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While artificial intelligence (AI) introduces a degree of vulnerability into data networks, the technology also plays a crucial role in protecting those online systems from attack. When deployed properly, AI systems can help detect threats before they land, alert appropriate staff members, identify potential malware, and ultimately keep organizational and individual data safe and secure.

Making the most of AI in cybersecurity means adopting advanced techniques like machine learning, natural language processing, and knowledge representation. These technologies are key in identifying and mitigating risks and implementing effective strategies to keep networks secure. Such strategies include:

1. Behavior anomaly detection.

The traditional approaches to security rely on established indicators of compromise and attack signatures to detect potential incidents or threats. However, this is not a practical approach as thousands of new attacks are launched each year. Information technology (IT) departments are left playing catchup as they attempt to identify threats, and organizations become vulnerable to novel threat approaches.

However, organizations can harness AI’s ability to analyze huge amounts of data—much more than a human can—and identify trends. For example, AI can be trained to detect changes in patterns that could be indicative of malicious activity. AI can analyze massive volumes of data on network, device, and user activity, establish a normal baseline pattern, and detect even small anomalies that could signal a security threat or incident. Beyond mere detection, AI can also be trained to categorize any outliers it detects in a stream of data, assign a priority level, and distinguish small threats from major ones. This can help prevent alert fatigue, which occurs when users receive so many alerts that they become background noise and lose their meaning.

2. Vulnerability management.

Each year, thousands of new system vulnerabilities are uncovered. Unfortunately, these vulnerabilities often come to light only when they become the object of a costly attack. Moreover, protecting systems in real time after these vulnerabilities are recognized can be difficult, or even impossible for most organizations.

AI can assist by identifying system vulnerabilities and correcting them before they become the target of attack. For example, organizations can use AI to simulate social engineering attacks on their users and learn about vulnerabilities. Similarly, penetration testing is another area where AI could be immensely useful and cost-effective. Cybersecurity teams can deploy AI to intentionally probe networks and software to find weaknesses.

Instead of constantly playing catch-up, AI can help organizations stay ahead of threats. Usually, security companies offer patches for known and exploited vulnerabilities, but this could change in the future—patches can be developed for AI-identified vulnerabilities that have not yet been used by criminals.

3. Phishing and other scam detection

Hackers frequently target organizations with phishing scams. Typically, a phishing attack involves a seemingly legitimate email that attempts to trick the recipient into giving away sensitive or private information, like their username and password to a company system. The email may contain malicious links or attachments; if the user clicks on them, malware, spyware, and other malicious technologies can be downloaded to their device.

Luckily, AI made for email security can identify these attacks and mark these emails as spam with risk of phishing or as legitimate. Through machine learning, AI can evolve as it encounters new threats and becomes more accustomed to typical patterns in user email correspondence. Machine learning is a key tool for preventing some of the more advanced threats from attackers, such as spear phishing. This technique targets high-ranking persons within an organization; the hacker usually conducts research on their target to learn their name, job title, associates, and even personal interests, so the attack may be more difficult to spot than an anonymous mass phishing campaign. 

4. Password authentication.

Two-factor authentication (2FA) and multi-factor authentication (MFA) are commonplace nowadays; these methods call for additional measures beyond a username and password to confirm a user’s identity before allowing access to a system or program. For example, after a user types in their username and password to log in to their account, they may be prompted to enter a verification code sent by text or email. AI can also power MFA methods like facial recognition, fingerprint scanners, and CAPTCHA. The last method requires a user to answer a question or perform a task that requires logic and reasoning to prove a human is originating the request.

Without MFA, networks are vulnerable to strategies like credential stuffing and brute-force attacks, which attempt to get past security checkpoints by trying many different password iterations. AI can play a key role in identifying these types of attacks and authenticating legitimate users to ensure their access to key services is not interrupted. In addition, AI can authenticate a user by identifying their behavior patterns over time, creating a profile, and detecting anomalies, like a login attempt at a strange hour or via an unknown device.