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Introduction

Data security is a practice adopted by business organization for protecting digital information from any unauthorized access. It is a challenging task for every businesses in today’s world. With the advancement of technology, organizations face challenges on data breaches and theft. Unauthorised access to sensitive information like personal details, and financial data not only incur financial loss, but it also creates reputational damage. Data security ensures protection to everything from hardware, software to any administrative control. Data safety in organization is an integral part and AI powered solution implements the automated response that perform advanced threat detection. It predicts harmful cyber threats and pulls down the unfavourable consequences. 

This white paper explores the problem scenario faced by the organization for not having data security in business processes and the challenges faced by them in implementing the data security mechanism. A more detailed discussion is done on how AI-powered data security platform comes up with a unique solution to this prevailing problem. 

Problem statement

Individuals or organisations are reluctant to implement strong data security due to lack of awareness and it leads to potential vulnerabilities. Inadequate security measures increase the risk of exposure to cyber threats. Human error and privacy violations lead to potential data breaches. Organizations face the significant challenges of data security in terms of the following measures as described below. 

Resource constraint is one of the notable challenges most organisations face in implementation. Lack of planning in arranging financial and technical resources often leads to failure to implement data security in business processes. For example,  technical constraints like less storage capacity or low network bandwidth can limit the data encryption process and expose it to cyber threats. 

Lack of awareness on vulnerable consequences of data breaches leads to unintentional sharing of sensitive information, phishing scams. Lack of proper training on data security generates significant damage. Sometimes, employees accidentaly share personal information or use weak password due to unawareness.

Implementing data security in multi-layer data systems is a tedious tasks. Data security mechanism needs to safeguard against potential risks while maintaining the integration of various system applications. This requires complex design procedure and understand the security protocols. For example, data security application in cloud computing environments should have complience with different security models and it faces challenges of complex management. 

Growing Technology-driven cyber threats expands the potential cyberattack to every system application. AI-driven vulnerabilities such as data poisoning, and phishing need a comprehensive and robust data security mechanism. The power of quantum computing enables the cybercriminals to store encrypted data and creates the need of strong data security application. 

Temporary disruption to operation is evident while implementing data security process within the system. So, most of the businesses feel reluctance on upgrading data security for slowing down normal operational daily activities. 

Background

The common belief among people is that hackers cause the most data breaches but the fact is 80% of data theft occurs due to insidinsideranagement of data (Oracle,2025). The misconception is that firewa all is enough to secure the data but the 40% oternet breaks-ins occur inspite ofin spitein firewall. 

firewalls consequence of unavailability of data security

Data Tampering: Various study reveals Data Tampering is one of the major repercussions incurred in the absence of a data security platform (National CIO Review, 2025). The malicious third party can tamper with the data in a distributed environment by unauthorized access. For example, the third party can intercept during the data transmission and change the dollar amount from $500 to $5000. 

Data Theft: Unsecure transfer of data is more prone to data theft. Sharing of personal information such as credit card information through WAN or over the internet enables unintended persons to view data through microwave or satellite links. Hackers with access to physical wiring in LAN or Local area networks can also steal the sensitive information of the organization.  

Unprotected Password related threat: As per the Cybersecurity and Infrastructure Security Agency (CISA), multifactor authentication in password protection is essential for protection against malicious cyber attacks (CISA, 2025). Easy to guess passwords often are vulnerable to attacks and create potential threats. Inadequacy of multifactor authentication via data security platforms grants unintended access to third parties. 

Unauthorised access to system information:

Figure: Number of individuals affected in USA 2005 -2023 by Data Breach

(Source: Statista, 2025)

Unprecedented access to confidential information gives the user the sensitive database information according to cybercrime FBI Government report. Inflexibility of data security system creates hindrance in various business process. For example, it must comply with different security policy allowing employees oir customers availing the specific set of information only. 

Insider threat and challenges 

The study by the National Institute of Standards and Technology (NIST) reveals that insider threat is a major source of data breaches where the insider willingly or unwillingly harms organisational security (NIST, 2025). It is challenging to detect harmful insiders compared to external attacks. Insiders can easily leak sensitive information as they know the organisation’s weaorganisationthe location of access. Lack of dual authorisation as data security makes the organisation more vulnerable to insider threats. Dual authorisation normally enables data protection by getting approval from two authorised individuals and reducing risk. The absence of an AI-powered UBA system or user behaviour analytics failed to detect unusual user activity and flag alerts well in advance. 

Figure 2: National CIO review on insider threat 

(Source: Nationalcioreview, 2025)

Examples of Real-life data breaches 

One of the major data breach incidents took place at Pegasus airlines for an unsecured AWS S3 bucket containing several sensitive information such as flight charts, navigation materials, and crew members personal details. The root cause of this data breach is the employee’s unawareness of cloud configuration and management. Effective monitoring and robust data security platforms can mitigate this type of risk.  According to the report, a malicious insider attack took place at Yahoo causing intellectual property theft. As a result, valuable source code and strategic information have gone to the public without any notice. 

All of these real-life data breach incidents indicate the effect of the reluctance to investment in implementing robust data security platforms. 

Cost of data breach 

Figure 3: Average cost of Data Breach in the USA

(Source: Statista, 2025)

Cost of financial loss due to data branches is highest in the USA. According to Statista report, the average cost of data breach is around $9.36 million in 2024 (Statista, 2025) approximately. Lost businesses, regulatory fines, law suits and settlement incur the unprecedented cost which is far beyond the financial loss. It results in loss of customer trust and damage of reputation. IT and information security teams are facing significant challenges in terms of contribution in data security platform. 

