Zero-day attacks like the Solarwinds attack on US federal agencies can be devastating to an organisation. Within minutes, an entire network can become compromised after hackers have been inside the network for months or years, completely undetected.
Modern cybercriminals have devised countless ways to break into platforms and programs. To fend off data breaches, malware, ransom attacks and other cybercrimes, organisations will need more robust AI solutions.
In such a scenario, organisations could be better off investing in Artificial intelligence (AI)-enhanced cybersecurity solutions, especially Predictive AI, which has been a part of cybersecurity solutions to varying degrees. This technology can empower organisations to put an end to threats before they happen.
Predictive AI, also known as the third wave, unsupervised-learning AI, detects and surfaces threats in real-time. It is a type of machine learning that automatically collects, analyses and tests data and can power modern, responsive cybersecurity platforms, outperforming previous-generation solutions.
Put simply, Predictive AI gives time to an organisation to raise the alarm, and get ready, enabling organisations to outmaneuver hackers.
Last year, quarantines, lockdowns, and self-imposed isolation pushed the workforce around the world to work from home leading to millions of new, remote connections, all at once. While security analysts working for organisations relying on Security Information and Event Management (SIEM) faced a massive number of false positives, cybercriminals swooped right in, unleashing phishing schemes.
But organisations that had invested in third wave AI solutions experienced far fewer issues, as this technology creates an evolving baseline of normal network behaviour.
Gradually, organisations are coming to realise that the first and second wave cybersecurity solutions that work with traditional SIEM are not enough, especially since it flags tons of false positives.
Also Read: How AI is Improving Predictive Analytics
Consider some alarming stats around the issue of false positive hunting:
- Nearly half of SecOps teams encounter false positive rates of 50 per cent or higher from their security platforms. (2019 CRISTICALSTART Impact of Survey Alert Overload Study)
- As much as 25 per cent of a security analyst’s time is spent chasing false positives — every hour an analyst spends on the job includes 15 minutes wasted on fruitless threat hunting. (2019 Ponemon Institute Research: Improving the Effectiveness of the SOC)
- Eight in 10 SecOps teams experienced high turnover in 2019 — two in 10 reported more than 40 per cent analyst churn. (2019 Ponemon)
- Enterprises spend $1.3 million and waste 21,000 hours annually dealing with false positives (2019 Ponemon)
- 38 per cent of SOCs report being understaffed. (The Exabeam 2019 State of the SOC Report)
Until now, cybersecurity has mainly been a fight after the damage is done. But threat detection and coordinated response must happen as it occurs, which is near impossible without automation and machine learning. For example, the MixMode cybersecurity platform detects threats in real-time with a patented AI engine. It is API-driven and can make the data sent to a SIEM, SOAR or other platform more precise and more accurate. As the platform integrates into an organisation’s network, it begins to learn what a normal baseline should look like and what anomalous behaviour looks like, and continues to optimise, and update the baseline without human intervention.
Also Read: Datatechvibe Explains: Model Drift
As cybersecurity evolves, solutions to sophisticated cybercrime must become even more powerful. Predictive AI security monitoring detects and surfaces threats in real-time before cybercriminals compromise the network. Without human intervention, it can fix the problems. It identifies and surface new deviations from the baseline, quickly find threats and alert security personnel.
Also now, with predictive analytics, organisations can use hacker bots to catch potential threats and holes faster than human efforts.The bots detect bugs faster than any human, protecting their own systems while systematically fleshing out other bots’ weak points and attacking. Just as IBM’s mobile analyser detects weaknesses within applications, the hacker bots use complex analytics and big data to sniff out vulnerabilities before an attack.
Predictive analytics and hacker bots work using self-learning analytics and detection techniques to monitor network activity and report real-time data.
This helps security analysts to apply their talents in more productive and more profitable ways, SecOps teams can focus on tasks that improve organisational effectiveness, free from the burden of excessive threat analysis.
Although predictive analytics and hacker bots still have a long way to go before being integrated into an organisation’s security system, the technology has immense potential and is becoming better every day.
Like all new technology, predictive AI needs massive system requirements, such as a large variety and volume of data, while cybersecurity companies are partnering with analytic firms to work out the kinks with predictive analytics, such as scaling them to meet storage and computation.
Using predictive AI will enable cybersecurity to shed its current cumbersome blacklist strategy and detect impending threats with confidence. Cyber threats of the future call for cybersecurity of the future, and we are placing our bets on predictive AI.
