What is Predictive AI and How is it Being Used in Cybersecurity? – Security Boulevard

What is Predictive AI and How is it Being Used in Cybersecurity?  Security Boulevard

The predictive AI field of machine learning collects, analyzes, and tests data to predict future possibilities. AI’s neurological network is patterned on the human brain. But AI works on a scale that goes far beyond what is humanly possible. The top uses for predictive AI technologies to protect sensitive data and systems are in network detection and response (NDR), threat detection, and cybercrime prevention.

Growing Network Vulnerability

As the COVID-19 pandemic increases the world’s digital dependency, businesses become more vulnerable to cyberattacks attempted every 39 seconds. Cyberattacks, data theft, and data fraud are among the top five global risks in the next 10 years. 

Odds favor hackers. Worldwide network traffic growth makes data more widely accessible. Security teams must find and protect every possible weak point in massive systems. But hackers only need to find one vulnerability to breach a network.

Skilled worker shortage. A global shortage of over 40 million IT security workers makes it hard for businesses to stop the unending flow of cyberattacks. The exponentially greater power of predictive AI solves this problem. 

Evolution of Predictive AI

Machine learning for network protection evolved in three distinct waves:

First wave. Humans create rules for supervised AI to follow. This predictive AI method collects network data and creates a historical baseline. Anomalies are detected when incoming data differs from the baseline. First wave AI can solve complex problems and is evolving, but it has flaws:

  • It takes months for first wave AI  to gather enough historical data to form a baseline.
  • Fresh data produces false positives when measured only against historical data.
  • A baseline of old data is useless against the evolving methods of hackers.

Second wave. Supervised and unsupervised machines create rules by using statistical methods that include regression, clustering, and classification. Those rules are used to make predictions. Although superior to first wave AI, the second wave still has disadvantages:

  • Limited context. Second wave AI can’t detect anomalies when network conditions change. In a constantly evolving environment, they must create rule after rule for huge amounts of data.
  • Weak reasoning. Machines learn only from the data they collect. Second wave AI can’t draw conclusions and make predictions through its own reasoning.

Third wave. Unsupervised—or self-supervised—machines learn by applying their own reasoning and analysis to changing situations. With this learned knowledge, third wave AI draws new conclusions and increases its own learning capacity. 

How Predictive AI Protects Networks

NDR

To protect worldwide networks, security teams watch for anomalies in dataflow with NDR. Cybercriminals introduce viral code to vulnerable systems hidden in the massive transfer of data. As cybersecurity evolves, bad actors work hard to keep their cybercrime methods one step ahead. To avoid next-generation hacks and breaches, security teams and their forensic investigation methods must become even powerful. 

First and second wave cybersecurity solutions that work with traditional Security Information and Event Management (SIEM) are flawed: 

  • Overpromise on analytics, but basic log storage, incremental analytics, and maintenance costs are massive.
  • Flag tons of false positives because of their context limitations.

MixMode adds an AI layer to SIEM to increase efficiency and decrease data migration, redundancy, and latency.

Threat Detection

The immense data overload companies currently face demands reliable and accurate protection against new attacks. Third wave AI-enabled security monitoring detects and surfaces threats in real time before they compromise your network. 

A best-in-class AI identifies patterns and understands what normal traffic looks like in changing conditions. Without human tuning, self-supervised AI solutions learn over time to fix the problems that traditional solutions can’t solve. They identify and surface new deviations from the baseline, quickly find threats, and alert security personnel.

Only an evolving baseline of normal network behavior—built with self-supervised AI—can detect anomalies accurately. The ground-breaking MixMode cybersecurity platform detects threats in real time with a patented AI engine.

Cybercrime Prevention

The only way to keep a company safe 24/7 is to alert users before attacks happen. Hackers execute zero-day attacks to exploit unknown vulnerabilities in real time. First and second wave network security tools are helpless against these attacks. 

Only a third wave, unsupervised AI—MixMode—can detect and surface zero-day attacks in real time before catastrophic damage is done. MixMode gives you the power to fight back:

  •  AI-driven alerts on known vulnerabilities
  • Best-in-class threat hunting tooling
  • IP addresses of hackers before they attack

MixMode–the Logical Choice

The security provided by traditional logs and end-point detection toolsets only goes so far. MixMode steps in to fill the gap. Most cybersecurity solutions that claim to be AI are manual, rules-based tech that requires human intervention before AI initiates. MixMode brings security teams all over the world these unique capabilities:

  • Creates and monitors an evolving baseline of your normal network behavior in only seven days.
  • Traces the path of network attacks and reduces dwell time by sending actionable alerts in real time.
  • Provides modern solutions that are orders of magnitude more effective—and less infrastructure intensive—than all other AI security platforms.

Learn more about MixMode’s self-supervised AI in our whitepaper, “How Predictive AI is Disrupting the Cybersecurity Industry.”

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*** This is a Security Bloggers Network syndicated blog from MixMode authored by Ana Mezic. Read the original post at: https://mixmode.ai/blog/what-is-predictive-ai-and-how-is-it-being-used-in-cybersecurity/