How AI & Machine Learning Can Provide Defense Against Cybercrime
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Generated: 8/29/2022
How AI & Machine Learning Can Provide Defense Against Cybercrime
Cybersecurity is a huge and growing concern for companies that are developing or have developed new software. This is especially true for smaller companies that may be dealing with a growing list of cyber risks, as well as those facing legal actions over these cyber threats.
There is a growing number of cyber threats that involve sophisticated and costly cybercrime campaigns focused on infiltrating an organization’s information systems.
These cyber crimes can involve phishing, malware, exploits, ransomware and other kinds of malicious software that infiltrate an organization’s computers, networks, and mobile devices. When these sophisticated cyber criminals fail, they have the option of leaving a trail of encrypted emails, malware, and logs stored on compromised servers. These are the tools an adversary will use to mask their identity. This can be quite daunting, especially at a larger company and in jurisdictions that focus on high value targets.
When a cyber attack gets more complex and persistent, the attacker may attempt to access more than one target. In addition to breaching the security of one organization, the attacker could attempt to hack into other computers, including those at a different company, and obtain valuable information.
Small and medium sized companies are often the initial targets of these attacks. These firms may be more vulnerable since they do not have the same security resources to counter such attacks.
The recent rise in cyber security breaches at several of the nation’s largest technology players (including Anthem, Home Depot, Target and Equifax) has once again put the spotlight on the need for better tools to deal with these complex cyber attacks. The goal should be to keep the bad guys where they belong—outside of an organization’s walls.
It is becoming increasingly difficult for security teams to identify the hackers during the time of the attack and before the damage is done. Security professionals often find themselves fighting for a short period of time to contain the damage before it escalates to an even more dangerous state.
As the cyber attacks have become more sophisticated, companies have become more reliant on their software and mobile applications to store and process valuable data. Hackers can target a variety of data sources, including information stored in a company’s software, mobile devices or cloud computing applications. Some of the most commonly attacked areas are customer records, HR data, employee data, business planning and other sensitive materials.
The cybersecurity threat has become part of every day life for many people. For companies that have relied on their software for almost everything, cybersecurity has become a major concern when developing and deploying new applications and IT infrastructure.
New technologies such as artificial intelligence and machine learning are helping organizations with the fight to contain their cyber risks. These technologies are being applied to identify and isolate malicious activity in real time, even if the attacks are sophisticated.
Here are a few of the ways artificial intelligence and machine learning are being used to better defend against cybercriminals.
The following example shows how AI and machine learning can be used to detect insider threat.
Prevention
It is quite easy for a person to use social engineering to steal an organization’s confidential, valuable data. While AI and machine learning are not fully capable of preventing cyber crimes, the technology can be used to monitor an employees activities. The system can be set to alert an authorized user if unusual behaviors are detected.
An example of the potential for an automated AI and machine learning solution is shown in the following example.
While some AI and machine learning technology is available for security use, most are focused on one aspect of the solution such as video analysis or identifying unusual patterns in the logs of a system. This can help with the identification of the source of a cyber threat. However, many organizations still lack the expertise to develop and deploy their own technology.
One way to address this problem is to partner with an independent provider of AI and security technology.
Palo Alto Networks announced in March that it was partnering with an Israeli security technology firm, Check Point Software Technologies, to develop the world’s first AI based security platform. Check Point will help Palo Alto customers deploy their products in North America and Asia.
One of the main benefits for Palo Alto and Check Point is to develop new AI and Machine Learning-based products for the market. In other words, they can focus their resources on developing other key products.
Many organizations have already begun the process of developing their own security tools and platforms. While it’s great innovation to develop their own solutions, it can be costly and time consuming. By partnering, an organization can share their expertise and resources and develop AI and AI-based solutions on a cost efficient basis.
Detection
It is clear the traditional methods of defense, which focus on manual processes and human review, do not scale with modern attacks. AI and machine learning are designed to act as systems for analysis similar to humans.
There are a number of new tools that are helping to create effective cyber intelligence solutions. AI and machine learning can be used to identify attack indicators and patterns.
In addition to helping to detect an attack before it spreads, AI and machine learning can be used to help identify potential attackers.
There are two ways an AI and machine learning system can be used to identify hackers.
First, AI-based software can find unusual activity in a system or network. For example, if a software is being used to execute a known virus or worm, a program that detects or attempts to block that activity can provide the first indication that there has been an attack.
Second, by monitoring and analyzing traffic patterns from users interacting with a system, AI algorithms can identify new attacks on systems.
AI and machine learning are also useful to detect malicious activity on a user’s device.
Many organizations are finding that the increased use of mobile devices has led to an increase in the number of cyber threats that use malicious mobile apps.
When users access software that is installed on a mobile device, the machine learning software monitors how that software reacts to various actions (like using and deleting a file or making a phone call). A pattern of normal behavior can be identified. When the AI system detects an odd event that is not part of that normal activity, it triggers an alert. This AI based system can prevent the malware from executing additional malicious actions that would harm the mobile device and its data.
