Introduction

The Internet of Things (IoT) has revolutionized the way we interact with technology, seamlessly integrating smart devices into our daily routines. From smart thermostats and wearable fitness trackers to home security cameras and voice assistants, IoT devices offer unparalleled convenience and connectivity. However, their rapid proliferation and extensive network connectivity also present significant security challenges. As more devices become interconnected, they create a broader attack surface for cybercriminals. This article aims to delve into the most common security vulnerabilities found in IoT devices, examining how these weaknesses can be exploited by hackers. Furthermore, it will provide practical strategies to defend against these risks, ensuring that your IoT ecosystem remains secure. Understanding these vulnerabilities and implementing effective defences is crucial for safeguarding personal and organizational data in an increasingly connected world.

What are IoT Devices?

IoT devices are interconnected objects that communicate over the internet, allowing them to send and receive data to and from other devices. These devices range from everyday items like smart thermostats and security cameras to advanced wearable technology such as fitness trackers and smartwatches. By integrating sensors, software, and network connectivity, IoT devices enhance functionality and user convenience. For example, a smart thermostat adjusts home temperatures based on real-time data and user preferences, while a security camera offers remote monitoring capabilities for enhanced safety.

In modern life, IoT devices play a crucial role in both personal and professional settings. They streamline everyday tasks, improve efficiency, and provide valuable insights through data collection and analysis. In personal settings, IoT devices contribute to home automation, energy management, and health monitoring. Professionally, they facilitate enhanced operational efficiency, predictive maintenance, and smarter decision-making. The pervasive adoption of IoT technology is reshaping industries by offering innovative solutions and creating new opportunities for businesses and individuals alike. However, as their influence grows, addressing the associated security challenges becomes increasingly essential to ensure their benefits are fully realized without compromising safety.

Common Vulnerabilities in IoT Devices

• Weak or Default Passwords: Many IoT devices come with weak or default passwords, such as “admin” or “123456,” which are rarely changed by users. This vulnerability allows attackers to easily gain unauthorized access, compromising the device and potentially the entire network it is connected to. These default credentials are often publicly known and exploited in automated attacks, leading to breaches that can affect both personal and organizational security.
• Lack of Encryption: Insufficient data encryption is another significant vulnerability in IoT devices. Without robust encryption, data transmitted between devices or from a device to the cloud can be intercepted and accessed by unauthorized parties. This lack of encryption exposes sensitive information, such as personal details or operational data, to tampering and theft, compromising user privacy and security.
• Insecure Communication Protocols: Insecure communication protocols also pose a major risk. Many IoT devices use outdated or poorly secured protocols for data transmission, which can be intercepted by attackers. These unprotected communication channels allow cybercriminals to eavesdrop on or manipulate data, potentially leading to unauthorized control of the device or leakage of sensitive information.
• Outdated Firmware and Software: Outdated firmware and software contribute to security vulnerabilities by leaving known flaws unpatched. Manufacturers may not regularly update their devices, leading to unaddressed security issues. This neglect creates opportunities for attackers to exploit these vulnerabilities, gaining control over the device or leveraging it as a gateway to other parts of the network.
• Insecure Interfaces: Insecure web and mobile interfaces are another point of attack. Many IoT devices are controlled through apps or web interfaces that may lack proper security measures. Attackers can exploit weaknesses in these interfaces to gain unauthorized access, control the device, or disrupt its functionality. Poorly designed interfaces can thus serve as gateways for broader cyberattacks, undermining device security and user safety.

Exploitation of IoT Vulnerabilities

The exploitation of IoT vulnerabilities can have significant and far-reaching consequences. One major method attackers use is the creation of botnets, which are networks of compromised devices controlled remotely by cybercriminals. By exploiting vulnerabilities in IoT devices—such as weak passwords or outdated firmware—attackers can hijack these devices and enlist them into a botnet. Once in control, the botnet can be used to launch distributed denial-of-service (DDoS) attacks, overwhelming targeted websites or online services with traffic and causing them to become inaccessible (as shown in fig. 2). Such attacks can disrupt business operations, damage reputations, and incur substantial financial losses.

Another critical risk is data theft and privacy breaches. Many IoT devices collect and transmit sensitive personal or business data, including health metrics, financial information, or proprietary business information. When these devices lack proper encryption or have insecure communication protocols, attackers can intercept and access this data.

