What is Predictive AI & How is It Used in Cybersecurity?
Date: 27 July 2022
The rise and evolution of technology has had many indisputable advantages for individuals and businesses. However, it has also had one serious drawback - the rise in cybercrime, cyber attacks and malware infection - facilitated by the ever-growing attack surface.
The increased network perimeter poses a major problem, particularly for high-level business operations which require constant monitoring of thousands of layers of codes and security events daily to prevent breaches. This job is beyond human capacity and thus requires a more efficient solution.
Fortunately, the advances in technology have also given rise to the advent of predictive artificial intelligence (or AI) which has been making massive strides in eliminating ever-growing cybersecurity problems.
In this article, you’ll get a quick insight into what predictive artificial intelligence is and its uses in securing your data.
What is Artificial Intelligence?
Artificial intelligence refers to a broad range of technologies that can simulate the human intelligence process through computer systems and acquired information.
As AI possesses advanced levels of human intelligence, it can generate new knowledge based on mechanisms that allow it to acquire, store, process, and apply previous knowledge.
To give you more context, the section below briefly captures what you need to know about AI models. Examples of AI models include neural networks, machine learning, expert systems, and deep learning.
- Neural networks are programming models that enable AI software to learn from observed and gathered data.
- Machine learning models use statistical processes that allow the software to learn rather than be programmed for a task.
- Expert systems grant software problem-solving capacities in specific areas.
- Deep learning models are the broadest, enabling software to learn based on data instead of pre-programmed algorithms.
Artificial intelligence has three explicitly built evolutions called waves for cybersecurity.
The first wave was the most primitive, as it was when programmers first wrote the codes for still-supervised AI to follow.
The AI from this wave collected data and created historical baselines that detect anomalies from different data. This makes it significantly slower than its present counterparts since it takes months to create baselines.
There were also many inaccuracies since it only measures against one fail-safe, its created baselines.
The second wave in the AI evolution made predictions possible. This wave includes unsupervised and supervised machines that can create their own rules through statistical methods, which enables them to make predictions.
Second-wave AI is more advanced than the first wave but can’t detect anomalies without network access or under network changes.
The third wave of AI evolution, also known as Predictive Artificial Intelligence, generates the most advanced types of cybersecurity solutions.
These self-supervised AI systems can apply their analysis even in rapidly-changing situations. Their self-learning capabilities also enable them to create new conclusions while learning from new observations all on their own.
Uses of Predictive AI in Cybersecurity
Now that you’re more acquainted with predictive artificial intelligence, let’s look at its uses in cybersecurity.
Automating Threat Monitoring
To effectively monitor threats to your network, you’ll have to sort through a lot of unstructured and structured data.
This will take a long time on your own, and even if you employ numerous people to check, you can’t be entirely sure that you haven’t overlooked any significant pieces of data. Fortunately, you can automate the threat monitoring process using predictive AI.
This makes the method free from human errors and even more cost-effective in the long run than having employees check your data manually.
Additionally, predictive AI enables you to perform your company's model and credit risk management to reduce the likelihood of cyber-attacks and ransomware attacks, or at least limit the damage and losses they cause significantly.
Improving Risk Management & Incident Response Decision-making
Predictive artificial intelligence can scan through high volumes of gathered data to give you rational decisions when facing an impending threat.
This improved predictive analysis method allows for more data-anchored choices and valuable cybersecurity insights that you can use when making strategic decisions or choosing your Incident Response steps.
You can also integrate these advanced cybersecurity policies when handling third-party services to prevent data breaches.
For example, you can use AI to manage your contracts for more consistent and up-to-date techniques that use improved tools and secure methods.
Sensing and Predicting Cyber Risks
The main benefit of using Predictive AI for your cybersecurity needs is its significantly advanced risk sensing and prediction processes since it accomplishes the task more efficiently and fluidly than human manual checking and restrictive rule-based algorithms.
This highly-efficient process based on predictive AI’s self-learning capacities enables companies to find new anomalies, assess risks from these new anomalies, and create future risk predictions.
For example, your predictive AI’s risk sensing capabilities can detect issues with your social media accounts' newly-integrated customer service management.
It can also give you predictions, such as how often anomalies like this would happen if no solution is applied.
Preventing Cyber Crimes
According to IBM’s 2021 report, an average data breach can cost a company around $4.24 million. Some reasons for these breaches include compromised credentials, business email compromise (BEC), phishing, social engineering, and malicious insiders.
You can easily maintain 24/7 all-around protection that simply can’t be matched with human cybersecurity interventions, using predictive AI.
With its predictive capabilities, third-wave AI can notify you of zero-day vulnerabilities from your company's software before a breach happens. For context, zero-day vulnerabilities are software weaknesses that even manufacturers don’t know about.
Hackers can target these weaknesses to execute what’s known as zero-day attacks using zero-day exploits that can gain access to your system and compromise it.
Stepping Up Cyber Security Operations with Predictive AI
The advancements in technology gave us unprecedented problems concerning information security, but they also gave us the means to combat them.
Predictive, strong AI is an effective way to give your company and its critical infrastructure some much-needed protection against cyber-attacks.
AI technologies can give you the peace of mind that no manual security process can provide, given the fast-scaling cybersecurity requirements of the modern business.
Through predictive artificial intelligence, you can root out problems before they damage your operations and your reputation.
It can also give you better courses of action and help you create a much stronger ransomware response policy or incident response plan when dealing with anomalies.
However, you should still try looking at a layered security approach that doesn’t discount human faculties. Bringing together highly-talented cyber professionals and coupling their capabilities with intelligent machines is the only way forward when it comes to beating the advanced criminal.
Businesses will always require human intervention and supervision. However, predictive analysis and AI can bolster what humans can achieve and help create a stronger cyber infrastructure globally.