The enterprise attack surface is massive; it expands and evolves continually. The larger the organisation, the more time-varying signals need to be analysed to calculate risk. This process is so layered in complexity that analysing and fortifying cybersecurity is no longer a human-scale problem.
In response to this growing challenge, Artificial Intelligence (AI)-based tools for cybersecurity are available to assist information security teams in reducing risk and improving security posture.
- What Is Artificial Intelligence (AI)?
- What Is Machine Learning (ML)?
- AI vs Machine Learning: A Comparison
- 7 Ways AI and Machine Learning Improves Cybersecurity
- Thoughts For a Secure Future
- 4 Tips for Embracing the Future of Cybersecurity
- Further Technology Articles
AI and Machine Learning (ML) are critical technologies for information security because they can analyse millions of events and identify threats in real-time, including malware and zero-day vulnerability exploits.
Such systems can quickly and efficiently identify risky behaviour that likely leads to a phishing attack or malicious code download. These actions are learned over time, pulling information from past events to identify new attacks.
Histories of past behaviours become user, asset, and network ‘profiles’, and AI uses them to identify and respond to behaviours that deviate from established norms.
What Is Artificial Intelligence (AI)?
Artificial intelligence is a capability that enables a computer to mimic human cognitive functions like learning and problem-solving. Using AI, a system uses math and logic to simulate human reasoning.
Like humans, in this sense, AI allows the computer to learn from new information and make decisions. These systems can understand and act based on derived information far quicker than any human.
To break it down, AI works in three fundamental ways.
- Assisted intelligence improves the technologies that organisations are already using. It’s the most widely used form of AI.
- Augmented intelligence is an emergent technology that enables organisations to do things they couldn’t do without it.
- Autonomous intelligence is in development for future use. It features machines that act on their own. You’ve likely heard of self-driving vehicles. They are a bit of a novelty now, though the technology behind them will ultimately become mainstream.
What Is Machine Learning (ML)?
Machine learning is a subset application of AI. It involves processes that use mathematical data models to help the system learn without direct instruction. Machine learning enables a computer system to continue learning and improving on its own, basing its decisions on experience.
Using a neural network is one way to train a computer to mimic human reasoning. This network comprises a series of algorithms modelled after the human brain. The neural network enables the system to achieve AI through deep learning. This connection explains how AI and machine learning work in tandem.
AI vs Machine Learning: A Comparison
As discussed above, AI and machine learning are closely connected. Because of this symbiotic relationship, comparing AI and machine learning is more of an examination of their interconnection.
AI enables an “intelligence” to think like a human and execute tasks independently. Machine learning is how the computer system develops its intelligence; let’s call it ‘evolution’.
7 Ways AI and Machine Learning Improves Cybersecurity
The following are seven ways that AI and machine learning can improve cybersecurity.
1. Human Error Reduction
Human error is a significant cybersecurity weakness. Complex system configurations are difficult to manage, even with the best IT teams handling the setup.
With its constant evolutions and innovation, computer security is challenging and layered in complexity. Responsive tools with integrated machine learning can help teams detect and mitigate issues that appear as they update and modify network systems.
A secondary challenge exists wherever a cloud computing infrastructure is layered on top of an older local framework. IT teams must ensure compatibility to secure the systems.
Traditionally, there are manual processes IT teams complete for evaluating configuration security. Such processes strain resources because they require constant updates and routine support tasks.
With adaptive automation, human IT teams receive timely advice on new issues. They can get advice on possible solutions and may even have systems in place already that automatically adjust settings. In these cases, automation reduces the instance of human error.
2. Increased Cybersecurity Efficiency
Human efficiency is another vulnerability within the cybersecurity field. Manual processes cannot be repeated perfectly, especially when the environment constantly changes.
Furthermore, setting up an organisation’s growing number of endpoint devices is arduous and time-consuming. Even after the initial setup, IT teams often need to revisit these devices to correct misconfigurations that can’t be patched through remote updates.
In addition, when employees must respond to threats such as ransomware, the scope of the danger can quickly shift, making it difficult for IT teams to adapt and respond as soon as needed. In these cases, AI and ML can close that gap and extinguish the change of human error. AI can adapt and respond instantly to unexpected changes.
3. Improved Threat Response
Threat response time is a pivotal indicator of a security team’s efficacy. Malicious attacks move very quickly. The time from when the exploit begins to the time the attack deploys is brief.
In the past, it took cybercriminals gangs a lot longer to wade through network permissions and disarm the system laterally. This often took weeks before they could proceed with the attack.
Unfortunately, the same technological innovations that assist cybersecurity teams also enable criminals to complete their legwork before an attack. This accelerates and shortens the attack time.