Mitigation
Once someone has been identified as a threat, AI and machine learning systems can provide the software with relevant additional information about the attack. This helps the software to identify the most effective action to take (e.g., prevent a malware attack, encrypt a device, shut off a network or move data). The AI system will use the data and information to identify the best course of action to mitigate the threat and prevent it from being spread. AI and machine learning systems can scan a network and identify the best way to contain a threat on a mobile device. They can identify and block any malicious software that a device might inadvertently download. If the AI system notices that an access point is being used only to distribute malicious software, it can identify the device and block the connection.
It is often more cost efficient for businesses to hire cybersecurity professionals to handle the cybersecurity issues after a breach occurs.
For those organizations that have only focused on cybersecurity, there is a high chance they will become a victim of cybercrime. The cost of a breach can be significant. It might take two to three years before the company is fully back in business. Even with strong security measures, organizations sometimes lose important data.
AI and machine learning are a promising way to contain these risks. By monitoring an organization’s network or device, it may be able to spot an attack before it escalates to an extremely dangerous state. AI and machine learning systems can identify suspicious activity before a cyber threat becomes a disaster. The AI/ML programs can help to prevent the data and information from being exfiltrated.
AI and machine learning can also identify and potentially alert the correct department for other support that may be needed. One of the major components of dealing with a cyber threat is the ability to contain the attack and data and to move as quickly as possible to eliminate the threat.
AI and machine learning systems don’t address every security issue, but they can be used to detect attacks very quickly. An AI and machine learning system can find the hacker’s email address, IP address or other information that can be used to monitor the hacker’s online activity. This information allows an organization to contain and block the hacker’s activity. These tools allow an organization to monitor its systems on a real time basis, and provide immediate reports to the appropriate department. It may also help the organization to block further attacks and prevent a company from being exploited.
Reducing risks and improving defenses
There is no doubt that AI and machine learning can provide critical new information about cyberthreats. There are many potential benefits of an AI and machine learning solution for any organization.
In the event of a cyber threat, there are few organizations that can adequately protect against an attack when the system is first breached. A real-time security solution that can identify the attackers, as well as contain the damage once the attack begins, can greatly reduce the impact of a cyber threat.
New AI and machine learning capabilities allow organizations to monitor its networks for signs of malicious, rogue activity and use the information to take action. This ability is extremely important when organizations are facing cyber threats.
An AI/ML tool can be a great addition to an overall security strategy.
In a situation where a targeted hacker has established a foothold, a security provider might face a difficult decision. They can use their own resources against the attacker or they may partner with someone they trust.
Cybersecurity is a huge and growing concern for companies that are developing or have developed new software. This is especially true for smaller companies that may be dealing with a growing list of cyber risks, as well as those facing legal actions over these cyber threats.
There is a growing number of cyber threats that involve sophisticated and costly cybercrime campaigns focused on infiltrating an organization’s information systems.
These cyber crimes can involve phishing, malware, exploits, ransomware and other kinds of malicious software that infiltrate an organization’s computers, networks, and mobile devices. When these sophisticated cyber criminals fail, they have the option of leaving a trail of encrypted emails, malware, and logs stored on compromised servers. These are the tools an adversary will use to mask their identity. This can be quite daunting, especially at a larger company and in jurisdictions that focus on high value targets.
When a cyber attack gets more complex and persistent, the attacker may attempt to access more than one target. In addition to breaching the security of one organization, the attacker could attempt to hack into other computers, including those at a different company, and obtain valuable information.
Small and medium sized companies are often the initial targets of these attacks. These firms may be more vulnerable since they do not have the same security resources to counter such attacks.
The recent rise in cyber security breaches at several of the nation’s largest technology players (including Anthem, Home Depot, Target and Equifax) has once again put the spotlight on the need for better tools to deal with these complex cyber attacks. The goal should be to keep the bad guys where they belong—outside of an organization’s walls.
It is becoming increasingly difficult for security teams to identify the hackers during the time of the attack and before the damage is done. Security professionals often find themselves fighting for a short period of time to contain the damage before it escalates to an even more dangerous state.
As the cyber attacks have become more sophisticated, companies have become more reliant on their software and mobile applications to store and process valuable data. Hackers can target a variety of data sources, including information stored in a company’s software, mobile devices or cloud computing applications. Some of the most commonly attacked areas are customer records, HR data, employee data, business planning and other sensitive materials.
The cybersecurity threat has become part of every day life for many people. For companies that have relied on their software for almost everything, cybersecurity has become a major concern when developing and deploying new applications and IT infrastructure.
New technologies such as artificial intelligence and machine learning are helping organizations with the fight to contain their cyber risks. These technologies are being applied to identify and isolate malicious activity in real time, even if the attacks are sophisticated.
Here are a few of the ways artificial intelligence and machine learning are being used to better defend against cybercriminals.
The following example shows how AI and machine learning can be used to detect insider threat.
