Generative AI is a part of artificial intelligence that can automatically generate well-researched content, such as text, images, audio, and video. Within a few seconds, it can produce a lot of content, but there is a lot of threat. Generative AI has plenty of impacts in various fields. This article will explain the 15 hidden cybersecurity risks of generative AI and chatGTP.
What is Generative AI?
Generative AI enables users to generate diversified output with content-based input. It is known as the generative AI model. The model uses neural networks to match similar structures and create new content. The model can leverage different language models.
The creation of diversified content is the main task of generative AI. It is powered by a large AI model ( foundation model). It helps you solve problems like summarization, classification, and sorting based on relationships. The model can generate insight and answers via multiple formats. The AI product recommendation system of eCommerce can improve customer interactions. Moreover, it suits repetitive jobs like ensuring compliance, processing customer compliance, etc.
The 15 hidden cybersecurity risks of generative AI
Within 2026, more than 80% of enterprises will use the risks of generative AI to diversify their business organizations. It is interconnected with third-party data, complex algorithms, and neural network architecture. So, it may lack transparency, privacy, and security. As a result, cyber experts are worried about the risk of generative AI. Here are the 15 potential threats of this model.
1. Data Vulnerability
The 2021 study showed that over 90% of breaches came from attacks or human errors, costing around $4.24 million each. Generative AI’s access to lots of data raises these risks. In 2020, the Capgemini Research Institute noted a massive 68% jump in cyber incidents targeting AI systems. These vulnerabilities expose sensitive information, leading to breaches and rule violations.
The Ponemon Institute found that 70% of organizations lost data due to insecure AI, highlighting the need for solid security in AI development. The aim is to protect against risks and keep valuable data safe.
Bottom Line: AI models relying on massive datasets can put data at risk and increase the chances of breaches.
2. Privacy Breaches
Generative AI and chatGTP’s use of user data spark privacy breach worries. The Pew Research Center shows that 79% of Americans worry about data use, highlighting rising societal concerns.
The Cambridge Analytica scandal misused millions of data for targeted political messaging, showing the dangers of data exploitation. Generative AI crafting content from user information worsens worries, raising the risk of unauthorized exposure and privacy violations.
A Gartner report predicts that by 2023, data privacy failures will bring regulations affecting around 75% of Fortune 500 companies. These numbers highlight the need for solid data protection frameworks and ethical AI practices. They’re crucial to reducing risks linked to generative AI and protecting personal privacy.
Bottom Line: The AI’s ability to generate content based on user data might compromise personal privacy, leading to unauthorized exposure.
3. Fake Content Proliferation
Generative AI raises a big concern by spreading fake content, heightening worries about misinformation. Statista shows a worldwide rise in fake news consumption, affecting 61% of adults regularly. Its knack for creating highly realistic content worsens this problem, potentially spreading false information.
A study from the University of Oxford revealed that AI-generated text often looks just like human-written content, making false info seem credible. Additionally, the World Economic Forum sees misinformation as a significant global risk, affecting trust and decision-making in society.
As generative AI advances, fake content becomes more sophisticated. To tackle this, robust verification methods and public awareness campaigns are crucial to combat the harmful impact of misinformation on society.
Bottom Line: Generative AI can create realistic-looking but false information, fueling the spread of fake news and misinformation.
4. Phishing and Social Engineering
Generative AI and chatGTP raise severe concerns about phishing and social engineering. Verizon’s Data Breach Investigations Report shows that 85% of successful cyberattacks involve social engineering, showing how common it is.
Generative AI’s sophistication allows for personalized, convincing content, making phishing attempts more deceptive. Barracuda Networks’ study notes a 667% increase in spear-phishing attacks using AI-generated content. These tricks exploit trust, making people more likely to fall for scams.
Additionally, the Anti-Phishing Working Group saw a 47% rise in phishing websites in 2021, showing a growing use of deceitful tactics. This emphasizes the need for solid cybersecurity, including better user awareness and advanced detection systems, to tackle the evolving threats from AI-powered phishing and social engineering.
Bottom Line: Malicious actors can leverage AI-generated content to craft sophisticated phishing attempts or manipulate social interactions for deceitful purposes.
5. Adversarial Attacks
Generative AI’s vulnerability to attacks is a significant cybersecurity risk. OpenAI’s research shows that small changes in input data can drastically alter AI-generated outputs, making AI systems easily manipulated.