For instance, a compromised smart home security camera might reveal private footage, or a hacked wearable fitness tracker might expose health records. The theft or manipulation of such data not only violates privacy but can also be used for identity theft, financial fraud, or corporate espionage. Also, exploited vulnerabilities can lead to unauthorized control of IoT devices. Attackers gaining control over a smart thermostat could disrupt heating or cooling, while compromised industrial IoT systems could lead to operational failures or even safety hazards. This unauthorized control can be used to cause physical damage, disrupt operations, or manipulate critical systems, posing severe risks to both individuals and organizations.

Strategies for Defending Against IoT Vulnerabilities

Defending against IoT vulnerabilities requires a proactive and multi-layered approach to ensure the security and integrity of connected devices. Implementing strong security practices can significantly mitigate the risks associated with IoT devices.

Change Default Passwords: One of the most fundamental steps is to change default passwords. Many IoT devices come with factory-set passwords that are often weak and widely known. These default credentials can easily be exploited by attackers if not changed. Users should create strong, unique passwords for each device, incorporating a mix of letters, numbers, and special characters. This practice prevents unauthorized access and enhances the overall security of the IoT network. It is crucial for both individuals and organizations to establish password policies and enforce regular password changes to maintain device security.

Implement Encryption: Implementing strong encryption is another vital strategy. Encryption protects data by converting it into an unreadable format that can only be deciphered with the correct decryption key. For IoT devices, it is essential to use robust encryption protocols for data in transit and at rest. This means ensuring that all data transmitted between devices, as well as stored data, is encrypted to prevent interception and unauthorized access. Encryption helps safeguard sensitive information such as personal data, operational details, and business intelligence, reducing the risk of data breaches and privacy violations.

Regular Software Updates and Patches: Regular software updates and patches are crucial for addressing known vulnerabilities. IoT device manufacturers periodically release updates to fix security flaws and improve functionality. Users should regularly check for and install these updates to ensure that their devices are protected against newly discovered threats. Outdated firmware and software can be exploited by attackers to gain unauthorized access or disrupt device operations. By keeping devices up to date, users can benefit from the latest security enhancements and minimize the risk of exploitation.

Secure Communication Protocols: Secure communication protocols are also essential in protecting IoT devices. Many devices use communication protocols that may not be adequately protected. Implementing secure protocols, such as Transport Layer Security (TLS) or Secure Socket Layer (SSL), ensures that data exchanged between devices and servers is encrypted and protected from eavesdropping or tampering. Users should verify that their IoT devices support modern security standards and employ protocols that offer robust protection against potential attacks.

Device and Network Segmentation: Device and network segmentation is a strategic approach to limiting the impact of potential breaches. By isolating IoT devices on separate networks from critical systems and data, users can contain any security incidents that may occur. For example, creating a separate network for IoT devices, distinct from the main business network, helps prevent a compromised device from affecting other sensitive areas. Additionally, network segmentation can help manage and monitor device traffic more effectively, making it easier to detect and respond to suspicious activities.

Let me leave you with this final note

IoT devices, while offering substantial benefits through their connectivity and automation capabilities, present significant security vulnerabilities that need to be addressed. Common issues include weak or default passwords, lack of encryption, insecure communication protocols, outdated firmware, and insecure interfaces. These vulnerabilities can lead to serious consequences, such as unauthorized access, data breaches, and operational disruptions.

To effectively defend against these risks, adopting a proactive approach is crucial. Changing default passwords to strong, unique ones is a fundamental step in preventing unauthorized access. Implementing robust encryption for data both in transit and at rest ensures that sensitive information remains protected from interception and tampering. Regularly updating firmware and software is essential to patch known vulnerabilities and mitigate security risks. Using secure communication protocols, such as TLS or SSL, helps protect data exchanges between devices. Finally, segmenting devices and networks limits the potential impact of breaches by isolating IoT devices from critical systems.

Authors Name: Ahmed Olabisi Olajide (Co-founder Eybrids)
LinkedIn: Olabisi Olajide | LinkedIn

The Threat of Deepfakes: AI and ML in the Fight Against Synthetic Media

Deepfakes, a type of synthetic media produced using artificial intelligence have lately been somewhat well-known due to their incredible realism in audio, video, and picture manipulation. Deepfakes, which are now used for public dishonesty, false information distribution, and reputation damage, have progressed from a benign form of online amusement to a major cause of worry. Their increasing complexity makes it more difficult to separate actual from fake information. To keep people’s trust in news sources in today’s fast-paced, social media-driven media environment, deepfakes must be identified and prevented. Given the prospective disruption of political processes, effects on financial markets, and erosion of public trust brought about by deepfakes, effective detection techniques are important. Artificial intelligence (AI) and machine learning (ML) are used to neutralise this threat.