Prevention
It is quite easy for a person to use social engineering to steal an organization’s confidential, valuable data. While AI and machine learning are not fully capable of preventing cyber crimes, the technology can be used to monitor an employees activities. The system can be set to alert an authorized user if unusual behaviors are detected.
An example of the potential for an automated AI and machine learning solution is shown in the following example.
While some AI and machine learning technology is available for security use, most are focused on one aspect of the solution such as video analysis or identifying unusual patterns in the logs of a system. This can help with the identification of the source of a cyber threat. However, many organizations still lack the expertise to develop and deploy their own technology.
One way to address this problem is to partner with an independent provider of AI and security technology.
Palo Alto Networks announced in March that it was partnering with an Israeli security technology firm, Check Point Software Technologies, to develop the world’s first AI based security platform. Check Point will help Palo Alto customers deploy their products in North America and Asia.
One of the main benefits for Palo Alto and Check Point is to develop new AI and Machine Learning-based products for the market. In other words, they can focus their resources on developing other key products.
Many organizations have already begun the process of developing their own security tools and platforms. While it’s great innovation to develop their own solutions, it can be costly and time consuming. By partnering, an organization can share their expertise and resources and develop AI and AI-based solutions on a cost efficient basis.
Detection
It is clear the traditional methods of defense, which focus on manual processes and human review, do not scale with modern attacks. AI and machine learning are designed to act as systems for analysis similar to humans.
There are a number of new tools that are helping to create effective cyber intelligence solutions. AI and machine learning can be used to identify attack indicators and patterns.
In addition to helping to detect an attack before it spreads, AI and machine learning can be used to help identify potential attackers.
There are two ways an AI and machine learning system can be used to identify hackers.
First, AI-based software can find unusual activity in a system or network. For example, if a software is being used to execute a known virus or worm, a program that detects or attempts to block that activity can provide the first indication that there has been an attack.
Second, by monitoring and analyzing traffic patterns from users interacting with a system, AI algorithms can identify new attacks on systems.
AI and machine learning are also useful to detect malicious activity on a user’s device.
Many organizations are finding that the increased use of mobile devices has led to an increase in the number of cyber threats that use malicious mobile apps.
When users access software that is installed on a mobile device, the machine learning software monitors how that software reacts to various actions (like using and deleting a file or making a phone call). A pattern of normal behavior can be identified. When the AI system detects an odd event that is not part of that normal activity, it triggers an alert. This AI based system can prevent the malware from executing additional malicious actions that would harm the mobile device and its data.
Mitigation
Once someone has been identified as a threat, AI and machine learning systems can provide the software with relevant additional information about the attack. This helps the software to identify the most effective action to take (e.g., prevent a malware attack, encrypt a device, shut off a network or move data). The AI system will use the data and information to identify the best course of action to mitigate the threat and prevent it from being spread. AI and machine learning systems can scan a network and identify the best way to contain a threat on a mobile device. They can identify and block any malicious software that a device might inadvertently download. If the AI system notices that an access point is being used only to distribute malicious software, it can identify the device and block the connection.
It is often more cost efficient for businesses to hire cybersecurity professionals to handle the cybersecurity issues after a breach occurs.
For those organizations that have only focused on cybersecurity, there is a high chance they will become a victim of cybercrime. The cost of a breach can be significant. It might take two to three years before the company is fully back in business. Even with strong security measures, organizations sometimes lose important data.
AI and machine learning are a promising way to contain these risks. By monitoring an organization’s network or device, it may be able to spot an attack before it escalates to an extremely dangerous state. AI and machine learning systems can identify suspicious activity before a cyber threat becomes a disaster. The AI/ML programs can help to prevent the data and information from being exfiltrated.
AI and machine learning can also identify and potentially alert the correct department for other support that may be needed. One of the major components of dealing with a cyber threat is the ability to contain the attack and data and to move as quickly as possible to eliminate the threat.
AI and machine learning systems don’t address every security issue, but they can be used to detect attacks very quickly. An AI and machine learning system can find the hacker’s email address, IP address or other information that can be used to monitor the hacker’s online activity. This information allows an organization to contain and block the hacker’s activity. These tools allow an organization to monitor its systems on a real time basis, and provide immediate reports to the appropriate department. It may also help the organization to block further attacks and prevent a company from being exploited.
Reducing risks and improving defenses
There is no doubt that AI and machine learning can provide critical new information about cyberthreats. There are many potential benefits of an AI and machine learning solution for any organization.
In the event of a cyber threat, there are few organizations that can adequately protect against an attack when the system is first breached. A real-time security solution that can identify the attackers, as well as contain the damage once the attack begins, can greatly reduce the impact of a cyber threat.
New AI and machine learning capabilities allow organizations to monitor its networks for signs of malicious, rogue activity and use the information to take action. This ability is extremely important when organizations are facing cyber threats.
An AI/ML tool can be a great addition to an overall security strategy.
In a situation where a targeted hacker has established a foothold, a security provider might face a difficult decision. They can use their own resources against the attacker or they may partner with someone they trust.