The MIT Technology Review reports a 50% rise in attacks on AI systems, signaling a growing threat. Bad actors can exploit these weaknesses to create misleading or harmful content. For instance, tweaking input in image recognition AI can lead to incorrect object identification.
As generative AI becomes more widespread, the threat of attacks on crucial systems like security, healthcare, and finance grows. This stresses the need for solid defense methods and ongoing research to protect AI systems from such manipulations.
Bottom Line: AI models are susceptible to manipulation through carefully crafted inputs, leading to unreliable or deceptive outputs.
6. Bias Amplification
Generative AI and chatGTP using biased datasets worsen societal prejudices, raising serious concerns about bias amplification. The AI Now Institute’s research reveals that 81% of AI professionals see bias as a significant problem in AI development.
When generative AI learns from biased data, it can magnify existing societal biases in its outputs. For instance, AI language models might mirror gender or racial biases in their training data. This perpetuation of biases in AI-generated content deepens inequalities and discrimination.
Studies show that biased data in AI can lead to unfair decision-making, impacting areas like hiring or lending. Tackling these issues requires proactive steps: using diverse datasets, checking for fairness in algorithms, and continually reviewing to reduce the risks of bias amplification in generative AI systems.
Bottom Line: Pre-existing biases in training data might be perpetuated or amplified in AI-generated content, reinforcing societal biases and discrimination.
7. Identity Theft Risks
Generative AI’s talent for mimicking voices, crafting realistic personal details, and forging synthetic identities increases the threat of identity theft. The Federal Trade Commission recorded 1.4 million identity theft cases in 2021, showing how widespread this cybercrime has become.
AI-powered identity replication allows for sophisticated impersonation attacks, leading to fraud and financial harm. Deepfake tech, a part of generative AI, saw incidents double in 2021, according to Sensity, a visual threat intelligence platform.
The ease with which AI creates lifelike audio and visual content is worrying. Instances like impersonating executives to approve fraudulent transactions highlight the risks. To combat this, we need better authentication protocols, enhanced identity verification methods, and public awareness efforts to reduce the growing threat of AI-driven identity theft.
Bottom Line: The capabilities of AI to mimic voices or generate convincing personal details can heighten the risks of identity theft and impersonation.
8. Manipulation of Online Reputation
Generative AI and chatGTP present a severe risk by generating deceitful content that manipulates online reputations. Statista shows a 50% rise in the impact of online reviews on purchasing choices recently.
AI-generated content like fake reviews or ratings can sway consumer opinions, harming businesses or individuals. A BrightLocal survey highlights that 82% of consumers check online reviews for local businesses, underscoring the significant influence of such content.
Bad actors might exploit generative AI to create fake positive or negative feedback, harming trust and brand credibility. This manipulation can cause financial harm and damage reputations.
To tackle this, we need better content authentication, strict review policies, and active monitoring to detect and counter the spread of AI-generated false reputational content.
Bottom Line: AI-generated content could be used to fabricate reviews, manipulate online ratings, or damage reputations.
9. Cybersecurity Weaknesses
Including generative AI and chatGTP in systems opens doors for cyber attackers. Accenture’s report notes a 40% spike in cyber attacks targeting AI systems last year. Flaws in AI setups become entry points for unauthorized access or disruptions.
When critical functions rely on AI, the risks grow. The Ponemon Institute’s study shows that 68% of organizations faced AI-related security issues causing data loss or system downtime.
IBM’s report highlights that system vulnerabilities lead to an average of $4.24 million in data breach costs. Protecting AI demands strong cybersecurity: regular audits, robust encryption, and strict access controls are vital to counter these vulnerabilities and fend off cyber threats.
Bottom Line: Vulnerabilities within AI systems might be exploited by cyber attackers to gain unauthorized access or disrupt operations.
10. Intellectual Property Infringement
Generative AI risks infringing on intellectual property rights by creating or replicating copyrighted content without intent. The World Intellectual Property Organization noted a 12% rise in global patent applications, highlighting the increasing need to safeguard intellectual property.
AI’s capacity to produce content resembling copyrighted material raises worries about accidental infringement. AI-generated text, music, or images might unintentionally mirror copyrighted works, leading to legal conflicts.
Furthermore, a Deloitte study predicts that by 2025, 87% of corporate value will stem from intangible assets, emphasizing the importance of protecting intellectual property. To tackle these risks, businesses using generative AI should set up robust IP monitoring, conduct thorough copyright checks, and adhere strictly to guidelines to prevent unintentional infringement when using AI-generated content.
