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Back in the 1980s a notorious PhD biologist Dr. Popp delivered floppy disks with a Trojan horse virus. And a new industry was born, with soldiers on both sides, with the first cyber commercial antivirus appearing in 1987.
It is a battle that has never ceased? — the first major example that impacted the mainstream was the Samy worm, which brought down MySpace in 2005.
Technologies have dramatically changed since then. Today, corporate cybersecurity teams need to work alongside regulations like GDPR, determine the right level of identity access for employees, and constantly develop security software to protect their data and customers.
Artificial intelligence (AI) changes the game again — on one hand, it gives companies stronger protection against cyber criminals, and on the other, it gives hackers and digital fraudsters a new weapon.
Key Takeaways
- In the 1980s, Dr. Popp’s Trojan horse virus on floppy disks led to the creation of the commercial antivirus industry in 1987.
- The battle against cyber threats persists, with significant incidents like the Samy worm that crippled MySpace in 2005.
- Corporate cybersecurity now involves following regulations like GDPR and the EU AI Act, managing identity access, and developing security software.
- AI can enhance cybersecurity by analyzing behavioral patterns and improving systems like Managed Detection and Response (MDR) and Intrusion Detection Systems.
- The other side of the coin is that while AI can aid in combating cyberattacks, it also enables hackers to create sophisticated scams.
Behavior-Based Detection Systems
Before the AI era, experts in cyber security used signature-based detection tools. A signature is a unique code or identifier that consists of a byte sequence or an instruction — think of it like DNA.
Signature-based detection tools recognize numerous typical signatures. If any suspicious activity appears, they compare it with the library of known signatures and notify users in the event of a match.
On the downside is an obvious limitation — what if an attack is new and the system is unfamiliar with it? In this situation, a signature-based detection tool’s competence is at a dead end.
With unknown threats, machine learning (ML) and statistical artificial intelligence can help — not only checking the signature but also looking further afield.
AI can analyze malicious or unusual behavioral patterns in massive amounts of data traffic, such as a sequence of attempts to fill in the password. It can examine these issues en masse and with precision, decreasing the number of false-positive notifications.
It’s not just one type of attack to monitor — there are many attack vectors to watch out for, including malware, zero-day exploits, phishing, ransomware, Distributed Denial of Service (DDos) attacks, insider threats, or advanced persistent threats (APTs).
4 Ways AI Can Meet Cybersecurity
With some support from software development company Belitsoft, we have compiled four ways AI can be integrated into cybersecurity:
4. Improved Managed Detection and Response (MDR)
MDR systems repetitively check every interaction with the site or platform and report any malicious activity. Complemented with AI and ML, MDR systems analyze behavioral patterns 24/7 and compile profiles of risky operations.
3. Enhanced Intrusion Detection Systems
An intrusion detection system manages the digital security of an organization. It includes methods and ways of detecting cyber attacks, blocking them, and reporting to the staff about potential bottlenecks and measures to reduce the risk.
Signature-based detection tools have proven their inefficiency in the conditions of the modern cyber world. Today, those protecting systems should be armed with behavioral detection.
2. Threat Intelligence
Generative AI allows cyber security engineers to quickly scan code and network traffic and determine any vulnerabilities. The Google Cloud Security AI Workbench runs on the large language model (LLM) Sec-PaLM. It has a set of AI-based tools that detect, summarize, and eliminate security threats. Security experts examine the behavior of scripts that are potentially undermining.
1. Strong Passwords
AI can assist in developing safe passwords that are difficult to crack. PassGPT is an algorithm that uses LLMs and was trained on millions of leaked and cracked passwords on the Internet.
As a result, the system can first predict the probability of hacking and then develop a complex password with progressive sampling. The system creates each new character separately.
What Are the Challenges of AI and Cybersecurity?
The quality of the historical data AI uses to learn from might be ambiguous. Machine learning will generate flaws if the input information contains errors and contradictions. Therefore, maintaining data hygiene must be a priority. Development experts should conduct regular integrity tests and validate encryption keys.
AI helps combat cyberattacks, but hackers also use AI to create even more sophisticated scams. Data poisoning and deepfakes appeared due to AI. AI is the same for all; however, the intentions are different. To be on the safe side, business experts should follow current trends in AI and comply with regulations such as the EU AI Act, in force from August 2024.
Meanwhile, tread carefully when inputting personal, sensitive, or confidential data in AI tools like ChatGPT.
The Bottom Line
The development of artificial intelligence has greatly affected the cybersecurity domain in two ways and will continue to help improve tools like firewalls and protocols.
On the other hand, it gave hackers additional opportunities to expand their fraudulent schemes.
But with the spread of AI into the world undeniable, businesses need to keep up with AI regulations and do their part to promote the ethical appliances of AI technologies.
