As technology advances, so do the threats to our online security. Cybercriminals are constantly developing new ways to infiltrate systems and steal sensitive data. To counter these ever-evolving threats, cybersecurity professionals are turning to artificial intelligence (AI) and machine learning (ML) technologies for help. Here, we’ll explore how AI is changing the game in cybersecurity tools and why it’s becoming a crucial component in protecting ourselves against cyber attacks.
AI – a brief reminder
Artificial Intelligence (AI) is a branch of computer science that focuses on creating intelligent machines. These machines can perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. AI systems use algorithms to analyze and interpret data in order to make decisions or take actions.
Machine Learning (ML) is an important subfield of AI that involves training machine models using large datasets. The goal is for the machine to learn patterns and relationships within the data so it can make accurate predictions or classifications when presented with new information.
But what makes these tools so effective? Unlike traditional security measures which rely on predefined rules or signatures to identify malicious activities, AI-powered tools have the ability to adapt and evolve based on ongoing analysis of new threat patterns. This means they can recognize even previously unknown attacks quickly and accurately.
Types of AI powered cybersecurity tools
AI in SOC
Automating security operations ensures that machines do the “heavy lifting” of tedious, monotonous, or burdensome tasks. They can process massive amounts of data at machine speeds and machine scale all day, every day – because machines never sleep! Your “human team” is then free to devote their singular reasoning and expertise to more pressing and proactive security activities, and your business will be better for it.
SIEM evolved to utilize AI in addressing threats
Growing demand for intelligent cybersecurity solutions resulted in innovative approaches to fighting cyber threats, such AI and ML-driven SIEM. These go beyond traditional SIEM capabilities, as they incorporate technologies that enhance threat detection, response, and predictive analysis. Some of these include:
Security Orchestration, Automation and Response (SOAR)
These platforms leverage AI and ML in automating repetitive tasks, streamlining incident response processes, and empowering organizations with more informed decision-making capabilities when faced with a cyberattack. Integration with other tools allows SOAR solutions to build a holistic security ecosystem that can adapt to new threats as they emerge.
User and Entity Behavior Analysis (UEBA)
These solutions utilize AI and ML algorithms to monitor the behavior patterns of users and entities across an organization’s digital environment. UEBA identifies deviations from the norm and can therefore detect potential insider threats, compromised accounts, and other security risks. This adds an extra protective layer to a business’ cybersecurity defense.
Endpoint Security solutions that reduces the attack surface and prevents attacks from running before impacting the system. Endpoint Detection and Response (EDR) automatically detects potential threats and quickly sees where to focus attention and which endpoints may be impacted.
EDR solutions focus on monitoring and collecting data from endpoints, which include IoT devices, smartphones, and BYOD devices, in order to identify potential threats. With AI and ML solutions, EDR can provide real-time analysis and thus respond to threats also in real-time. This lets businesses mitigate the risks associated with an expanding attack surface, in line with today’s increasing trend of utilizing BYOD and remote-work arrangements.
Extended Detection and Response (XDR) goes beyond the endpoint by incorporating cross-product data sources or even more visibility.
Managed Detection and Response (MDR) where a team of threat hunters and response experts take targeted actions to neutralize threats.
Anti-ransomware provides “fire protection”, automatic file recovery, and behavioral analysis to stop ransomware and boot record attacks. Deep learning technology through artificial intelligence can be built into a solution to detect both known and unknown malware without relying on signatures.
Tools and Platforms for Artificial Intelligence in Cyber Security
On the market, there are cybersecurity tools with a deep learning neural network incorporated, that transforms security from a reactive to a predictive approach to defend against possible threats and cyber attacks.
There are a number of tools and platforms that have been created specifically for artificial intelligence in cyber security.
Some of the most popular tools and platforms for artificial intelligence in cyber security include:
– IBM Watson for Cyber Security: This tool uses cognitive computing to help identify, understand and respond to cybersecurity threats.
– Cisco Talos: This platform provides advanced threat protection by using machine learning to detect and block malicious activity.