On the security side, human response time can lag behind the attack, even when they know when it occurs. Furthermore, teams are often more involved with reacting to attacks rather than preventing attempted ones.
ML-assisted technology pulls data from an attack and immediately groups and prepares it for analysis. It creates simplified reports to make processing and decision-making easier. ML-assisted security also recommends action for limiting damage and preventing future incidents.
4. Advanced Threat Identification
Another factor that impacts response times during a cyberattack is predicting and identifying new threats. As we have already discussed, a lag time exists with existing threats.
Emerging unknown threats pose an additional risk because threat agents use tools and select behaviours to deceive security teams and further slow their response times. Worse, data theft is a quiet attack that often remains undiscovered.
For these reasons, zero-day exploits are a persistent, underlying concern for IT security teams. However, cyber-attacks are rarely built from the ground up. They are most often designed over existing behaviours, source codes, and frameworks from previous attacks. The upside is that machine learning has a pre-existing path from which to work.
ML programming can identify an attack by highlighting commonalities between new threats and previously identified ones. Humans cannot accomplish this in a timely fashion compared to ML, which further highlights the necessity of adaptive security models. ML increases threat awareness, making it far easier for teams to predict new threats and reduce response lag time.
5. Real-Time Possibility Synthesis
Possibility synthesis is the synthesising of brand-new possibilities based on lessons learned from previous, down data and new, unfamiliar datasets.
This differs from recommendations in that it concentrates on the chance that a particular action or system’s state falls under similar past situations. Possibility synthesis is used for a pre-emptive probe of weak points within an organisation’s network systems.
6. Predictive Forecasting
Predictive forecasting is the most advanced ML component process. Forecasting is achieved by evaluating existing datasets to predict potential outcomes.
This feature is used for several things, including outlining fraud prevention, building threat models, and data breach protection. Thus, it is the foundation of most predictive endpoint solutions.
7. User Behaviour Modelling
Behaviour modelling involves creating custom network profiles for staff based on user behaviours and tailoring security to fit your organisation. Such a model can define what an unauthorised user may look like based on the pre-established characteristics of user behaviour.
These characteristics include subtle traits like keyboard strokes to form a predictive threat model. Once an outline of possible outcomes from potential unauthorised user behaviours is established, ML security can recommend recourse actions to reduce exposed attack surfaces.
Thoughts For a Secure Future
AI and ML have their limitations, partly due to legal and human delays. Technology evolves and performs so rapidly that legislation has yet to keep up.
Currently, ML needs datasets to work, though that often conflicts with current data privacy laws. For example, training software systems require many data points to build accurate models.
This doesn’t marry well with privacy laws and “the right to be forgotten.” The human identifiers within specific data cause violations, so potential legislative and other solutions must be considered.
One possible solution is to program systems to make original data nearly impossible to access once the software is trained. Another solution is anonymising data points, which must be examined further to avoid skewing the program’s logic.
Within the field itself, more AI and ML cybersecurity experts are needed who can work with programming within this scope. ML network security needs staff who can maintain and adjust it as needed.
The talent pool of qualified (human) individuals is far smaller than the growing global demand for expert staff. Human teams remain essential, even with the rise of AI and ML technologies. Critical thinking and creativity will always be crucial to decision-making. AI and ML are intended to augment existing human security teams, not replace them.
4 Tips for Embracing the Future of Cybersecurity
As we move forward with artificial intelligence security, there are a few steps security experts should take while adopting AI and ML:
- Remain future-focused. Threat agents find opportunities in outdated technology. Furthermore, organisations incur unnecessary costs when using redundant manual labour.
- Keeping abreast of the latest security tools and methods will help mitigate risk as threats become increasingly complex.
- Supplement your teams with AI and ML rather than replacing them. Vulnerabilities will always exist, and no system is foolproof. Even the most advanced AI and ML-assisted adaptive systems can falter; be sure your IT team learns has expertise with your chosen infrastructure.
- Update your data policies to comply with legislation. Data protection is a focal point for governing bodies worldwide. Be sure to review evolving legislation and modify your security policies accordingly routinely.
Today’s organisations struggle to protect themselves from the ever-growing number of cyber-attacks, and AI and ML are potent weapons that help IT security teams enhance their security posture.
AI and ML enable teams to learn and analyse potential cyber threats in real-time. They employ algorithms to build behaviour models and use these models to predict cyber-attacks as new data comes to light.
When combined, AI and ML technologies enable companies to fortify their cybersecurity by increasing the speed and accuracy of their threat response.
If you would like to discuss your network security requirements in more detail with one of our cyber security professionals, please don’t hesitate to get in touch.