Striking the right balance between Artificial Intelligence and traditional security measures is crucial, along with ongoing training and vigilance to maximise AI’s potential in cybersecurity
Artificial Intelligence (AI) brings significant advantages to cybersecurity, such as enhanced threat detection and rapid response. However, it is essential to be mindful of the associated risks, including adversarial attacks and biases.
Can AI completely replace human cybersecurity experts? No, AI will not replace human cybersecurity experts.
Although there is, and will continue to be, job displacement as AI technology is leveraged for automation and replaces manual tasks and reduces the demand for specific skill sets, artificial intelligence cannot replace human intelligence.
AI in cybersecurity should complement humans with a balanced approach to use both resources optimally. In cybersecurity AI refers to applying artificial intelligence and machine learning techniques to enhance the security of computer systems, networks, and data from various cyber threats.
It involves using AI algorithms and models to automate tasks, detect anomalies, and make informed real-time decisions to protect against a wide range of cyberattacks.
AI plays a key role in enhancing cybersecurity defences. From a cybersecurity functionality perspective, AI technology is the force behind many features critical to security solutions.
Understanding the benefits of AI technology at an individual level facilitates the transition from traditional, often reactive, security measures to dynamic, proactive, and intelligent solutions.
The most expansive benefit of AI in cybersecurity is its ability to analyse vast amounts of content and deliver insights that allow security teams to quickly and effectively detect and mitigate risk. This core capability drives many of the benefits provided by AI technology.
Proactive defence
AI-powered technology is at the core of proactive cybersecurity defence. By processing inputs from all applicable data sources, AI systems can automate a pre-emptive response to mitigate potential risk in near real-time.
The types of AI technology that enable this are Automation to speed up the defensive response. machine learning to benefit from knowledge of the tactics and techniques used in past cyberattacks and pattern recognition to identify anomalies
Predictive Analysis
Predictive analysis is a technique that uses AI technology, specifically machine learning algorithms. These algorithms analyse information to find patterns and identify specific risk factors and threats.
The machine learning models created from this analysis provide insights that can help security teams predict a future cyberattack.
AI capabilities in predictive analysis include analysing historical data sets, recognising patterns and dynamically incorporating new content into machine learning models.
By being able to predict a potential cyberattack, security teams can take pre-emptive steps to mitigate risk.
Reduced false positives
Cybersecurity solutions are integrating AI to reduce false alarms. Advanced AI algorithms and machine learning capabilities identify patterns in network behaviour far more accurately than traditional rule-based systems.
This reduces the burden on human analysts, stopping legitimate activities from being flagged as threats.
AI technology helps security teams contextualise and differentiate between typical anomalies and actual threats, reducing alert fatigue and optimising their workload. It also minimizes the drain on resources.
Continuous learning
AI is continuously learning and evolving to reduce the risk and impact of cyberattacks. Unlike static security systems, AI-powered cybersecurity technology adapts and learns as new security content becomes available, resulting in ongoing improvements and enhanced effectiveness.
Reinforcement learning, a specialised type of machine learning that trains an algorithm to learn from its environment, is used to ensure optimal results.
With continuous learning, security teams can anticipate new patterns, techniques, and the tactics cyber criminals use. They can improve predictive analysis accuracy over time and optimise security defences to stay ahead of evolving threats.
