HITESH

BANSAL COUNTRY HEAD ( UK & I ) – CYBERSECURITY & RISK SERVICES , WIPRO

“ Bias is never an easy thing ; it can create harm as well as financial damage . Bias can emerge at many different points of the AI system life cycle , and data may not be the only reason . Aggregation , evaluation , and measurement bias are all dangerous , and these start with people . But it is also important for companies to protect their data . For example , secure data storage is important to avoid manipulation and tampering , which could lead to severe discriminatory issues in AI outputs later on .”

As Purohit adds , ensuring the ethical use of AI in security is critical to maintaining the trust and confidence of customers and stakeholders . “ Businesses should develop ethical principles that guide the use of AI in security . These principles and guidelines should align with the organisation ’ s values and be based on internationally recognised ethical frameworks .

“ Businesses must take a proactive approach to the ethical use of AI in security ,” he concludes . “ By developing ethical principles and guidelines , assessing and mitigating bias , certifying data privacy and security , businesses can use AI for security in a responsible and ethical manner .”

As Tech Mahindra ’ s Chief Digital

Services Officer , Kunal Purohit , explains , AI can be used to improve incident response and crisis management in the event of a security breach in several ways .

In the case of predictive analytics for example , AI can be used to predict and prevent security breaches by analysing historical data and identifying potential threats before they occur .

“ Predictive analytics can help security teams identify weaknesses in their systems and take steps to strengthen their security posture ,” Purohit explains .

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