Artificial Intelligence (AI) in Cybersecurity
AI in Cybersecurity
Artificial intelligence (AI) broadly refers to any human-like behavior displayed by a machine or system that enables it to perform a function or complete a task. In AI’s most basic form, computers are programmed to “mimic” human behavior using extensive data from past examples of similar behavior. AI enables computers to make decisions that allow them to act on their own without human input and take care of routine tasks like maintenance without human intervention.
What is AI in cybersecurity?
Artificial intelligence (AI) has been a game-changer in many industries including cybersecurity. With the power of AI, security solutions can offer organizations many benefits including stronger protection against cyberattacks, lower payroll costs and increased cyber resilience. 41% of companies employ AI security tools like automated email security.
Why is cybersecurity AI important?
Cyber threats become more complex and sophisticated every day as cybercriminals evolve their tactics through social engineering. That’s bad news for businesses – an estimated 97% of employees are unable to detect a sophisticated cyberattack attempt like a spear phishing message. But AI isn’t fooled by cybercriminals tricks, enabling it to spot cybersecurity threats that humans can’t.
How is AI used in cybersecurity?
AI is often used as an umbrella term that encompasses the technology behind many smart solutions and devices. It’s a constantly evolving field with new innovations emerging regularly. These terms are often used in discussions about AI.
Machine Learning
Machine learning (ML), which is sometimes used interchangeably with AI, refers to the process by which computers develop pattern recognition, or the ability to continuously learn from and make predictions based on data, then make adjustments without being specifically programmed to do so. Machine learning collects, organizes and structures data, ensuring that AI has the information that it needs to plug into its algorithm.
Deep Learning
Deep learning is a subset of machine learning (ML), which is itself a subset of artificial intelligence (AI). The concept of AI has been around since the 1950s, with the goal of making computers able to think and reason in a way similar to humans. As part of making machines able to think, ML is focused on how to make them learn without being explicitly programmed. Deep learning goes beyond ML by creating more complex hierarchical models meant to mimic how humans learn new information.
Neural Networks
A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy.
Computer Vision
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.
Expert Systems
In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities.
Natural Language Processing (NLP)
Natural language processing (NLP) combines computational linguistics, the rule-based modeling of human language, with statistical, machine learning and deep learning models. This enables computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.
Is AI a benefit or threat to cybersecurity?
AI can be beneficial in cybersecurity, but it can also be a security threat that impacts cybersecurity in myriad positive and negative ways. Security automation and AI can save businesses more than 80% of the cost of manual security. Plus, AI and security automation enabled organizations to respond to breaches nearly 30% faster than companies without security automation.
However, there are downsides. IBM researchers identified a technique that cybercriminals can use to manipulate AI to serve their ends. In adversarial machine learning, AI systems’ neural networks are tricked by intentionally modified external data. An attacker ever so slightly distorts these inputs for the sole purpose of causing AI to misclassify them.
How does AI improve cybersecurity?
AI can improve an organization’s defenses in many important ways. Every business can reap the benefits of AI thanks to today’s affordable, automated security solutions.
Advanced Unknown Threat Hunting & Detection
AI-enabled tools are much more likely to spot a zero-day threat than conventional security tools because of their constant collection and analysis of fresh threat intelligence. Since AI doesn’t rely on threat reports to detect vulnerabilities, AI-enabled tools can come up with clues fast, leading to early detection of threats to prevent the attack.
Accelerated Incident Response Rates
AI and security automation enabled organizations to respond to breaches nearly 30% faster than companies without security automation.
Hardened Network Security
42% of companies in a cyber resilience survey cited use of AI technology and security automation as a major factor in their success at improving their cybersecurity posture.
Improved Vulnerability Management
AI helps power automation tools that find and fix security gaps fast. Security automation is the number one way to reduce a company’s attack surface.
Optimized Security Operations Center Performance
Security automation tools make use of AI to pare down false alerts and prevent IT staffers from wasting time, improving the performance of a SOC by increasing caseload capacity by 300% or more.
Overcoming the IT Skills Shortage
AI-driven automation can stand in for human staffers capably, a valuable asset in alleviating staffing issues. 46% of IT professionals believe that AI and automation is the key to alleviating the IT skills shortage.