Take Away: Generative AI could inadvertently produce content that infringes upon copyrights or patents, leading to legal conflicts.
11. Ethical Concerns
Generative AI and chatGTP spark ethical worries about responsible tech use. An Edelman survey shows that 84% want businesses to address societal issues, indicating a demand for ethical responsibility. Without clear rules, AI development risks creating morally questionable or harmful content. AI-generated material might inadvertently reinforce stereotypes or spread harmful stories.
The AI Now Institute points out that 58% of people worry about AI’s ethical implications, showing widespread concern. To address this, we need robust ethical frameworks, transparent AI algorithms, and ongoing evaluations to ensure AI reflects societal values and minimizes harm.
Ethics is crucial in developing AI to align with societal norms and expectations.
Bottom Line: Lack of clear ethical guidelines governing AI content generation can result in morally questionable or harmful content creation.
12. Loss of Trust in Authenticity
Generative AI’s knack for creating convincing content sparks worries about trust erosion. Pew Research Center found that 51% of Americans fear deepfake videos and their impact on trust. AI’s ability to craft realistic yet fake content makes it hard to tell real from manipulated info.
MIT Sloan Management Review notes that 60% of consumers struggle to distinguish between actual and AI-made content. This blurring risks undermining trust in media, institutions, and sources of information.
As generative AI advances, the risk of misinformation or altered narratives grows. This calls for robust authentication methods, improved media literacy, and transparent disclosure of AI-generated content to preserve trust in information’s authenticity.
Bottom Line: Over-reliance on AI-generated content might erode trust in the authenticity of information or media.
13. Regulatory Challenges
Generative AI brings regulatory hurdles as technology evolves faster than existing rules. The AI Index 2021 Report shows only 26% of countries have specific AI strategies, revealing a regulatory lag. AI’s rapid growth makes it challenging for rules to keep up with emerging risks.
The OECD’s study notes that 71% of AI patents come from only ten countries, showing gaps in both innovation and regulation. Inconsistent AI governance among regions makes global regulations challenging.
Solving these issues needs collaboration among governments, industries, and international bodies. They must create adaptable frameworks balancing innovation and ethics and effectively addressing cybersecurity risks from generative AI.
Bottom Line: The absence of comprehensive regulations could result in the unchecked proliferation of AI-generated content, posing challenges for governance.
14. Complexity in Attribution
The intricate nature of generative AI complicates tracing the origin of its content, increasing cybersecurity risks.
The Center for Strategic and International Studies finds over 50% of cyberattacks are hard to attribute accurately, showing how tricky attribution can be. AI’s ability to create varied content without clear markers makes tracing challenging, hampering accountability for misuse or cyber incidents.
The International Data Corporation (IDC) shows that 70% of organizations struggle to govern and audit AI models, indicating monitoring challenges for AI-generated content. Dealing with attribution issues as generative AI progresses requires innovative methods like digital watermarking, traceability protocols, and improved metadata systems. These techniques aim to establish accountability and reduce cybersecurity risks linked to untraceable or misattributed AI-generated content.
Bottom Line: Determining the origin of AI-generated content could become increasingly challenging, complicating accountability in cases of misuse.
15. Resource Misuse and Overload
Generative AI’s intensive processes raise worries about environmental impact and energy use. Studies in Nature Communications show that training big AI models can emit as much carbon as five cars in their lifetimes. The heavy computing needs of generative AI contribute to higher electricity use, affecting environmental sustainability.
Data centers that support AI training and deployment also consume a significant amount of global energy, about 1-2%, as per the International Energy Agency. As AI models get bigger, they strain computational resources even more.
Solving this requires energy-efficient computing innovations, sustainable AI designs, and optimizing algorithms to shrink the environmental impact of generative AI while keeping computational efficiency intact.
Bottom Line: The intensive computing resources required for generative AI might lead to environmental strains and energy consumption concerns.
Final Thought
Generative AI and chatGTP risk are not limited to those 15 numbers. You will get hundreds of AI threats in your day-to-day life. However, not all those impacts are destructive. Which one is the most significant threat of AI to you? Please confirm by comment.
Nasir H is a business consultant and researcher of Artificial Intelligence. He has completed his bachelor’s and master’s degree in Management Information Systems. Moreover, the writer is 15 years of experienced writer and content developer on different technology topics. He loves to read, write and teach critical technological applications in an easier way. Follow the writer to learn the new technology trends like AI, ML, DL, NPL, and BI.