The attack surface for businesses is vast and is quickly expanding and changing. To effectively quantify risk, up to several hundred billion time-varying signals must be processed, depending on the size of your business. The outcome? Analyzing and enhancing cybersecurity posture is no longer a problem on a human scale.
Artificial intelligence (AI) based cybersecurity products have emerged in response to this unprecedented challenge to assist information security teams in reducing breach risk and enhancing their security posture. Since they can quickly analyze millions of events and identify a wide range of threats, including malware that exploits zero-day vulnerabilities and risky behavior that could result in phishing attacks or the download of malicious code, AI and machine learning (ML) have emerged as crucial technologies in information security.
These technologies develop over time and use historical data to recognize current emerging sorts of threats. Through the use of behavioral histories to create profiles for users, resources, and networks, AI is able to recognize and react to departures from the norm.
Data Analytics vs. Artificial Intelligence
Unfortunately, AI is currently a buzzword that is widely utilized incorrectly. A growing number of businesses are seeking for methods to join the AI bandwagon, much like they did with big data, the cloud, IoT, and every other "next big thing." But a lot of the AI products available today don't genuinely pass the AI test. Despite the fact that they employ technologies that evaluate data and let the results influence particular outcomes, pure AI focuses on duplicating cognitive functions to automate tasks.
Here is the significant distinction:
AI systems are dynamic and iterative. As they process more data, they become smarter because they "learn" from their mistakes and develop more independence and capability.
Data analytics (DA) on the other hand, is a static process that uses specialized hardware and software to analyze huge data volumes and draw conclusions about the information they contain. DA is neither self-learning nor iterative.
Getting the Basics of AI
Artificial intelligence (AI) describes systems that are able to comprehend, pick up new information, and take appropriate action. AI currently operates in three ways:
Enhances what individuals and institutions are currently accomplishing.
Enables people and organizations can now accomplish things they otherwise couldn't.
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.
With a bank of domain-specific knowledge, methods for learning new things, and mechanisms for putting that knowledge to use, AI can be considered to have elements of human intelligence. Deep learning, neural networks, machine learning, and expert systems are all current instances or subcategories of AI technology.
Machine learning is the process of enabling computer systems to "learn" from data rather than being explicitly programmed, e.g., by gradually improving performance. Machine learning functions best when focused on a single goal as opposed to a broad purpose.
Expert systems are made to address issues in specific fields. They solve issues and reach judgments via fuzzy rules-based reasoning using carefully curated collections of knowledge by imitating the thought processes of human experts.
Neural networks employ a programming paradigm with biological inspiration that enables a computer to learn from observational data. Each node in a neural network gives its input a weight that reflects how accurate or inaccurate it is in relation to the operation being carried out. The sum of these weights is then used to determine the output.
Deep learning is a member of a larger family of machine learning techniques. Deep learning techniques for image identification are now frequently more accurate than humans, with a range of applications including autonomous vehicles, scan analysis, and medical diagnosis.
Applying AI to Improve Cybersecurity
Cybersecurity is one of the most challenging issues we face, and AI is best positioned to address it. Machine learning and AI can be used to "keep up with the bad guys," automating threat detection and responding more effectively than conventional software-driven approaches, with today's constantly developing cyber-attacks and the proliferation of gadgets.
At the same time, cybersecurity has several particular difficulties:
Massive shortages in the number of qualified security personnel
A wide attack surfaces
Tens or hundreds of thousands of devices per company
Hundreds of attack channels
Masses of data that have beyond the scope of a human problem
Many of these issues should be resolved by an AI-based cybersecurity posture management system that is self-learning. It is technologically possible to correctly hone a self-learning system such that it can autonomously and continuously collect data from all of your corporate information systems. Following data analysis, the correlation of patterns across millions to billions of signals pertinent to the enterprise attack surface is performed.
As a result, human teams working in many cybersecurity domains are fed new levels of intelligence, including:
IT Asset Inventory - compiling a thorough, accurate list of all hardware, software, and people with access to information systems. Inventory categorization and business criticality measurement are also very important.
Threat Exposure - Just like everyone else, hackers follow fashion trends, so what's in 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.
Controls Effectiveness - In order to maintain a strong security posture, it is crucial to comprehend the impact of the numerous security technologies and security processes that 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 anticipate how and where you are most likely to be compromised based on your IT asset inventory, threat exposure, and control 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 more effectively by using prescriptive insights from AI analysis.
Incident Response - AI-powered systems can offer better context for prioritizing and responding to security warnings, for quick responses to incidents, and for revealing root causes in order to reduce vulnerabilities and prevent future problems.
Explainability - The key to harnessing AI to augment human infosec teams is the explainability of recommendations and analysis. This is crucial for gaining support from all relevant parties inside the organization, for comprehending the effects of different infosec programs, and for reporting pertinent data to all concerned parties, such as end users, security operations, the CISO, auditors, CIO, CEO, and board of directors.
Early Adopters of AI
Since its debut 18 years ago, Gmail has filtered emails using machine learning methods. Today, machine learning is used in practically all its services, particularly deep learning, which enables algorithms to make more independent adjustments and practice self-regulation. Before, there was a time when having more data meant having more difficulties. With deep learning, more data is now always better. Elie Bursztein, director of Google's anti-abuse research team.
When it comes to "knowledge consolidation" jobs and machine learning-based threat detection, the IBM team is relying more and more on its Watson cognitive learning platform. What if we could automate some of the routine or repetitive labor that currently goes on in a security operation center using machine learning? – At IBM Security, Koos Lodewijkx serves as vice president and chief technology officer for security operations and response.
The networking industry is in desperate need of innovative solutions to the unfeasible economics of today's networks, according to Juniper Networks. According to Juniper, a production-ready, financially viable Self-Driving NetworkTM is the solution to this issue.
AI Use By Adversaries
Instead of constantly pursuing criminal activities, IT security professionals can utilize AI and machine learning (ML) to enforce good cybersecurity practices and reduce the threat surface. The same AI approaches can also be used by state-sponsored attackers, criminal cybergangs, and ideological hackers to get past defenses and evade detection. The "AI/cybersecurity problem" is present here.
Companies will need to be on the lookout for the potential drawbacks of this innovative new technology as AI develops and progressively infiltrates cybersecurity:
Machine learning and artificial intelligence can help guard against cyber-attacks, but hackers can foil security algorithms by targeting the data they train on and the warning flags they look for.
Hackers can also use AI to break through defenses and develop mutating malware that changes its structure to avoid detection.
Without massive volumes of data and events, AI systems will deliver inaccurate results and false positives.
If data manipulation goes undetected, organizations will struggle to recover the correct data that feeds their AI systems, with potentially disastrous consequences.
AI has become a necessary piece of technology for supporting the work of human information security teams in recent years. AI provides much-needed analysis and threat detection that can be used by cybersecurity professionals to decrease breach risk and strengthen security posture because humans can no longer scale to sufficiently guard the dynamic enterprise attack surface. In terms of security, AI can categorize risks, quickly identify any malware on a network, direct incident response, and discover intrusions before they happen.
AI enables cybersecurity teams to create strong human-machine alliances that advance our understanding, improve our lives, and advance cybersecurity in a way that looks more powerful than the sum of its parts. To gain access to more of our whitepapers, visit here.