Cybercriminals are posing new challenges and threats as technology advances incredibly. Understanding the impact of AI and ML on cybersecurity in this digital age is essential. AI and ML can both improve cybersecurity defences and empower hackers.
This post aims at shedding light on the intersection between AI, ML and cybersecurity by examining benefits, risks and future implications. We will explore the changing landscape of cyber security and how individuals and organizations can harness AI and ML to protect themselves against cyber threats.
Modern cyber attackers have developed advanced techniques, tactics, and procedures (TTPs) that are both fast and plentiful. Advanced threats like ransomware, crypto-jacking, software supply chain attacks and phishing are also common.
A growing cyber threat surface is being created by the increasing dependence of global workforce on digital resources. Businesses assign their CISOs to develop, maintain, and continuously update their cybersecurity strategies and solutions to meet these challenges.
The Age of AI & Machine Learning in Cybersecurity
From a tactical perspective, CISOs ensure that their business’s security architecture can withstand changing threats.
It is essential to choose the right tool stack that can combat complex cyber threats at a rapid pace. CISOs must combine multi-layered, proactive security solutions to create a strong defence.
Read: What is a Cybersecurity Maturity Model?
Advanced Threats Require Advanced Solutions
Many CISOs today know that Machine Learning (ML), Artificial Intelligence (AI), and machine learning (ML) are needed to speed up and automate the fast decision-making process required to respond to advanced cyber threats.
Artificial intelligence (AI) is designed to give computers the same responsive ability as the human brain. AI includes the ML discipline.
It continually analyzes data to identify behavior patterns and form conclusions to detect new malware. The federal-level constantly discusses the importance of building the right security infrastructure.
The Subcommittee on Cyber of the U.S. Senate Armed Forces Committee held a hearing in May 2022 on the importance of leveraging artificial intelligence and machine learning within cyberspace.
Do You Know How Cyber Security Software Services Help to stop Breaches
Google and Georgetown University’s Center for Security and Emerging Technology representatives discussed how AI and ML could be used to protect against adversary attacks, organize data effectively, and process millions upon millions of attack vectors per minute. This is far more than any human-only capability for threat detection.
The cyber security committee highlighted another growing concern: the “shortfall in technically qualified cybersecurity personnel across the country in both government and industry alike.”
According to the 2021 Cybersecurity Workforce Study, more than 2.7 million unfilled cybersecurity roles exist. Many security departments can be overwhelmed by the alert-to-response ratio, leading to a decline in cyber skills.
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AI can help overworked teams increase their protective services, automate and coordinate complex and time-consuming responses and scale up.
All representatives emphasized the importance of using AI in cybersecurity. The key benefits are summarized below:
- Automated attack vector processing: AI can process millions of vectors per second and combat emerging attacks by detecting real-time patterns.
- Support for Zero-Trust Model: Human patterns can be predicted, so disparate data sets not derived from AI are useless or ineffective. AI can perform the full threat analysis required to maintain a zero-trust model.
- Security Operations Management: Artificial Intelligence technology can be used to augment cybersecurity teams. It automates the interpretation of attack signals and prioritizes alerts.
Analog Players In A Digital World – The Deficits of Legacy AV
The number of malware threats can be easily documented and accounted for in a long time. Businesses chose legacy anti-virus (AV) and anti-malware solutions (AM). These solutions allowed them to block known threats.
This was possible because malware variants were already identified and assigned a signature. The signature is then distributed to all protected endpoints.
These legacy AV/AM solutions are signature-based and designed to detect known threats but not to be blind to any unexpected. This allows for a gap between initial malware use and creating a new signature to stop it.
Today’s threat landscape has a problem: threat actors are skilled at creating new malware. VirusTotal claims that it receives over 2 million samples each day.
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They reported that legitimate certificates signed more than a million samples in 2021. Legacy AV and AM are only capable of protecting against known threats. They cannot keep up with the new malware, ransomware or incoming zero-day vulnerabilities.
Speed is critical during an attack. Legacy solutions such as AV and AM cannot detect and stop malicious attacks in real-time. AVs and ams are only as good and up-to-date as the last update.
Actionable analysis from prior attacks can often be weeks or months old before they are available to these solutions.
Related: Top 10 Cyber Security Threats to Know
Why AI & ML Thrive In The Cybersecurity Arms Race
Machine learning and artificial intelligence can be used to combat modern threats. Their capabilities extend beyond the detection and flagging of known threats.
They can identify new malicious behaviours and learn from existing exploits and threat actor TTPs. AI and ML can be great tools to enhance a company’s cybersecurity strategy.
Preventative Strategies & Response
AI and ML can detect and stop malicious files and processes early in the attack cycle. Preventing most commodity malware attacks before they occur is a good way to reduce the attack surface and ease the workload on the malware triage team.
SOC analysts have deep insight into the events on a device in a cybersecurity incident with Accelerated Threat Hunting. This is achieved using AI and Machine Learning.
Instead of manually triaging, analysts are provided with pre-correlated story lines that reveal the relationships between events. This often eliminates the need for further forensics tools.
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Increased Security Policies
Users can choose the level of protection they wish to automate with a security solution that AI backs. Automatic remediation can be activated on suspicious activity if a particular device or user is of critical importance.
In other cases, it might be possible to set a more permissive policy that allows suspicious activity to generate alerts and does not require automated remediation.
How AI & ML Augment Your Security
Combining AI and ML with human experts analysts is the best way for CISOs to build a scalable security platform.
Combining AI and ML with human expert analysts can enhance the strengths of an IT team while also covering weaknesses. Automation is the key to this combination.
Endpoint Protection (EPP), Endpoint Detection & Response (EDR), and Endpoint Protection (EPP), by SentinelOne, seamlessly combine automation and both AI and ML to detect modern attacks and respond quickly, without any intervention.
Businesses can now focus on operations-specific tasks instead of worrying about automation. SentinelOne’s EPP can also replace legacy AM and AV solutions. It can be tailored to meet a business’s needs and even scaled.
Related: How To Develop The Cybersecurity Workforce?
SentinelOne is focused on faster and more efficient action through AI-powered prevention and autonomous detection.
The Singularity platform XDR Platform gives organizations access to all back-end data. It provides a unified view of assets and networks and a layer of real-time security across all assets.
Singularity(TM), Identity is an easy-to-use platform that detects, prevents, responds to, and hunts in all enterprise assets. This allows organizations to see and control what has not been seen before.
This platform is powered by AI and provides advanced threat hunting and complete visibility across all virtual, physical, or cloud-based devices.
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