CIOInsider India Magazine

The cyber-attack surface is significant and is still expanding quickly. This indicates that more than just human interaction is required for cybersecurity posture analysis and improvement within a company. Information security is becoming increasingly reliant on artificial intelligence (AI) and machine learning, which can instantly scan millions of data sets and identify a variety of cyberattacks, from malware threats to suspicious behavior that could culminate in phishing attempts. These systems are continuously growing and learning, accumulating data from both the past and the present to identify new forms of attacks that could occur in the near or distant future.

What are the Advantages of AI in the Cybersecurity Industry?
One of the many areas in which AI is extremely effective is cybersecurity. In today’s quickly evolving cyber-attacks and rapid gadget proliferation, AI and machine learning can help keep up with cybercriminals, automate threat detection, and respond more effectively than traditional software-driven or manual techniques.

Cybersecurity is one of the most demanding situations in today's business landscape, and AI is best suited to solve it. Given today’s continually evolving cyber-attacks and proliferation of gadgets, machine learning and artificial intelligence (AI) can be utilized to keep up with faulty automated threat detection and respond more effectively than traditional software-driven techniques. A self-learning AI-based cybersecurity posture management system ought to be able to address a number of these challenges. A self-learning system can be technologically developed to collect data continually and autonomously from your company's information systems. After data analysis, patterns from millions to billions of relevant signals to the enterprise attack surface are correlated.

Gaining a thorough, accurate inventory of all hardware, software, and users with access to information systems is known as IT asset inventory. Inventory categorization and business criticality measurement are also essential.

Danger Exposure: Hackers follow trends like everyone else, so their style changes frequently. AI-based cybersecurity solutions can offer current information on regional and sector-specific threats to assist in prioritizing crucial actions based not just on what could be used to attack your organization but also on what is likely to be used to attack your enterprise.

To maintain a high level of security, it is critical to comprehend the effects of the various security instruments and security procedures you have used. AI can assist in identifying the areas of your infosec program’s strengths and weaknesses.

Breach Risk Prediction: AI-based solutions can forecast how and where you will most likely be breached, considering your IT asset inventory, threat exposure, and controls efficacy. This allows you to allocate resources and tools to your weakest points in advance. You may create and optimize policies and processes to increase your organization’s cyber resilience using prescriptive insights from AI analysis.

Incident response: AI-powered systems can offer better context for prioritization and response to security warnings, quick responses to incidents, and reveal root causes to reduce vulnerabilities and prevent future problems.

Explainability is crucial for using AI to support human infosec teams. This is achieved by making recommendations and analyses more comprehensible. This is crucial for gaining support from all relevant parties inside the organization, comprehending the effects of different infosec programs, and reporting pertinent data to all concerned parties, such as end users, security operations, and the CISO auditors, CIO, CEO, and board of directors.

Microsoft’s Artificial Intelligence Model
Microsoft Corp released a solution that uses the most recent GPT-4 generative artificial intelligence model from OpenAI to assist cybersecurity experts in identifying breaches, threat signals, and better data analysis. Security analysts will benefit from the application called Security Copilot, which is a straightforward prompt box that will assist them with activities like summarizing occurrences, identifying vulnerabilities, and sharing information with coworkers on a pinboard.

The model, which Microsoft defined as a growing set of security-specific skills that are supplied with more than 65 trillion signals daily, will be used by the assistant. The debut comes as Microsoft makes a rush of announcements about using AI in its most well-known products. By multi-billion dollar investments in ChatGPT owner OpenAI, which recently released GPT-4 to execute various jobs ranging from developing an actual website through a hand-drawn mock-up to assisting people with their tax calculations, the business has aimed to outrun competitors.

Tensor Processing Unit of Google
Tensor Processing Unit, or TPU, is a unique semiconductor that Google created. More than 90 percent of the company’s work on artificial intelligence training—the process of feeding data through models to make them useful at things like producing text that is similar to human speech or creating images—is done on those chips. The business has provided details on how it connected more than 4,000 of the chips into a supercomputer using optical switches that it created specifically to do so.

Because the large language models that drive products like Google’s Bard or OpenAI’s ChatGPT have grown exponentially in scale and are now much too large to fit on a single chip, improving these connections has emerged as a key area of competition among businesses that create AI supercomputers. Instead, the models must be distributed among tens of thousands of chips, which must then cooperate in training the model over the course of many weeks. The PaLM model from Google, which is the company’s largest language model to date that has been made publicly available, was trained over the course of 50 days using two of the 4,000-chip supercomputers.