– Symantec Endpoint Protection: This solution uses AI to protect endpoints against malware, zero-day threats and other attacks.
– CrowdStrike Falcon: This platform provides next-generation antivirus protection by using machine learning to detect and block malware.
– FireEye Helix: this platform uses ML and AI to detect, investigate, and respond to cyber threats.
– Palo Alto Networks WildFire: uses ML to identify and block malicious files, URLs, and emails.
– Microsoft Defender Advanced Threat Protection: detects threats and protects organizations.
– Alertlogic Cloud Insight: detects and responds to emerging threats.
Benefits of AI-Powered Cybersecurity Systems
Perhaps most importantly, AI can provide advanced threat detection and prevention capabilities. AI can constantly monitor network for anomalies and potential threats, providing near-instant alerts when anything suspicious is detected.
In addition, AI can also assist in response and recovery efforts in the event that your systems are breached. For example, many AI-powered cybersecurity platforms include features like automatic incident response and real-time threat analysis, which can help minimize the damage caused by a breach and get your systems back up and running as quickly as possible.
Investing in an AI-powered cybersecurity solution is a smart choice for any business that wants to stay ahead of the curve when it comes to protecting its critical data and infrastructure.
Common Challenges With Implementing AI in Cyber Security
While AI is seen as a valuable tool to enhance cybersecurity, there are also some potential Challenges to consider.
– One major concern is the risk of false positives and false negatives in automated threat detection systems powered by ML algorithms.
– In addition, attackers could potentially use AI themselves to launch more sophisticated attacks that evade detection by AI-powered defenses. This would be especially concerning if malicious actors were able to leverage the same AI technologies used for defense against their targets.
– The risk of bias. ML models are only as good as the data they are trained on, and if that data contains inherent biases or inaccuracies, it can lead to flawed results. In terms of cybersecurity, this could result in overlooking certain types of threats or flagging benign activity as suspicious.
– There is also a shortage of skilled professionals who are familiar with both cybersecurity and AI technology. Without proper training and understanding of these complex tools, organizations may not be able to fully realize the benefits or mitigate any potential risks associated with them.
– Lack of data – In order to train AI models for cybersecurity, they need access to large volumes of data. This can be a challenge, as many organizations are reluctant to share their data due to privacy and security concerns.
– Implementation costs: Implementing AI can be expensive, as it requires specialized hardware and software. Organizations need to weigh the costs and benefits of AI before deciding if it is right for them.
How to Get Started – Tips for Implementing AI in CyberSecurity System
If you’re like most experts, you know that you need to be using some form of AI in your cybersecurity systems, but you may not know where to start. Here are a few tips to help you get started:
1. Understand your current system and its weaknesses. Before you can implement AI, you need to first understand what your current system is lacking. This will help you identify the areas where AI can be most helpful.
2. Research different AI solutions. There are many different AI solutions on the market, so it’s important to do your research and find the one that best fits your needs.
3. Implement AI gradually. You don’t need to implement all of AI overnight – start with incorporating it into one or two areas of your cybersecurity system and then expand from there.
4. Monitor results and adjust as needed. As with any new technology, it’s important to monitor the results of using AI in your cybersecurity system and make adjustments as needed.
Will AI make things more secure?
Artificial intelligence is revolutionizing the world of cyber security. With the rise in sophisticated and complex cyber threats, AI-powered tools are becoming more essential than ever before.
The integration of machine learning algorithms and AI into SOC, Endpoint Detection and Response (EDR), Extended Detection and Response (XDR), Security Orchestration, Automation, and Response (SOAR) has significantly improved cyber defenses by detecting advanced threats that traditional security solutions may miss.
AI-powered cybersecurity tools have proven to be effective in preventing data breaches, identifying vulnerabilities early on, reducing response times to potential attacks and minimizing human error.
However, as with any technology solution there are risks associated with its usage. The deployment of such advanced technologies requires adequate training for IT professionals who will manage them. Additionally, there is a need to ensure that these systems are monitored closely to prevent malicious behavior or unintended consequences from occurring.
