In order to talk about Artificial Intelligence we must define it and agree on this definition. Artificial intelligence is basically a program. A set of procedures, functions, memory, database and logic. Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decision making. Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.
Artificial Intelligence Powered World!
Artificial Intelligence Modeling
Lemonade is one of this year’s hottest IPOs and a key reason for this is the company’s heavy investments in AI (Artificial Intelligence). The company has used this technology to develop bots to handle the purchase of policies and the managing of claims. Lemonade uses Facial Recognition AI Models to help doctors get data from patients answers to questions and process patients video and image data.
An AI (artificial intelligence) model is a program that has been trained on a set of data (computer vision (images, videos), robotics, NLP) to recognize certain types of patterns in the set of data. AI Models use machine learning algorithms to perform pattern recognition creates a machine learning model. This model is tested by humans to perfect the AI model.
An Example: Using your CCTV set of data such as objects(people, things, etc), location of the CCTV feed, we can run machine learning algorithms to find a pattern.
Artificial Intelligence Cybersecurity
Artificial intelligence techniques can be used to learn how to remove noise or unwanted data so security experts can spend more time on valid security threats. Ai can also benefit cybersecurity with automated techniques to generate whenever cyber threats are detected. AI and machine learning (ML) have become critical technologies in information security, as they are able to quickly analyze millions of events and identify many different types of threats – from malware exploiting zero-day vulnerabilities to identifying risky behavior that might lead to a phishing attack or download of malicious code. These technologies learn over time, drawing from the past to identify new types of attacks now. Histories of behavior build profiles on users, assets, and networks, allowing AI to detect and respond to deviations from established norms.
Today, AI works in three ways:
- Assisted intelligence, widely available today, improves what people and organizations are already doing.
- Augmented intelligence, emerging today, enables people and organizations to do things they couldn’t otherwise do.
- Autonomous intelligence, being developed for the future, features machines that act on their own. An example of this will be self-driving vehicles, when they come into widespread use.
Machine learning, expert systems, neural networks, and deep learning are all examples or subsets of AI technology today.
- Machine learning uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance) using data rather than being explicitly programmed. Machine learning works best when aimed at a specific task rather than a wide-ranging mission.
- Expert systems are programs designed to solve problems within specialized domains. By mimicking the thinking of human experts, they solve problems and make decisions using fuzzy rules-based reasoning through carefully curated bodies of knowledge.
- Neural networks use a biologically-inspired programming paradigm which enables a computer to learn from observational data. In a neural network, each node assigns a weight to its input representing how correct or incorrect it is relative to the operation being performed. The final output is then determined by the sum of such weights.
- Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Today, image recognition via deep learning is often better than humans, with a variety of applications such as autonomous vehicles, scan analyses, and medical diagnoses.
New levels of intelligence feeding human teams across diverse categories of cybersecurity, including:
- IT Asset Inventory – gaining a complete, accurate inventory of all devices, users, and applications with any access to information systems. Categorization and measurement of business criticality also play big roles in inventory.
- Threat Exposure – hackers follow trends just like everyone else, so what’s fashionable with hackers changes regularly. AI-based cybersecurity systems can provide up to date knowledge of global and industry specific threats to help make critical prioritization decisions based not only on what could be used to attack your enterprise, but based on what is likely to be used to attack your enterprise today.
- Breach Prediction – Accounting for IT asset inventory, threat exposure, and controls effectiveness, AI-based systems can predict how and where you are most likely to be breached, so that you can plan for resource and tool allocation towards areas of weakness. Prescriptive insights derived from AI analysis can help you configure and enhance controls and processes to most effectively improve your organization’s cyber resilience.
- Mitigate Vulnerabilities – AI powered systems can provide improved context for prioritization and response to security alerts, for fast response to incidents, and to surface root causes in order to mitigate vulnerabilities and avoid future issues.
- Augment Strengths – Key to harnessing AI to augment human info security teams recommendations and analysis. This is important in getting buy-in from stakeholders across the organization, for understanding the impact of various programs, and for reporting relevant information to all involved stakeholders, including end users, security operations, CISO, auditors, CIO, CEO and board of directors.