The cyber environment is becoming increasingly complex as the exponential surge of data continues. By 2020, Cisco estimates that approximately 99% of devices (50 billion) will be connected to the Internet. Currently, only 1% of devices are connected. This calls for an increasing importance placed on the security and privacy of user information.
One of the main contributors to cyber security threats is a lack of understanding of the value of personal information. Many do not think twice before allowing Facebook apps such as ‘How many babies will you have?’ to access their profile information. There is little awareness or even consideration for the consequences of allowing certain apps or websites to gain access to personal information.
With the progressive Artificial Intelligence (AI) sophistication, the detection of cyber security breaches helps the user in protecting their personal information. In most instances, this is done seamlessly without the active involvement and consciousness of the user.
The following explores how AI is being used to fight personal information threats in different industries.
Anomaly detection is a technology that uses AI to detect unusual behaviour in a complex environment. An example of this, is when a customer suddenly makes a large withdrawal from their bank account. This activity would fall outside of the parameters of ‘normal behaviour’ for this particular customer, afterwards the customer and bank would be notified of this unusual activity.
Credit card fraud/misuse is only one of many challenges faced by the banking sector. AI is helping to mitigate these risks using a technique called misuse detection. This is where machines detect credit card invasions based on previous rules that have been programmed into the machine. Every known invasion has a unique signature, which are characteristics that define an invasion. These signatures also have a related error. When the system detects one of these signatures, a warning is raised to the bank.
Another challenging area for banks is loan application fraud. AI is used to quickly analyse information relating to an applicant’s authenticity and detecting unusual behaviour or anomalies in the data provided i.e. suspicious residential/business address provided. Time spent filling in applications is another essential factor used to detect potential application fraud. By eliminating fraudulent loan applicants early in the application process, fraud can be reduced, and more time can be spent thoroughly assessing genuine applications.
Insurance companies have become a hot commodity for hackers due to the huge amount of data insurers gather and store on individuals and businesses. The reports on Liberty Life’s 2018 email breach caused a 4.7% drop in its share price, wiping R1.68 billion off its R34 billion market value. Understandably, the need to stay competitive and mitigate security threats has led companies to digitize their services and invest into new digital systems. However, this investment sparks many potential cyber security threats.
At the point when a client presents their insurance application, there is an expectation that the potential policyholder gives correct information. However, there is still a significant number of candidates who fabricate data to control the quote that they receive from the insurance company. To tackle this issue, insurers utilize AI to evaluate a candidate’s online networking profiles for affirmation that the data given isn’t false. For instance, AI can analyse the potential policyholder’s social media pictures, posts, and information to confirm application details i.e. is the potential policyholder a smoker, are they providing the correct employment details etc. This technique is effective in tracking fraudulent applications being submitted.
AI can be used to automate insurance claims assessment and routing based on existing fraud patterns. This process not only flags potentially fraudulent claims for further review, but also has the added benefit of automatically identifying good transactions and streamlining their approval and payment. With AI based fraud detection, fraudulent claims can be evaluated and flagged before they are paid out, which reduces costs for insurance providers and helps reduce costs for consumers.
Healthcare privacy and security is complex due to thousands of people being able to view patient data. It would be an impossible feat to manually analyse the number of transactions to patient data each day. Moreover, when a patient’s data is connected to the internet, there is a greater risk of privacy and security breaches.
AI has the power to sift through thousands of transactions to patient data per second and review different factors relating to each transaction such as location of access, number of log in attempts, and the duration between each log in attempt. In a case where a staff member’s account is suddenly accessing 10 000 patients’ files at the same time, this unusual behaviour would be detected by AI and an alert would be issued.
Medical equipment such as pacemakers and insulin pumps are widely used in the world and offer substantial benefits to patients. However, these devices are vulnerable to attacks as many do not have the necessary version of the operating system needed to fully utilize the security and privacy of the device. Security researchers have tested the vulnerability of medical devices such as allowing malware to be delivered to a patient’s pacemaker system. The pacemaker was commanded to issue a shock to the patient. In these circumstances, AI is used to detect unusual commands being sent to the device using anomaly detection (mentioned above). AI can continuously monitor the device without being dependent on manufacturers to inform the hospital/patient of vulnerabilities.
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