The Risks and Rewards of Generative AI in Technology: What Businesses Need to Know

Key Takeaways:

• Generative AI can spread misinformation and introduce security risks to your company if there is a lack of QA and fact-checking.
• Legal and ethical compliance is becoming a lot more important for AI adoption.
• AI is reshaping software development, cybersecurity, and business operations.

Generative AI Concerns

1. Spreading of Harmful Content or Misinformation

AI can generate false or misleading information that looks real, causing confusion and spreading incorrect facts. If AI pulls from unreliable sources, chances are that it will create fake news and inaccurate medical advice which can physically harm anyone that follows this without fact-checking.

This is not just limited to medicine. An AI finance chatbot might generate false financial advice that causes people to make bad investments under the assumption that this is valid information. That’s why, to reduce the sheer amount of misinformation, that can be generated AI-generated content must checked by humans before being published on social media and websites.

2. Homogenization of Content and Information

Generative AI often repeats similar patterns, making content less unique and more generic over time. Since AI learns from existing data, it may create similar articles, images, or code, reducing diversity in creative work.

Experts from the University of Washington and North Eastern University acknowledge that syntactic templates in the generated text display a clear lack of creativity and repetition. For example, multiple companies using the same AI model might produce nearly identical marketing messages. This can limit fresh ideas and make the internet feel repetitive, with fewer original perspectives.

3. Potential Legal and Copyright Issues

AI might generate content that closely resembles copyrighted material, leading to legal disputes. Since AI learns from existing works, it can unintentionally copy text, images, or music without proper attribution. An AI writing tool could produce an article that closely matches an existing blog post, causing copyright violations. Companies using AI-generated content without verification might face lawsuits from original creators. To avoid legal risks, businesses should check AI-generated work for originality before publishing or using it commercially.

4. Displacement and Redundancy of Existing Roles

Since AI can automate tasks that were traditionally done by humans, this will likely lead to job displacement in some industries. Workers in fields like content writing, customer support, and even software development may see reduced demand for their roles. Companies using AI chatbots for customer service might reduce the number of human agents they hire. However, AI also creates new job opportunities, such as AI model training and ethical AI oversight. Upskilling and learning new technologies can help workers stay relevant in an AI-driven job market.

5. Creation of Shadow IT

Employees may use AI tools without approval from their company’s IT department, creating security risks. Unmonitored AI tools can introduce vulnerabilities, such as exposing sensitive company data to external platforms.For example, an employee using an AI-powered writing assistant might unknowingly share confidential business plans.Shadow IT can also lead to inconsistent workflows, making it harder for teams to collaborate effectively. Organizations should establish clear policies on AI tool usage to prevent security risks and ensure proper oversight.

6. Data Privacy Violations

AI models often require large amounts of data, which can include personal or sensitive information. If AI tools are not carefully managed, they may store or share private data without user consent.Strict data protection policies and encryption measures are necessary to keep user information safe.For instance, an AI-powered voice assistant might record and analyze conversations without users realizing it. Hackers could exploit AI vulnerabilities to access personal details, leading to identity theft or privacy breaches.

7. Lack of Transparency

AI-generated decisions are often difficult to explain, making it unclear how or why certain results were produced. Companies using AI for hiring, loans, or medical diagnosis may struggle to explain AI-driven choices to affected individuals. For example, an AI tool rejecting a job applicant might not provide a clear reason, making it hard to address biases. Lack of transparency can lead to distrust, especially in high-stakes fields like healthcare and finance. AI developers should build models that provide explanations and justifications for their outputs to improve accountability.

Why Generative AI Matters Now

‍Generative AI is transforming sectors like software development, cybersecurity, and business operations. It makes work faster and a lot more affordable. For tech companies and industries, this makes generative AI needed more now than ever.

However, there are risks like ethical considerations and legal issues that can emerge from Intellectual Property (IP) and security gaps. 

Benefits of Generative AI in Technology

Quicker Go-To-Market Timeline

Generative AI helps teams develop products faster by automating coding, design, and testing. Instead of writing every line of code manually, developers can use AI to generate working prototypes quickly. This means businesses can launch apps, websites, or new features in weeks instead of months. For example, an AI tool can generate an entire website layout in minutes, reducing the time designers spend on mockups.

More Affordable Development

AI reduces development costs by handling repetitive coding tasks, allowing teams to focus on higher-level work. Smaller teams can build complex software without hiring as many developers, saving money.

For example, instead of paying a team to manually write test cases, AI can generate and run tests automatically. AI-powered automation also cuts down on errors, reducing the need for expensive fixes later.

Newer Concepts and More Innovation

Generative AI can suggest new ideas and solutions that humans might not have thought of. By analyzing vast amounts of data, AI can identify trends and generate creative designs, codes, and strategies. For example, AI can help engineers create innovative product designs by suggesting unique shapes and structures. Artists and writers can use AI to brainstorm new concepts, generating fresh ideas in seconds. With AI as a creative assistant, businesses can explore cutting-edge technology and stay ahead in innovation.

More Productivity and Work Gets Done

AI takes care of repetitive tasks, allowing people to focus on more important and creative work. Developers can spend less time debugging and more time improving product features.AI-powered chatbots handle customer questions instantly, freeing up human workers for complex issues. With AI speeding up daily tasks, businesses can accomplish more in less time without increasing workload.

Risks of Generative AI in Technology

1. Code Quality Issues

Generative AI can produce code that looks correct but has hidden errors. These mistakes might not be obvious at first but could cause problems later. The AI may not always follow best practices, leading to messy or hard-to-maintain code. This can make it harder for developers to understand and improve the code - Dev experts from SonarCode even acknowledge this. Because AI generates code quickly, developers might rely on it too much without carefully reviewing it. This can result in lower-quality software. Some AI-generated code might be inefficient, leading to slow performance.