AI driven systems are advancing in their capacity to detect deepfakes using pattern, anomaly, and deviation analysis of audiovisual data. AI uses deep learning and neural networks to enable the recognition of changing material, therefore protecting digital platforms from the dangers of synthetic content. This article covers the significance of artificial intelligence and machine learning in the battle against deepfakes as well as the continuous developments of these technologies.

The Evolution of Deepfakes

From simple picture editing to complex film and audio creation, deepfakes have developed over time. Though the technique originated in early work on face recognition in the 1990s, the word “deepfake” did not initially come up until 2017. Deepfakes might overcome its initial limitation (that of face-swapping in still images) partly due to developments in machine learning, especially Generative Adversarial Networks (GANs). Using a generator of phoney material and a discriminator, the GANs introduced in 2014 aim to distinguish between real and fake. This competitive process continually improves the quality of contents that are generated. By 2018, deepfake technology had progressed to creating convincing video and audio, sparking both awe and concern.

Today, deepfakes can manipulate facial expressions, lip movements, and voice in real-time, blurring the line between reality and fiction. This technology has found applications in entertainment and education but also poses significant challenges. Privacy concerns arise as anyone’s likeness can be co-opted without consent. Security threats emerge from potential misuse in fraud or disinformation campaigns. Perhaps most critically, deepfakes erode trust in digital media, making it increasingly difficult to discern authentic content from fabrications.

AI and Machine Learning Approaches to Detecting Deepfakes

Detecting deepfakes requires sophisticated AI and machine learning (ML) techniques capable of recognizing subtle anomalies in synthetic content. Deepfakes, generated using techniques like Generative Adversarial Networks (GANs), often contain small discrepancies that are difficult for the human eye to detect but can be identified through AI-powered analysis. AI-based detection techniques leverage vast amounts of data and advanced algorithms to learn patterns that differentiate real media from deepfakes.

• Neural Network: One of the primary AI techniques used is neural networks, specifically deep learning models like Convolutional Neural Networks (CNNs). CNNs are widely employed because they can process visual information, analyze patterns, and detect inconsistencies in images and videos. By training on real and fake datasets, CNNs learn to spot irregularities in the pixel structure or frame transitions that signal manipulation. These networks are capable of detecting the subtle differences between a real human face and a digitally altered one, even if the deepfake is highly realistic.
• Facial Movement Analysis: Real faces move naturally, with specific muscle patterns and micro-expressions that deepfake algorithms struggle to replicate perfectly. AI models analyse these facial dynamics, tracking the synchronization between lip movement and audio, eye blinking rates, or slight shifts in facial muscles to detect discrepancies in manipulated videos.
• Audio Inconsistencies: Deepfake videos often feature mismatches between the audio track and the visual content. For instance, AI can pick up on unnatural speech patterns, irregularities in sound frequency, or mismatches between mouth movements and spoken words. These abnormalities can be flagged by AI systems designed to match audio to visuals.
• Pixel-level Analysis: This is another effective AI-driven approach. Deepfake generators, while sophisticated, tend to leave pixel anomalies, particularly at the boundary of manipulated regions (e.g., around the eyes, mouth, or skin texture). AI can detect these pixel-level irregularities, which are often too subtle for human viewers to notice but are indicative of digital tampering.
• AI-Powered Spatial and Temporal Analysis: This can scrutinize video frames for inconsistencies across time. While an individual frame may appear realistic, examining the sequence of frames often reveals inconsistencies in motion or lighting, which deepfakes fail to maintain consistently. These subtle distortions can signal that a video has been digitally altered.

The Arms Race: Deepfake Creation vs. Detection

The battle between deepfake creators and detectors is a continuous arms race. As AI and machine learning tools improve at detecting synthetic media, deepfake creators respond by refining their techniques, making detection increasingly difficult. This cycle of advancement is driven by the dual capabilities of AI, which plays a key role in both the creation and detection of deepfakes.