Questions You Should Ask Potential SIEM Solution Provider
How does the product meet our auditing and compliance needs?Although it has been supplanted by threat detection, compliance management is still one of the most frequent use cases for SIEM solutions. It can provide coverage for ISO27001, PCI DSS, and SOX, among many others. You need to make sure that a potential solution is compatible with your specific industry regulations. Ask your potential vendor to demonstrate a clear relationship between your industry compliance needs and their policies.
Do you offer assistance with deployment or training for personnel?
Once deployed, a SIEM solution requires a dedicated team of skilled analysts to manage and ensure effective use. You should inquire with any potential SIEM vendors about what they can offer to offset the stress of deployment and operation. (SIEM Buyers Guide)
Cost considerations
Enterprise-grade SIEM systems can cost hundreds of thousands of euros. It requires initial license costs, often arranged as base price plus user or node, database costs for servers, hiring and training personnel, and costs of additional external storage. Not all businesses can afford it. Some SIEM vendors offer a lightweight version with basic log management and reporting capabilities without advanced analytics, a good alternative for businesses looking to save money.
For relatively small companies or those with simple IT infrastructure, the cost of an AI-enabled SIEM would probably be prohibitive while offering little to no advantage when coupled with good security hygiene.
A large and complex IT infrastructure might easily justify the costs. However, it is always advisable to get a detailed evaluation of the products.
Data security, cloud security, and infrastructure protection are the fastest-growing areas of security spending through 2023. In 2018, a whopping $7.1B was spent on AI-based cybersecurity systems and services, which is predicted to reach $30.9B in 2025, according to Zion.
Operating considerations and benefits
SIEM also requires constant monitoring from the IT security team. Manually monitoring every system is not only exhausting but will also induce burnout. SIEM backed with AI capabilities can offer:
- Self-learning to automate repetitive, unstructured processes
- The ability to automate system alerts
- Data visualization dashboards
- Real-time analytics
- Top-level enterprise security
- Cross-department sharing
SIEM still cannot match the power of human ingenuity and collective collaboration of cybersecurity adversaries. Hence, the enterprise’s security team needs to take the lead on threat hunting and incident response. However, a properly implemented AI-augmented SIEM can optimize these processes through its predictive and automated capabilities.
Such SIEM can provide the groundwork for an IT security team, for instance, through security correlation rules, it can perform automated threat hunting. The AI element in SIEM can identify false positives through the automatic application of contextualization on all alerts. AI-augmented SIEM can speed up the detection and response times.
Essentially, you can think of this technology not only as a second pair of eyes, but also another set of hands. However, keep in mind that specialized human intelligence will always triumph over AI.
Other AI powered options
If you already have a SIEM and want to hold onto it, you can complement it with an AI and automation driven MDR service that coexists alongside your current system. As a reminder, Managed Detection and Response (MDR) is an outsourced service that provides organizations with threat hunting services and responses. Also, there is a team of experts who monitor your endpoints, networks, and alerts.
Another alternative is to decommission the SIEM entirely and upgrade to a SOAR platform. Whether you choose an MDR service or a SOAR platform for detection and response depends on your unique business needs and, of course, your budget.
In the last four or five years, we have seen security teams trying to use Security Orchestration, Automation, and Response (SOAR) products to “clean up” some of the noise that the sensors generate – combining SOAR with SIEM.
The Takeaway
AI and ML-driven SIEM solutions continue to gain traction. Therefore, it is crucial for you as a decision-maker to recognize the potential of these technologies and prioritize their adoption. Then, you can make more informed choices about the tools and solutions.
To ensure a successful transition to AI and ML-driven cybersecurity solutions, your organization must also invest in building a skilled workforce that understands these technologies and can effectively utilize them. This may include providing training and development opportunities, as well as collaborating with academic institutions and industry partners to address the AI and ML skills gap.
By carefully considering these challenges and working towards overcoming them, you can successfully harness the potential of AI and ML-driven SIEM solutions to enhance the cybersecurity posture in an increasingly complex digital landscape.
