Today we will look at how AI is changing cybersecurity and what it means for business. As touched on in our last article, Cybersecurity challenges and trends for 2023, the new year brought with it further acceleration and adoption of artificial intelligence led security systems, anti-virus software and in contrast, automated machine learning hacking tools.
Depending on the size of your organisation, you are potentially vulnerable to billions of attacks a day across a broad spectrum of surfaces, and no team of humans, no matter the size nor resources invested, can effectively monitor every single one of these in real time. This is where a clear benefit of the ability of AI to analyse massive data sets becomes apparent. Billions of threats can be analysed and prioritised in terms of threat level with automated features, utilising machine learning to recognise patterns and repeat offender IP groups.
The same goes for adversaries and bad actors who are increasingly utilising these tools to circumvent neural network type facial recognition and infiltrate and deploy vast amounts of malicious code across a huge number of access surfaces simultaneously, potentially bringing entire networks and servers down.
With businesses having enthusiastically adopted broader AI technology like the Internet of Things (IoT), facial recognition, automated shopping carts, recommendation and customer service bots and smart content, it is only rational that they will look to meeting security concerns with the same technology.
Here we will look at the use of AI in cybersecurity, tools working to protect you, adversaries making use of artificial intelligence and machine learning and how they might impact upon your business.
How AI is used in cybersecurity
AI enabled threat detection has a myriad of potential uses and applications. The ability to automate threat detection is of great value to cybersecurity professionals and businesses wishing to offer the best protection to their users.
Pattern recognition and automated threat prioritisation that empowers teams and mitigate against human error are powerful tools for growing businesses seeking to meet the challenges of the future digital attack landscape.
As an emerging and evolving field in business application, AI cybersecurity specialists are in short supply and high demand, meaning customers are looking to use trusted software and applications to meet the need.
Here are some specific examples of AI in cyber security and risk management:
- AI biometric security – Fingerprint and facial recognition
- Cyber-attack threat detection
- Fake comments and reviews – spam handling
- Financial transaction handling and fraud detection
- Secure authentication steps
- Cloud server security
- Email spam detection and filtering
- ANPR vehicle recognition
- Zero-day exploit detection and other malware detection
- Border security
- Event screening
- Drone operation
Benefits of AI and machine learning for cybersecurity
Security professionals will know that it takes a vast number of resources to secure all attack surfaces in real time. AI is helping data analysts understand threats and the actions of criminals better than ever before. However, these tools and applications can be resource intensive themselves at scale, and proper cybersecurity advice should always be taken when scaling or integrating AI threat detection into your existing systems, and ideally also perform a full security audit after integration to identify any new vulnerabilities.
Cybercriminals are taking advantage of the applications of AI technologies in the implementation of automated and mass deployed phishing and malware.
Aside from threat detection technologies, VPNs and Email encryption are two of the most successful methods companies can use to protect the privacy of the data on their servers against AI led attacks.
Despite the paralleled rise in the activity of cybercriminals employing AI in their scheme, the benefits of AI and its applications are many. Here is a summary of some of the key benefits:
- AI can handle vast amounts of data – most companies will have some protection against elements of their communication and stored data, but still mor often than not there is no human member of staff available to detect threats from traffic and even if you do have, they are not able to perform this function 24/7. AI can sweep vast amounts of IPs and incoming traffic and communication transactions and identify changes to code or attempts to login by unauthorised users in real time.
- Attack Surface Auditing – AI can help you assess the vulnerabilities in your entire network, allowing you to focus resources more efficiently. AI can support and enhance the vulnerability detection of your security staff.
- Zero-day exploit recognition – Machine learning enables AI systems to not only identify threats from known data sets but can identify patterns and risk of new threats as they emerge – something even the most brilliant cybersecurity professionals would struggle with in real time. It is worth nothing that machine learning data sets and technologies that are powerful like this at large scale for bi9g corporations are some of the most expensive tools on the market but considering the average cost of data breaches, it could be considered a worthy investment if you are operating at scale.
- Self-Teaching – AI threat detection can use machine learning to handle the data it has filtered and identify patterns and groups of similar attacks. It can automatically block IP addresses from know threat clusters and add more to the blocked list that it identifies as part of a related network. This requires some human oversight but with appropriate monitoring it can perform a very efficient real time treat detection, enabling security staff resources to be targeted elsewhere.
How AI is used in cyber-attacks by adversaries
We have covered phishing and malware but arguably a more devastating threat from hackers to secure servers is a brute force attack. Hackers use credential stuffing deployed by AI to lists of potential login portals and server access points, email login portals etc. The use machine learning and information on from data breaches to try to force their way into your server, making sometimes thousands of attempts per hour on one site with know password elements. This is why it is never more important than ever to secure your emails and any other access points with encrypted tools and double up your authentication layers.
According to IBMs report last year business email compromise cost GBP £4.41 million. Why not book a Demo with Pie Security and find out how our email encryption tool and data compliance assessments will help your business stay secure and meet European data privacy regulations in 2023.