On one side, deepfake creators leverage technologies like Generative Adversarial Networks (GANs) to produce increasingly sophisticated synthetic media. GANs consist of two neural networks—the generator, which creates fake content, and the discriminator, which attempts to identify real versus fake content. As the discriminator improves, the generator learns to produce even more realistic deepfakes, creating an ever-evolving challenge for detection systems. This feedback loop enables deepfake creators to generate media that are harder to distinguish from authentic content. On the other side, advancements in AI detection technologies prompt creators to innovate further. For example, when facial movement analysis became a popular detection method, deepfake algorithms improved their ability to replicate natural facial dynamics. Similarly, pixel-level analysis of deepfakes spurred creators to enhance image resolution and reduce detectable inconsistencies. As detection techniques evolve, so do the methods of countering them, resulting in a constant tug-of-war.

AI itself is central to both sides of this arms race. The same machine learning models that power detection systems also underpin deepfake generation tools. This dual role of AI presents a unique challenge—while it helps in defending against synthetic media, it also serves as the foundation for producing increasingly convincing deepfakes. The result is a perpetual cycle of creation and detection, where advances on one side directly fuel innovation on the other. This arms race continues to shape the future of media integrity and security.

Key Technologies in Deepfake Detection

The rapid advancement of deepfakes has prompted the development of sophisticated AI and machine learning technologies to detect synthetic content. These technologies harness the power of neural networks, deep learning models, and advanced forensics techniques to identify even the most subtle manipulations. Here’s a detailed look at some of the top technologies used in deepfake detection.

• Convolutional Neural Networks (CNNs): Convolutional Neural Networks (CNNs) are among the most widely used AI tools in deepfake detection. CNNs excel at processing and analyzing visual data, making them ideal for detecting image and video anomalies. By breaking down visual content into smaller pixel-level units, CNNs can identify inconsistencies that are typically invisible to the human eye. For example, they can detect subtle differences in skin texture, lighting, and facial expressions across frames. Trained on massive datasets of real and fake media, CNNs learn to spot even the slightest signs of tampering. Their ability to handle complex visual data makes them central to the detection of deepfake videos.
• GANs for Detecting Deepfakes: Generative Adversarial Networks (GANs), the very technology used to create deepfakes, are also employed in detecting them. GANs consist of two neural networks: a generator that creates fake content and a discriminator that tries to distinguish between real and fake. In detection, GANs are used to reverse-engineer deepfake generation processes by analysing and comparing real content with synthetically produced media. Detection-focused GANs excel at identifying unusual artifacts in deepfake videos, such as inconsistencies in lighting, facial alignment, or audio mismatches.
• Audio-Visual Forensics: Audio-visual forensics integrates AI-driven techniques to analyse both the video and audio components of media. Deepfakes often struggle to perfectly sync voice and facial movements, creating detectable discrepancies. By analysing the synchronization between lip movement and speech, AI algorithms can detect subtle differences that suggest manipulation. Additionally, deepfakes tend to introduce audio artifacts, such as unnatural pauses or pitch irregularities, which can be flagged by forensic tools. This method is especially useful for catching deepfake videos where the speaker’s words and facial movements don’t align naturally.
• Real-World Applications: AI-based deepfake detection technologies have found crucial applications in various fields. In media, news organizations are employing these tools to verify the authenticity of video content before broadcasting. Security agencies use AI to detect deepfakes in surveillance footage or to prevent the spread of disinformation during elections. In law enforcement, deepfake detection helps combat criminal activities like fraud or impersonation by identifying doctored evidence. Social media platforms are increasingly deploying AI-powered detection tools to remove manipulated content, safeguarding user trust.

Challenges in Deepfake Detection

Despite significant advancements, deepfake detection technologies face several challenges that limit their effectiveness. As deepfake creation methods become more sophisticated, the limitations of current AI and machine learning models are increasingly exposed.

• Limitations of AI Models: One major challenge is the inability of AI models to keep pace with the rapid evolution of deepfake techniques. Deepfake generation tools, especially those based on Generative Adversarial Networks (GANs), are constantly improving, making it harder for existing detection models to identify fakes. Additionally, detection tools often rely on massive datasets for training, and deepfake creators can exploit unseen techniques that the AI hasn’t been trained to detect. This means that newer, more advanced deepfakes may bypass even the most advanced detection algorithms.
• Ethical Concerns and Bias: AI detection systems are not immune to biases. Detection algorithms may perform unevenly across different demographics, such as race, gender, or age, leading to false positives or negatives. For instance, facial recognition and detection models have historically struggled with people of colour due to unbalanced training data, which raises concerns about fairness and inclusivity. Ethical questions also arise when it comes to privacy, as detecting deepfakes may require intrusive data collection, such as facial scans or personal audio recordings, which could infringe on individual rights.
• Accessibility and Open-Source Tools: Many advanced deepfake detection tools are developed by large corporations or government agencies, limiting public access. The lack of open-source detection software means that smaller organizations, independent media outlets, and the general public have fewer resources to detect deepfakes. This disparity in access puts underfunded groups at a disadvantage when combating misinformation. The need for more accessible and open-source tools is crucial in ensuring that everyone can participate in the fight against deepfakes and safeguard the integrity of information.

