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How can Artificial Intelligence Help Cybersecurity Teams?

For enterprises, security is a constant concern, and most organizations started using threat detection software instead of just relying on the manual work of software engineers and cybersecurity specialists.

High-performance organizations use TDS (thread detection software) and SIEM (security information and event management) tools that are more likely to identify issues long before they spread and cause damage.

How can Artificial Intelligence Help Cybersecurity Teams?: eAskme
How can Artificial Intelligence Help Cybersecurity Teams?: eAskme

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The automated software monitors all activity and highlights suspicious processes. The only practical problem is that these tools sometimes create more alerts than a team can analyze.

This situation's technical term is alert fatigue, when the number of signals is so high that real threats get lost among false positives and minor warnings.

Paradoxically, the solution to fight alert fatigue caused by technology resides in adding more technology layers and replacing manual work with AI.

Some fear to take this decision due to employability concerns, but as we will debate, AI is just a tool to help specialists and give them back some time to engage in more meaningful work, not to take their place.

Cybersecurity alerts and IT automation

Most organizational processes can be analyzed using automated systems.

However, these have a very high rate of false positives compared to real threats.

A study from Illinois University cites another paper stating that 51% of alerts are false positives and only 4% out of an average of 17000 weekly alerts get the proper attention.

At this rate, we could argue that the company would be better off without such a security tool.

The primary cause of this situation is that most automated network monitoring tools rely on single event matching instead of looking at the context.

Simply put, if the program detects one anomaly, it will trigger an alert without asking why that happened and if it is a real threat. Better overwhelmed than sorry.

An excellent example is that of ZIP archiving classified as ransomware due to similar read/write behavior.

The way cybersecurity AI reduces alert noise is by filtering out unimportant alerts by assigning priorities.

A robust system can perform auto-closure of alerts, which are false positives or low-value warnings, auto-association between alerts caused by the same event, and auto-escalation of situations requiring human assistance.

In the latter case, an explanation is attached to the situation, such as an error code.

How can AI show the difference between an actual attack and a false positive?

The key to fighting alert fatigue is through alert triage based upon contextual information and event correlation.

Artificial intelligence needs to replace the detective work performed by security experts in hours or even days with automated responses provided in seconds or minutes.

The way to do this is to perform root cause analysis and go upstream the chain of events that caused an alert and identify ramifications (backward and forward tracking).

Looking at the context makes it easier to distinguish between processes that seem suspicious and a real threat.

If a cybersecurity specialist did this analysis, it would boil down to inspecting a graph with thousands of ramifications for each alert. This task would only add up to the complexity of the problem and solving time.

When the manual work is AI-based, thousands of possibilities are analyzed continuously and instantaneously using big data.

Each event is compared to an existing database of similar occurrences classified by rarity.

The difference from single event solutions is that the entire chain of causality is taken into account when evaluating the impact and the potential of that alert to be an actual damaging event.

When an organization faces a real attack, every minute means money, and the hours spent cleaning the system and restoring it to the initial state can add up to millions.

In this case, the time to identify what caused the problem becomes one of the top KPIs.

Will AI take over cybersecurity jobs?

The short answer is no. It is not supposed to replace human specialists, and it is not created in such a manner.

Instead, it aims to provide helpful assistance by taking some of the repetitive and guesswork off the SOC and NOC team members' shoulders.

In fact, deploying a machine learning software to take care of the cybersecurity alert triage system can have numerous benefits related to productivity, accuracy, and even the employees' well-being by reducing stress and the feeling of being overwhelmed by a constant influx of work.

A study by PaloAlto Networks shows that 28% of all alerts are not addressed at all.

Making the transition from manual alert processing to AI-based monitoring tools will allow SOC or NOC team members to use the newly freed-up time to look deeper into high-value work.

The AI triage alert tools are no more than devices to detect real issues before spreading in the organization.

The AI system creates tickets based on the importance of the issue and forwards them to the cybersecurity team who make decisions.

Most organizations would not even feel comfortable letting the computers make essential decisions, which would not even be possible in heavily regulated industries.

One fundamental shift would be in the support team's mindsets.

Having an AI-based tool to help fight alert fatigue would put them in a proactive mode instead of a reactive one.

Another significant change will be in the workload structure of the security analysts.

Having a lot less alerts to deal with, their time and energy will become a lot more available for proactive tasks.

The work would shift from firefighting to strategy.

Alert triage as a competitive differentiation

Investing in an automated, AI-based triage system is a way to make your organization more competitive.

  • First, you free a lot of time from your top performers in the SOC team, which could be invested in value-adding projects.
  • Secondly, you create a database of potential problems and have better hedging options if an attack happens.
  • Next, you save time and money, which otherwise would be spent fixing any potential problem detected by the system in an early stage.
  • Finally, you avoid potentially irreparable image damage by proactively protecting your organization's data.

If you still have any question, feel free to ask me via comments.

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