Cybersecurity capabilities driven by AI technology
Automated response to threats
- Minimising the time between detection and response
- Reducing the workload on security teams by automating some threat-hunting activities
- Taking immediate, automatic action, such as isolating affected systems or blocking malicious IP addresses
Behavioural analytics
- Assessing the potential risk of user activity based on historical and contextual data
- Identifying insider threats by analysing behaviour patterns
- Monitoring user behaviour and network traffic for unusual activity that could signal malicious activity
Security incident forensics
- Analysing security incidents to determine the impact
- Creating a timeline of security incidents based on user behaviours and system changes to establish the sequence of events
- Performing root cause analysis
Threat Detection and Analysis
- Analysing incoming email for sophisticated phishing attacks
- Detecting unknown threats
- Identifying patterns and anomalies that may indicate a potential security threat or fraudulent activity
- Monitoring and securing IoT devices
Vulnerability Management
- Prioritise identified vulnerabilities based on potential impact
- Reduce the time and effort required for manual vulnerability assessments
- Scan networks and systems for vulnerabilities
Key advantages of AI in cybersecurity
Enhanced Threat Detection:
- Identify threats more quickly, accurately, and efficiently
Makes digital infrastructure more resilient and reduces the risk of cyberattacks
Enhanced Security Detection:
- Understand suspicious or malicious activity in context to prioritise response
- Customise security protocols based on specific organisational requirements and individual user behaviour
- Detect fraud using advanced, specialized AI algorithms
- Detecting potential threats in near real-time to expedite response and minimise impact
Just when you were starting to get used to the idea of AI security
AI technology has many benefits for cybersecurity but has attracted concerns from security professionals about its safety and vulnerability.
The potential risks introduced by AI technology need to be understood and the integration of AI technology into cybersecurity strategies need to be examined critically.
Some of these issues are due to the characteristics of AI technology, such as a need for more transparency and questions about data quality.
Biases or inaccuracies in the content feeds used to train an algorithm can impact security decision-making.
This can lead to misleading results for AI algorithms and machine learning models. These are commonly cited concerns that highlight these issues.
To avoid these risks, it is essential that the training data used by AI algorithms and machine learning models is diverse and unbiased.
Vulnerability to AI attacks
AI-powered cybersecurity solutions depend heavily on data to feed machine learning and AI algorithms.
Because of this, security teams have expressed concern about threat actors injecting malicious content to compromise defences. In this case, an algorithm could be manipulated to allow attackers to evade defences.
In addition, AI technology could create hard-to-detect threats, such as AI-powered phishing attacks.
Another concern related to AI being used offensively is malware being combined with AI technology. The malware can learn from an organization’s cyber defence systems and create or find vulnerabilities.
Privacy concerns
AI in cybersecurity is a particular area of concern because of the many US and international laws and regulations that have strict rules about data privacy and how sensitive information can be collected, processed and used.
AI-powered cybersecurity tools gather information from various sources, and in the collection efforts, they commonly scoop up sensitive information.
With threat actors targeting systems for this information, these data stores are at risk for cyberattacks and data breaches.
Also, using AI technology to identify risk factors from large data sets, including private communications, user behaviour, and other sensitive information, can result in compliance violations due to the risk of misuse or unauthorized access.
Dependence on AI
Relying too much on AI can create a cybersecurity skills gap as people depend more on technology than their intelligence.
This can lead to security teams becoming complacent, as they assume that AI systems will detect any potential threats.
To avoid this, it’s important to remember that human intelligence is still crucial in maintaining security. Human expertise brings a unique perspective to threat hunting and threat detection.
To try and replace human intelligence with AI technology, which can harm overall security rather than enhance it.
Ethical dilemmas
The use of AI in cybersecurity raises additional ethical issues. When considering risk factors related to ethical concerns, AI bias and the lack of transparency are the two issues that often come up.
AI bias and lack of transparency can lead to unfair targeting and discrimination of specific users or groups. This can result in misidentification as an insider threat, causing irreparable harm.
Cost of Implementation
Incorporating AI technology into cybersecurity can be expensive and require a lot of resources, including limited human expertise to set up, deploy, and manage the AI systems.
Additionally, AI-powered solutions may need specialised hardware, supporting infrastructure, and significant processing capacity and power to run complex computations.
Although the benefits of utilising AI in cybersecurity are undeniable, organizations must have a comprehensive understanding of the expenses involved to avoid unpleasant surprises.
This report was extracted from material produced by Palo Alto Networks