Future Trends: What Lies Ahead?

The future of deepfake detection is set to be shaped by emerging technologies and innovative approaches aimed at staying ahead of increasingly sophisticated synthetic media. As deepfakes evolve, more accurate and reliable detection tools are needed to safeguard the integrity of digital content. Here are some key trends that are likely to shape the future of deepfake detection.

• Advanced AI Tools: One of the most promising trends is the development of more sophisticated AI tools, such as self-supervised learning and transformer models. Unlike traditional deep learning models that require massive datasets, self-supervised models can learn from smaller data samples, making them more adaptable to new and evolving deepfake techniques. Transformer models, which have revolutionized natural language processing, are being adapted to analyse and cross-verify both visual and audio data, improving detection accuracy. These advanced tools will enhance AI’s ability to identify subtle anomalies in deepfakes.
• Blockchain for Decentralized Verification: Blockchain technology offers a novel solution to the deepfake problem through decentralized media verification. By creating immutable records of media content at the point of creation, blockchain can verify the authenticity of images, videos, and audio files as they circulate online. Any alterations to the original content can be detected through the blockchain ledger, ensuring transparency and accountability. This decentralized approach empowers content creators and consumers to verify the integrity of digital media without relying on centralized platforms.
• AI-Based Content Verification: The future will likely see the integration of AI-based content verification systems across social media platforms, news organizations, and security agencies. These systems could operate in real-time, flagging potential deepfakes as they are uploaded or shared. Combined with technologies like digital watermarking, which embeds hidden, tamper-proof identifiers in media, AI-based systems will offer an automated, scalable solution to deepfake detection.

Conclusion

The ongoing battle against deepfakes highlights the crucial role that AI and machine learning play in preserving the integrity of digital media. Through advanced techniques like CNN, GANs, and audio-visual forensics, these technologies enable the detection of subtle manipulations in synthetic media, helping to safeguard trust in what we consume online. However, the continuous arms race between deepfake creators and detectors underscores the need for ongoing innovation. The continued development of AI-driven detection tools is vital to staying ahead of increasingly sophisticated deepfakes. As the technology evolves, so too must our defences. Ensuring the authenticity of digital content is not just a technical challenge but a societal imperative to protect individuals, institutions, and the broader public from the harmful impacts of misinformation and deception in an ever-expanding digital landscape.

Authors Name: Ahmed Olabisi Olajide (Co-founder Eybrids)
LinkedIn: Olabisi Olajide | LinkedIn

By Abuh Ibrahim Sani

 Introduction

As technology continues to evolve, the significance of cybersecurity cannot be underestimated. Cyber threats continue to increase daily, with hackers developing sophisticated tools to carry out heinous acts; individuals and corporate bodies must arm themselves with the basic knowledge and tools to navigate the digital landscape safely.

The challenge of Internet security has grown significantly. Nearly everything we use, see, or come into contact with is online, including wearable technology, home appliances, cell phones, and even partially driverless cars. Businesses, governments, and other institutions may access trade secrets, medical information, and financial data remotely thanks to the Internet. This is the paradox of connectivity; the more interconnected our computer systems are, the more vulnerable they are to data theft, malware, operational disruption, and even outright physical harm to network and hardware infrastructure.

Despite the efforts of industries, corporate bodies and government to protect cyberspace, there is a greater threat which stands as the worst part of threats – the human factor and psychology. No matter the number of security devices like firewalls, intrusion detections, and intrusion prevention tools put in place by organizations can guarantee sufficient security without addressing the human element and its impact on security. The failure to consider human psychological means there is no security. There is a need for education, training, and awareness that cybersecurity is everyone’s business not the sole responsibility of IT professionals or cyber experts. It is a collective effort to protect cyberspace, data and network infrastructure. There is so much ignorance on the issue regarding cybersecurity that needs to be addressed. The absence of basic cybersecurity knowledge makes small and medium enterprises vulnerable to attacks, allowing attackers to steal from people and their assets. There should be a rigorous campaign in public gatherings such as schools, parks, etc. An effective cyberattack against one person frequently leads to a cyberattack against an organisation. Additionally, if they are not trained to recognise the telltale signs of a cyberattack, they may unintentionally allow an attacker in by the front door or rear door.