For example, AI could create a long, unnecessary process when a simple one would work better. Developers still need to test and refine AI-generated code to ensure it meets real-world requirements and business needs.

2. Potential Security Issues

AI can accidentally introduce security flaws, such as weak encryption or open access to sensitive data. These mistakes make software vulnerable to hacking. Because AI pulls from a broad range of sources, it may reuse insecure coding patterns that could expose private user information.

Hackers could try to trick AI into writing harmful code, like backdoors that allow unauthorized access to systems.If AI-generated code isn’t reviewed properly, it might include outdated security methods that don’t protect against modern threats. Companies must carefully audit AI-generated code to make sure it follows security best practices and avoids risks.

3. Creation of Bias or Inaccurate Information

AI learns from existing data, which means it can repeat biases found in that data. This can lead to unfair or incorrect results. If AI is trained on biased sources, it might generate content that reinforces those biases, affecting decision-making in critical areas like hiring or lending.

Incorrect information generated by AI can spread quickly, especially if users rely on AI-generated text without verifying its accuracy. For example, an AI-powered chatbot might provide outdated medical advice because it was trained on old data. To avoid these problems, AI outputs should always be checked by humans before being used for important decisions.

4. Concerns Around Compliance and Intellectual Property (IP)

AI may generate code or content that closely resembles copyrighted material, raising legal concerns. Businesses using AI-generated content could unknowingly violate compliance laws, leading to legal action. Some AI models don’t track where their training data comes from, making it hard to prove originality.

If a company accidentally uses AI-generated code that copies someone else’s work, they might face lawsuits. To prevent legal risks, organizations should have policies in place to verify that AI-generated content is original and compliant.

5. Additional Technical Debt

AI-generated code might work at first but creates long-term problems if it’s not structured well. Poorly written code can make future updates more difficult, requiring extra time and resources to fix. Developers may have to rewrite large sections of AI-generated code to meet evolving needs, increasing costs.

For example, an AI-generated function may not be flexible enough to support new features, forcing developers to rebuild it later. Managing technical debt means carefully reviewing AI-generated code and ensuring it follows best practices for maintainability.

6. Lack of Visibility of Code Logic for Debugging

AI-generated code doesn’t always include clear explanations or comments, making it hard for developers to understand. If something goes wrong, developers might struggle to find the cause because the AI doesn’t explain its choices. Debugging AI-generated code can take longer because developers have to figure out the logic behind the code on their own.

For example, if an AI generates a complex algorithm, it may be difficult to tell why certain values are used. To make debugging easier, developers should add documentation and test AI-generated code thoroughly before deploying it.

Strategies to Navigate Generative AI Risks

• Human-in-the-Loop Validation: AI-generated content should undergo human review before publication to prevent misinformation and misleading outputs. Implementing manual QA checks ensures factual accuracy and compliance with ethical standards.
• Data Provenance and Source Tracking: AI should only generate content using verified, high-quality datasets to prevent copyright issues and biased results. Maintaining a clear record of training data sources helps trace and validate AI-generated outputs.
• Automated Bias Detection and Correction: Deploying AI models with built-in bias detection helps reduce discriminatory outputs. Regular audits and fairness assessments ensure AI decisions remain ethical and unbiased.
• Role-Based Access and Usage Policies: Restricting AI tool access to authorized users minimizes the risk of shadow IT and unauthorized data exposure. Clear guidelines on AI usage help prevent unmonitored deployments that could compromise security.
• Secure Model Deployment and Monitoring: Continuous monitoring of AI systems detects anomalies, security threats, and unintended behaviors. Logging AI decisions enhances transparency and allows real-time issue resolution.
• Explainable AI (XAI) Frameworks: Adopting AI models that provide justifications for their decisions ensures transparency in high-risk applications. Explainable outputs allow users to verify AI-generated results and address potential flaws.
• Technical Debt Management for AI Code: AI-generated code should be regularly reviewed to maintain quality, efficiency, and security. Implementing structured refactoring processes reduces long-term maintenance costs.

Strengthening Security with Generative AI Tools

AI-Powered Threat Detection Systems

Machine learning-driven security platforms analyze network traffic and detect anomalies in real-time. To handle this, tools like Darktrace and Microsoft Defendutomated Secure Code Review Tools AI-assisted code scanning tools such as GitHub Copilot and Snyk help detect vulnerabilities in software development. These tools flag security risks early, reducing exposure to exploits.

Intelligent Identity and Access Management (IAM) Solutions

‍AI-enhanced IAM tools like Infisign, Okta, and CyberArk analyze user behavior to detect anomalies and prevent credential-based attacks. Adaptive authentication frameworks strengthen access controls across enterprise environments.

AI-Powered Phishing and Fraud Prevention

Generative AI can simulate phishing attacks to train employees and improve email security awareness. Solutions like Tessian and Abnormal Security identify suspicious communication patterns and block phishing attempts.

Working With Generative AI Experts to Limit Risk

Although everyone would like to deem themselves an expert in AI - working with generative AI experts removes the chances of not meeting industry standards and compliance laws like HIPAA, GDPR, and CCPA which are detrimental to any tech company.

More than this, you get access to a wide range of data engineers, product developers, and Generative AI technicians that can handle the heavy lifting.

This can also be done with something simple like creating automation and workflows - the fact it lowers your amount of work and effort. Reach out to know how Entrans, automates processes across distribution, manufacturing, and tech industries!