Mar 2023
AI based analytics : Create an effective analytics dashboard using AI. Data visualization management is similar to the sorting of your closet: a constant, invisible strategy operating in the background is needed to maximize results. The variety of information on the Analytics dashboard can provide you with valuable data on how to make reasonable decisions in optimizing your site, campaign and marketing strategy. Details of organic flow can inform you of a variety of things. As a site guide, you care about getting traffic and more importantly about how and why it converts to sales. Analytics tools are not effective in solving unstructured data such as text, sound and images. IT service provider company in India are covering a huge market in AI and in future is expected to grow.
AI progress greatly expands the scope of analysis compared to days when Excel was the main tool of analysis. Some ways as AI become integrated into the analysis include the following areas:
- Natural language processing (NLP)
- Transcription enables speech analytics.
- Computer vision enables image and video analytics..
Fig 1.1 The picture represents the data analysis as per Grandviewresearch report
“As of 2022, the worldwide market value for artificial intelligence was estimated at USD 136.55 billion, and it is projected to grow at a compound annual rate of 37.3% between 2023 and 2030. The widespread adoption of advanced technology in various industries, such as healthcare, retail, finance, and manufacturing, is fueled by the steady research and innovation efforts of leading tech giants.”
Image represents AI doing predictive analysis on given data
Throughout the following decades, AI is anticipated to have a significant impact on global competitiveness, offering early adopters a competitive advantage. IT consulting companies in India are more focussed on AI and at the RAISE 2020 event, Mukesh Ambani from Reliance Industries Limited (RIL) stated that – “Historically, nations have competed on the basis of their physical, financial, human, and intellectual capital. But, governments will face greater competition from digital capital in the future decades.”
Machine learning is the use of statistical methods that allows computers to identify and learn the data provided, not clearly programmed for a certain function. Some analysis methods that can be improved using AI, and machine learning includes:
- Forecast: Using short and long -term data variability to improve forecasting efforts.
- Model Recognition: Understanding of normal trends to observe anomalies as often during fraud,
- Classification algorithms: Data grouping and processing includes clustering.
AI in cybersecurity : AI has become a powerful tool for fighting cyber threats. AI oriented cybersecurity solutions can detect, analyze and respond to harmful attacks faster. Faster threat detection and answer
Faster threat detection – With AI, you can better understand your networks and identify potential threats faster than ever. AI fed solutions can distinguish huge amounts of data to determine abnormal behavior and quickly detect harmful activities, such as a new zero daytime attack. AI can also automate many security processes such as corrections, making it easier for you to stay according to your cybersecurity needs. This can help you respond faster to attacks by automating certain tasks, such as flowing from the vulnerable server or by warning your IT team of possible problems.
Greater scaling and cost savings : AI can automate the tedious security tasks by releasing valuable resources to focus on other areas of business. In addition, it can quickly and accurately process a lot of data to identify threats faster than any person. This helps to shorten the time of security events and helps reduce the cost of defense against cyber threats.
AI -powered tools can also help determine harmful activities by correlating different data points, allowing proactive protection for their systems. These solutions are easy to change, which means you can buy additional protection without the high hardware or staff costs.
Risk involved with AI in cybersecurity: Bias and discrimination in decision -making as AI systems can come from a variety of sources, including data sets containing biased information or algorithms that lack the required objectivity. “If these biased decisions are not properly controlled, these decisions can cause discriminatory decisions for certain groups or individuals and have significant consequences for the organization.”
AI -powered technologies can detect anomalies, scan vulnerabilities and harmful activities, and recognize patterns and behaviors that could show a threat. By using AI opportunities and best practice, you can improve your cybersecurity posture and gain a competitive advantage in the ever -changing cyber threat landscape.