Cybersecurity is a crucial issue in the digital age, yet its complexities can be intimidating for everyday users. This paper aims to bridge that gap by offering a clear and practical approach to cybersecurity education.

One of the main concerns in safeguarding a country’s cyber sovereignty from hostile activity is through education and awareness. This demonstrates how crucial cybersecurity education is to foster the development of a robust cybersecurity ecosystem promote cyber sovereignty create safe digital and IT infrastructure and services, protect against advanced cyberattacks, and raise people’s knowledge and maturity in cybersecurity.

Global authorities and people alike are increasingly concerned about cybersecurity resilience, especially as people’s awareness of their privacy is growing. Therefore, we assume that educating people about cybersecurity is essential to building a society and businesses that are resilient and secure online.

Understanding Cybersecurity Basic

 In a modern world dominated by technology, the term “Cybersecurity” has become important in ensuring the integrity, confidentiality and availability of digital information. Cybersecurity can be described as the practice of protecting computer systems, networks, and data from theft, damage, or unauthorized access. It involves developing mechanisms and strategies to defend against a diverse array of cyber threats.  In the field of cybersecurity, the biggest threat often lies not in technology, but in the human element. Despite improvements in digital security measures, social engineering attacks continue to exploit the most vulnerable part of any security system; the people who use it. These attacks underscore a significant challenge, as they involve persuading individuals to give out sensitive information or take actions that put security at risk. Cybercriminals are using more and more advanced methods, such as psychological manipulation, to get around technical defences. This study seeks to shed light on the strategies employed in social engineering attacks and highlight the significant impact of human elements in cybersecurity. By identifying and resolving these weaknesses, we can enhance the security of sensitive data and improve overall safety measures. Emphasizing human factors is not just a component of cybersecurity, but it is the key to a strong security plan.

Why Cybersecurity Matters, Even for everyday users?

Cybersecurity might seem like a concern reserved for IT professionals, but that’s not the case. In our increasingly connected world, where our personal and professional lives rely heavily on digital tools, cybersecurity is crucial for everyone.

We entrust a vast amount of sensitive information to online platforms, from financial records ,medical record to personal details. Unfortunately, some of these platforms don’t always have adequate security measures in place. This leaves our information vulnerable to theft and misuse. Every online interaction contributes to building our digital identity. Cybersecurity helps safeguard our privacy by protecting our accounts from unauthorized access and threats. It prevents identity theft and ensures the confidentiality of our information .

In July 2016, The DNC email leak is a prime illustration of how social engineering can result in a significant security breach. Hackers were able to access the DNC’s email system by carrying out a phishing attack. A legitimate Google security team sent an email to DNC staff members, asking them to reset their passwords. When staff clicked the link and entered their login information, they unknowingly gave hackers access to their email accounts. This incident revealed important political communications and caused significant consequences. Social engineering and the significance of securing account recovery protocols, staff training and awareness.

Economic Implications of Cyber Theft

Individuals, businesses and industries are prime targets of cyber threats. For startups and multinational companies, the consequences of cyber-attacks and theft can be devasting and significant, leading to financial losses, reputational damage, and even bankruptcy in some terrible cases.

In 2021, Facebook suffered a data breach that leaked users’ information.  The breach emerged that a total of 533 million users’ personal information was compromised. The attack cost Facebook a total cost of $3.7 billion. And also, In 2017, Equifax suffered a data breach due to poor cybersecurity handling and management. The breach resulted in approximately 143 million American customers losing their personal information such as social security number, date of birth, driver’s license, addresses and other sensitive information. The company pays compensation to the tune of $ 1.3 billion as a consequence of the data breach. These losses are due to poor measures or negligence by the company which affected individuals. These examples highlight the impact of human error in cyber-attack.

A lack of knowledge and awareness of the importance of cybersecurity has led to many losses. There is an urgent need for an aggressive campaign to educate the masses on how cyber theft can harm their lives and businesses. Business loss and data theft are on the increase daily due to ineffective policy, protection and awareness among the people, resulting in economic loss.

