Cognitive Automation Platform

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Client: AI Technology Company

Technology

Deep Learning

Services

Cognitive automation platform development
Deep Learning model training

Challenges

Today, most of the alerts are managed manually, creating an extra burden on security teams in different organizations. Security teams deal on average with 11,000 alerts daily. Of them, 18% are reviewed manually, 32% are false positive, 28% are ignored, and only 17% are automated. Due to manual processes, a lot of security team members burn out and quit jobs taking their knowledge and experience out of the company. Onboarding new specialists and bringing them up to speed takes time and effort, leaving loopholes in IT security systems unfilled for a while.

The Client was looking to build a solution that would allow cybersecurity teams to manage the flow of alerts in an automated way and triage them to identify the most critical ones that require immediate attention and investigation.

Solution

Our rinf.tech team built a Cognitive Automation Platform that uses deep learning and user feedback to automate security alerts triage and management.

A unique mix of capabilities enables the platform to

analyze alerts,
learn from expert knowledge, and
automate the decision-making process for alert triage.

The solution allows cybersecurity teams to focus more on hunting, investigating, and responding to real threats without the burden of false positives and irrelevant alerts.

Our deep learning model captures the expert knowledge through feedback and encapsulates it, allowing the knowledge of all previous and current cybersecurity specialists to be used in decision-making.

Features & Components

Third-party integrations

Integrations are a vital feature enabling & facilitating process streamlining through data collection, processing, decision-making and post-decision automation. Our custom solution seamlessly integrates with companies' existing ecosystem and a range of out-of-the-box tools & applications.

AI jobs

The bread-and-butter of our custom Cognitive Automation platform. It represents streams of data being processed through the deep learning model for alert triage automation. The model analyzes each alert and decides what to do with it – drop, duplicate or escalate. The knowledge gathered by the model from user feedback is applied to each alert, ensuring all alerts are treated adequately.

Knowledge capture

We've deployed a no-code, intuitive model training through UI usage. Security team members can provide their feedback on the platform's decisions, enabling the model to learn from the collective knowledge & experience of all specialists ever working within the security team.

Model training

As feedback is collected, the model can be retrained to incorporate newly gained knowledge into automated decision-making.

Post-decision automation

Once a decision has been made by the model, it can be integrated with post-decision tools such as incident response platforms, collaboration, messaging systems or automation tools. It helps streamline operational processes with data-driven decision-making.

Results

Improved incident response time

Security analysts can focus on and address real threats rather than reviewing manually thousands of alerts daily.

No-code model training

Our deep learning model learns from security experts' experience and knowledge and can adapt to any organization's peculiarities. This doesn't involve a single line of code.

Reduced mistakes caused by human factor

Our custom solution eliminates stress and minimizes errors caused by manual repetitive tasks through alert triage decision process automation.

Higher retention of cybersecurity specialists

As security team members can deal with real threats and have much fewer routine and manual processes, they feel less stressed out at work, which increases the chance they'll stay with the company for a while.