direct messages that I considered potentially inappropriate. I then observed Instagram's enforcement actions and the feedback it provided.

Fraud/fake accounts: Accounts created for fraudulent purposes and structured to resemble real user profiles were appeared more difficult to identify in observational tests based solely on profile and content. This could increase the importance of user reporting, especially if the account has just been created.

Hate speech: Users are given the option to filter specific words or phrases. However, in the tests conducted, some comments expressing the same meaning with different spellings were able to pass through the filtering mechanism.

Sexual/Inappropriate Content: Rapid intervention was observed in some content that could be clearly deemed inappropriate. Conversely, changes such as reducing the size of the content or censoring certain parts were found to make detection difficult in some cases.

User Reporting System: The effectiveness of user reporting appeared more limited in cases where it could not be automatically determined whether the content constituted a violation.

Feedback Consistency: While the user initially received feedback about the action taken after submitting a report, in some tests no additional notification was received regarding subsequent stages.

Findings: A difference was observed between content formats. Image-based content was found to be subject to moderation more frequently than video content. Only a small percentage of reported Reels videos were removed but some videos were subject to moderation after review. Direct messages, however, were different. In many cases, the notification did not result in an account ban. Instead, temporary messaging restrictions lasting a few days were observed, and in some cases, no visible action was taken.

**Limitations: 1)These observations are based on controlled user-level tests and do not provide information about Instagram's internal moderation models, datasets, or application infrastructure. Therefore, the findings should not be interpreted as a comprehensive evaluation of Instagram's content moderation system.

2)The original content was not available for retrospective analysis. Therefore, the study relies on the observations and moderation outcomes recorded during the observation period.
3)To protect user privacy and avoid unnecessarily reproducing potentially harmful material, original reported content and identifiable information were excluded from the published analysis.**

I hope this study contributes to a better understanding of the user-facing aspects of content moderation and provides useful observations for both users and platform stakeholders.