A shared Responsibility

 Contrary to the perception and belief of the populace that cybersecurity is a thing of IT professionals, in a real sense, cybersecurity is everyone’s responsibility. Responsibilities that require the active participation of every individual who interacts in the digital world and outside the platform. Anyone can be a victim of cybercriminals, either online or offline. Every person has a role to play to have a safe cyberspace . These roles range from using a strong password for an online application or platform to knowing who you share information with and what information you share with people. Individuals and corporate bodies are responsible for securing credit cards, debit cards, and other sensitive information. Being vigilant would protect everyone and loved ones from social engineering, phishing and other forms of hackers’ tricks of obtaining information. Phishers do not use advanced technologies; instead, they take advantage of human nature to commit hacking. There is a dearth of knowledge on which ring in the information security chain is first compromised, even though people are more to blame for the chain’s fragility than technology. Research has shown that certain personality traits increase a person’s susceptibility to different types of lures.

To back up the point why cybersecurity is a shared responsibility. In 2021, a Colonial Pipeline Ransome attack resulted in a shortage of fuel across the southeastern United States. The attacker exploited a compromised password to gain access to the company network. This breach demonstrated how a weak password can lead to widespread disruptions. Also in 2020, a Zoom security incident occurred during covid-19 pandemic when a user failed to secure the meeting with a password or used public links which allowed uninvited participants to disrupt the meetings. Individuals need to comprehend and make use of security options offered by platforms, like implementing passwords for meetings and utilizing waiting areas . This scenario shows that cybersecurity is more than just technical solutions; it also involves user behaviour and awareness. Security cannot be achieved by simply installing a robust security system. Ignoring the human factor and failing to raise awareness will lead to security breaches.

Social Engineering (Exploiting Human Psychology)

Social Engineering is an act of obtaining sensitive information from victims through pretence. An attacker could come as a legitimate person and obtain information without the victims verifying their identity before releasing information. Social Engineering is increasing, according to statistics due to technological advancement and attractiveness in the world. Many have fallen for social engineering attacks, and many will still be victims. There is a need for a holistic approach by government and policymakers to develop strategies on how to educate and create awareness for the masses to be aware of whom they share information with and what to share. A zero-base trust mindset should be encouraged. An organization needs to do more to train employees, both recruits and old staff, to be aware of hackers’ tricks. Hackers can pretend to be staff members of a company and come in the way of helping an employee in distress, but in a real sense, it’s an act to obtain valuable information from the staff. Hackers’ tricks involve playing with human thinking and behaviours, the act of deceiving and convincing to get sensitive information about individuals or organizations.

Social engineering is the psychological manipulation of people into performing actions or divulging confidential information. Unlike other cyber threats that depend on the use of software and online platforms and loopholes in the system, social engineering targets human elements, exploiting their weaknesses and the natural tendency to trust. This can be done offline without the use of any software. It is just a matter of technique and tactics. It is called “Master the Art”.

In July 2020, Twitter experienced a significant breach in which numerous prominent accounts were taken over to endorse a Bitcoin scam. The attackers employed phone spear phishing to deceive Twitter employees into giving access to internal tools. They pretended to be IT department staff and persuaded employees to disclose their credentials during phone calls. Upon gaining entry, the intruders seized verified accounts of significant individuals and businesses, sharing messages requesting Bitcoin from followers with a pledge to double their investment. This event demonstrates how social engineering can be used to target insiders to access important systems.  Social engineering attacks can be carried out in different format such phishing, shoulder surfing, malware, phone, social media chat, ransomware, malware, eavesdropping. These are easy because of human error and behaviour toward their information security.

Information security is everyone’s business, it’s your primary responsibility to protect your data from being compromised and stolen. Hackers are aware that human beings are the easier target so they leverage the emotional state, negligence, carelessness, and ignorance to steal information. Many are victims of social engineering and many will still fall because people tend to forget the basic principles of security. It’s human nature, however, all hope is not lost if you take precautionary measures to protect yourself and your organization. Companies with sophisticated security appliances have been hacked due to human error.  Protect your password like your life, no one should have access to your password even if the person is the IT of your company. Remember, the protection of your information is your responsibility. No security measure can prevent your data from being stolen if you give free access to the attacker.  In simple interpretation, if you leave your door open without locking it, you will know what the outcome would be.