DarkTrace > Case Studies > AI-Driven Cybersecurity Transformation at Confiar Financial Cooperative

AI-Driven Cybersecurity Transformation at Confiar Financial Cooperative

Technology Category

Cybersecurity & Privacy - Intrusion Detection
Cybersecurity & Privacy - Malware Protection

Applicable Industries

Equipment & Machinery
National Security & Defense

Use Cases

Cybersecurity
Tamper Detection

Services

Cybersecurity Services

The Customer

Confiar

About The Customer

Confiar is a Colombian Financial Cooperative with a network of 54 offices across the country, including in major cities like Bogota and Medellin. The Cooperative, which was founded nearly 50 years ago, is renowned for its leading role in social housing projects. It is committed to safeguarding its clients' sensitive data and has recognized the importance of having a robust security stack to protect against fast-moving and sophisticated cyber threats. Confiar has been proactive in incorporating AI into its security stack to stay ahead of emerging risks and protect its users and data in the dynamic landscape of cyber threats.

The Challenge

Confiar, a Colombian Financial Cooperative, was facing a significant challenge in safeguarding its clients' sensitive data against fast-moving threats like ransomware. Despite having traditional security tools such as firewalls, anti-virus, and email protection, the Cooperative needed a solution that could understand user behavior and protect against novel and sophisticated threats that could evade these traditional solutions. The Cooperative was also aware of the increasing prevalence of social engineering, hidden malware, and unauthorized data transfers, and recognized the need to incorporate AI into its security stack to protect its users and data in this new era of dynamic cyber threats. During the initial trial of Darktrace's Self-Learning AI, several vulnerabilities across employee devices were identified that the organization wasn't previously aware of.

The Solution

Confiar adopted Darktrace's Self-Learning AI to defend against novel and sophisticated threats. This AI solution analyzes all raw traffic across an organization, including email, cloud, and industrial environments, and develops a sense of 'self' for every user and device. It spots subtle deviations indicative of a cyber-threat, enabling the detection of malware attacks launched with tactics that have never been seen before. The AI constantly learns the patterns of an organization's users and devices, and is capable of discerning the subtle indicators of a genuine threat. Confiar also utilized Darktrace's Cyber AI Analyst, which automatically investigates sophisticated threats and provides critical details to the security team in seconds. Additionally, Confiar benefited from Darktrace's Proactive Threat Notification (PTN) service, which provides an additional layer of protection by sending alerts when incidents indicative of a serious emerging attack are identified.

Operational Impact

The adoption of Darktrace's Self-Learning AI and Cyber AI Analyst has significantly enhanced Confiar's cybersecurity posture. The AI solution's ability to learn the patterns of the organization's users and devices and spot subtle deviations indicative of a cyber-threat has enabled Confiar to stay ahead of emerging risks. The Cyber AI Analyst has augmented the security team by automatically investigating sophisticated threats and providing critical details in seconds. Furthermore, the Proactive Threat Notification service has provided an additional layer of protection, especially during the transition to a hybrid workforce, by sending alerts when serious emerging attacks are identified. This combination of AI and expert support has minimized the guesswork in uncertain times and ensured a robust defense against cyber threats.

Quantitative Benefit

Darktrace's Cyber AI Analyst investigates incidents up to nine times faster than a human.
During the initial trial, Darktrace's Self-Learning AI identified several vulnerabilities across employee devices that the organization wasn't previously aware of.