Protection That Gets Smarter with Time
When businesses choose automated security solutions that utilize AI, their protection only gets better over time, preventing solutions from becoming obsolete quickly.
How is AI a threat to cybersecurity?
Unfortunately, advances in technology can also benefit the bad guys. Malicious hackers can leverage AI tools to enhance the precision and effectiveness of their attacks as well as find exploitable vulnerabilities. For example, they can use AI to bypass detection by having it learn the most common detection rules. Criminals can also use AI to conceal malicious codes in benign applications that they can execute later to enable a cyberattack.
The Information Systems Audit and Control Association (ISACA) cautions that AI can have a negative impact on data privacy, pointing out that the AI processing of personal data can impinge on individuals’ rights and freedoms. ISACA lists several privacy challenges of AI including, data persistence (data existing longer than the human subjects that created it, driven by low data storage costs), data repurposing (data being used beyond their originally imagined purpose) and data spillovers (data collected on people who are not the target of data collection).
What companies use AI in cybersecurity?
Companies have been making use of AI for decades because it’s a flexible, dynamic tool that can be used in so many ways. Some cybersecurity companies specialize in AI-driven security solutions, while others make use of AI to improve performance and add automation options to more conventional security solutions.
What are some examples of AI in cybersecurity?
Here are some examples of how AI is used by leaders in cybersecurity.
Google is using AI to analyze mobile endpoint threats. Organizations can use this analysis to protect the growing number of personal mobile devices. Google widely utilizes AI in its products and open-source platforms.
IBM/Watson
IBM calls its AI for businesses Watson. It helps organizations predict future outcomes, automate complex processes and optimize employees’ time. NLP With NLP, disparate, unstructured data can be brought together and processed so you can understand what it all means and make more informed decisions.
Microsoft
Microsoft utilizes AI in many ways, including in its security tools. For example, customers can benefit from AI-driven endpoint protection as a feature of Microsoft Defender.
Graphus
Graphus uses AI to complete complex pattern analyses, looking for anomalies in a company’s communication pattern that indicate potential cyberattacks and phishing messages.
The Benefits of AI-Powered Automated Email Security [h3]
AI has many benefits in email security, giving businesses an edge in the fight against dangerous threats like ransomware, business email compromise and account takeover.
Catches More Threats
AI-powered and automated email security is up to 40% more effective at spotting and stopping malicious messages like phishing email than a SEG or conventional security
Immune to Social Engineering
AI isn’t fooled by psychological manipulation, and it can’t be tricked into clicking on dangerous links or downloading spurious documents.
Avoids Costly Human Mistakes
AI doesn’t make mistakes like humans do and human error is the culprit in an estimated 90% of security breaches according to IBM’s X-Force Threat Intelligence Index.
No Threat Database Updates
Solutions that utilize AI benefit from the fact that it collects and analyzes its own threat intelligence, eliminating the wait for techs to upload threat reports to gain protection from new threats.
Zero Manual Configuration Needed
Automated solutions can take care of themselves for the most part, eliminating the need for manual configuration and making setup times fast.
Continually Refines Protection
Thanks to machine learning, AI refines a company’s protection against security threats at lightning speed through data analysis.
Advanced Phishing Detection with Graphus
Stop phishing immediately with Graphus – the most simple, affordable, reliable automated phishing defense available today. Graphus can intelligently sort and filter the emails that come into a company’s environment to determine which ones are safe and which ones are suspicious. How does it do that? By using a unique, patented algorithm that fosters machine learning, enabling it to learn each company’s unique communication patterns and refine its judgment criteria all by itself to tailor that company’s protection now and in the future.
TrustGraph® automatically detects and quarantines malicious emails that might break through an organization’s email security platform or existing Secure Email Gateway (SEG), so the end-user never interacts with harmful messages.
EmployeeShield® alerts recipients of a potentially suspicious message to danger that they may not notice by placing an interactive warning banner at the top that allows users to quarantine or mark the message as safe with a single click.
Phish911™ empowers employees to proactively report suspicious and unwanted emails for IT to investigate reducing your exposure to potential disaster.
