Today’s digital world resembles an endless deluge of buzzwords and technological catchphrases. For companies, it can be difficult to keep up with the latest trends that aim to provide innovative solutions to their daily problems. Artificial Intelligence (AI) is one of many developments that is changing how companies defend their networks and protect their business information. Combining AI and cybersecurity systems to prevent attacks from succeeding will be vital for running a sustainable business in the future.
While advances in digital technologies brought new efficiencies to every organization, it also added new risks to their operations. Cybercrime has become one of the primary threats that businesses have to deal with every day. A single unsecured printer or a careless employee clicking on the wrong link can lead to a successful cyberattack that puts the company’s operations at risk.
AI and Cybersecurity: Solutions to Prevent Attacks
Cybercrime isn’t going away any time soon. In 2019 alone, the U.S. suffered from 1,473 data breaches, exposing more than 164 million sensitive records. Although this is down from the previous year, criminals have changed strategies and now target smaller businesses instead of large, multinational enterprises. According to a CNBC report, 43% of cyberattacks went after small businesses in 2019.
Another factor that is compounding the issue is that most enterprises report they struggle to detect an attempted breach in the first place. As threats constantly evolve, it’s becoming a challenge for security professionals to stay ahead of new exploits and vulnerabilities in their networks. This is why researchers turned to innovations in AI and Machine Learning (ML) to help with the heavy lifting.
Currently, 69% of enterprises think AI will be a necessary component to respond effectively to a cyberattack, while 48% plan to increase their budgets for AI-powered cybersecurity solutions. Here are three ways AI is improving cybersecurity today.
1. Quicker Threat Detection
Protecting a company’s networks and information systems isn’t easy. These ecosystems rely on multiple layers of hardware and software systems, all working together, to remain operational. For security professionals, it’s simply not possible to monitor all the interactions between these systems. AI and ML tools are uniquely suited to analyze all the traffic on the system and initiate protective actions if it detects suspicious activity.
AI can help network security professionals to:
- Monitor and analyze user entity behavior with deep learning.
- Detect anomalies in network behavior that could indicate a breach.
- Automate many of the threat definition update tasks in real time.
- Execute preventative measures in cases where it suspects a breach has succeeded.
- Establish data-loss prevention controls and stop device influx on the network.
By analyzing normal patterns of information flowing through the network, AI can quickly identify behavioral anomalies. This can help the security team to be proactive and respond to any threat promptly.
2. Improved Threat Response Times
It’s not enough to detect a threat as the company will also need to respond to it effectively. AI can help automate many of the preventative measures until a network security resource can review the attack and resolve the issue. AI systems are capable of scanning millions of exchanges and highlight any of them that could pose a risk to the company.
3. Threat Analysis and Categorization
A major headache for network security professionals is dealing with thousands of incidents, many of which are false positives. AI can help security professionals sort and categorize cybersecurity incidents and thus enable them to act on the most important issues first. This reduces the burden on staff while also improving the accuracy of incident reporting.
Cybersecurity vs Artificial Intelligence in Cyberattacks
Businesses should be aware that AI isn’t just helping protect networks against cyberattacks. Cybercriminals are using the same capabilities of AI to supercharge their attacks on networks. By using AI-enabled malware, criminals can rewrite their code in real time, making it extremely difficult to detect using a previous signature. This is why human intervention will still be required for the foreseeable future to analyze complex threats.
There’s also an emerging risk of model hacking. Criminals have started attempting to co-opt AI systems using the same principles companies use to protect their networks. Instead of using AI to attack the system, hackers can use similar tools to manipulate patterns and create blind spots in the detection algorithms. By turning the AI system against itself, criminals can successfully exploit the environment. To protect against this, security professionals need to harden their models and ensure they have the basic protections in place.
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