Increased investment in cyber security 

Figure 4: North America Cyber Security Market Size

(Source: Fortunebusinessinsights, 2025)

Recent reports depicts the rising investment in data security platform integrating artificial intelligence with IoT (Internet of Things), cloud technologies and big data to protect data privacy from unusual theft. The purpose of the investment is to build a strong data security system safeguarding against any cyber attack. Furthermore, it enables organization to attain regulatory compliance and avoid financial losses. 

Proposed solution

Figure 5: Scope of AI-driven Data Security Platform

(Source: Oracle, 2025)

AI-driven Data Security Platform to safeguard

AI-driven data security platform is a unique solution for protection against any kind of cyber threat. AI and machine learning algorithm are efficient enough to analyze large amount of data within short interval of time. It proactively detects the vulnerable security threats and alerts automatically in response to suspicious activity. Hence, it is a useful solution for data breaching or safeguarding against any unauthorized access to system.

Advanced threat detection:  AI can analyze a large volume of datasets and identify malicious behaviour. AI systems continuously learn and predict anomalies by comparing them with previous unnatural behaviour. As it acts on a real-time basis, it immediately generates a signal on detection of threats.  

Automated response: AI-driven data security system identifies and manages access to applications. If there is any unusual visitors make an entry into the system, it alerts the security system with an automatic response. It predicts malicious activity by assessing unusual login times, and data access patterns and blocking the access immediately. 

Predictive analysis: Machine learning models like neutral networks,and  decision trees can assess the historical data. It can analyse the patterns and correlation and enable the system to predict the future threat well in advance. AI models have unique features of adaptive learning which enables continuous learning. As a result, it can predict threats more accurately. 

Behavioral Analysis: AI tools can monitor the user behaviour and relies on signature based detection or rule based system. It provides additional layer of defense by utilizing big data analytics. Behavirial analytics in data security is used in various industries such as banking, insurance, healthcare. Adavances software programs are used to track behavioural analytics which collectively used to understand the normal pattern of beheaviour of the user. This software generates automatic response on detection of suspicious behaviour. 

Enhanced Data Encryption: The significant benefit of AI-driven data security platform is that it saves time by preventing human error. AI models can predict the best encryption method and it can classify data accurately within a minimum period. Whereas, the conventional method needs time to choose from dozens of encryption algorithm and makes the entire system vulnerable to malicious threats. 

Conclusion

The traditional data security mechanism is no longer a viable process. Rather adoption of an AI-driven data security platform is effective to safeguard against any potential cyber threat. Its automatic predictive response in real time can protect sensitive information of the organization. As discussed in the papers, due to unawareness of data breach vulnerability many organizations have suffered significantly in recent years and they have taken drastic decisions to invest more in data security platforms to avoid financial loss. There is a rising trend of implementing data security processes powered by AI. The time is to act now to embrace AI with data security.

References

Oracle. (2025, January 27). Oracle Advanced Security Administrator’s Guide. Retrieved from https://docs.oracle.com/cd/A97385_01/network.920/a96582/overview.htm

Ponemon Institute, & DTEX Systems. (2023). 2023 Cost of Insider Risks Global Report. Retrieved from https://www2.dtexsystems.com/l/464342/2023-09-15/3w7l7k/464342/1694800570ZwvyrzsD/2023_Cost_of_Insider_Risks_Global_Report___Ponemon_and_DTEX___Dgtl.pdf

National Institute of Standards and Technology (NIST). (2020). Security and Privacy Controls for Information Systems and Organizations (NIST Special Publication 800-53 Rev. 5). Retrieved from https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-53r5.pdf

VectorEdge. (2025, January 27). Contact us. Retrieved from https://www.vectoredge.io/contact

Syteca. (2025, January 27). Real-life examples: Insider threat-caused breaches. Retrieved from https://www.syteca.com/en/blog/real-life-examples-insider-threat-caused-breaches

National CIO Review. (2024). 2023 Insider Threat Report. Retrieved from https://nationalcioreview.com/wp-content/uploads/2024/07/2023_Insider_Threat_Report-16d8d8f7.pdf

Cybersecurity and Infrastructure Security Agency (CISA). (2025, January 27). Multi-Factor Authentication (MFA). Retrieved from https://www.cisa.gov/MFA

Statista. (2025, January 27). Data breaches recorded in the United States by number of breaches and records exposed. Retrieved from https://www.statista.com/statistics/273550/data-breaches-recorded-in-the-united-states-by-number-of-breaches-and-records-exposed/

Federal Bureau of Investigation (FBI). (2025, January 27). Cyber Crime. Retrieved from https://www.fbi.gov/investigate/cyber#:~:text=If%20you%20need%20to%20report%20an%20ongoing,or%20by%20contacting%20your%20local%20field%20office%EF%BB%BF

Statista. (2025, January 27). U.S. average cost incurred by a data breach. Retrieved from https://www.statista.com/statistics/273575/us-average-cost-incurred-by-a-data-breach/#:~:text=The%20cost%20of%20data%20breach,as%20the%20third-costliest%20segment

Fortune Business Insights. (2025, January 27). Cybersecurity market report. Retrieved from https://www.fortunebusinessinsights.com/industry-reports/cyber-security-market-101165

ScienceDirect. (2021). A survey on artificial intelligence in information security. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0020025521006071

ResearchGate. (2023). Integrating artificial intelligence for enhanced data security and privacy. Retrieved from https://www.researchgate.net/publication/379549265_Integrating_Artificial_Intelligence_for_Enhanced_Data_Security_and_Privacy