Cybersecurity

Optimize Your Security Operations Center

DataRobot’s artificial intelligence (AI) platform can help Security Operations Center (SOC) leaders through shortened decision cycles, anomaly detection, and automated responses.

SOC 1.0 was about controlling the data flowing in and out of the business by bounding networks and sending alerts on traffic coming in and out of the network. 

SOC 2.0 incorporated lateral traffic detection and sensor reporting from servers and endpoints across the corporate network. Leaders now have visibility over the network and stand to optimize detection and response. 

SOC 3.0 is the deployment of AI/ML into existing security processes to detect disparate threats from insiders and advanced persistent threats (APTs), as well as providing the augmented intelligence needed to decrease response times and empower your analysts to focus on the most difficult security incidents.

DataRobot automates the entire end-to-end process of preparing, building, and deploying accurate predictive models that are available to integrate with your cloud platform-of-choice, onsite system, or fully-managed service. Our Automated Machine Learning platform will help your security teams create trusted solutions that can predict and auto-route alert responses, shortening analyst decision cycle per alert through consequence-based alert prioritization.

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Detect Advanced Persistent Threats

The Problem

In December 2020, the U.S. learned that an Advanced Persistent Threat (APT) cyber actor had gained and maintained access to over 18,000 networks inside the U.S. government, private companies, and non-profit organizations since at least March 2020. This attack was a complex supply chain attack involving cyber activity,  human actors, and malicious code embedded in the design process. Advanced actors are using every available method to gain access to networks. Solutions that focus exclusively on cybersecurity will never illuminate and isolate these threats. The DataRobot AI Cloud sits above typical cybersecurity solutions, able to harvest financial data, security data, and more to identify these multi-pronged attacks by advanced persistent threat actors.

Efficient Time Management

Cut down on the hours analysts spend on reading intelligence to increase hours acting on intelligence.

Detect the Undetected

Avoid signature-based detection methods, utilizing AI to detect never before seen malware.

Vulnerability Management

The Problem

For many organizations, it is unrealistic to expect every vulnerability will be resolved in near-real-time. Instead, they must choose to patch those vulnerabilities that pose the greatest risk to the health of their missions and the information networks that enable them.

While the National Vulnerability Database (NVD) assigns a Critical Vulnerability Security Score (CVSS) to each vulnerability, this is not enough context to inform which vulnerability management efforts will have the greatest impact on the security of specific organizations and networks. The CVSS captures the impact of a vulnerability if it were to be exploited, but it does not account for actual weaponization. There are over 120k published Common Vulnerabilities and Exploits (CVE) in the NVD. However, studies indicate only 20-22% of known CVEs have public exploits–meaning, the vast majority of CVEs cannot be weaponized by the vast majority of actors.

Prioritize Predictions

Current patching is typically a minimum of 30 days behind release dates for most cybersecurity solutions. DataRobot will predict what patches are more important to your enterprise for faster and safer vulnerability management.

Streamline Processes

In 2019, the average medium to large company spent around 413 weekly hours on vulnerability management processes. This is enough work for ten people each week! Initial DataRobot augmented VM shows a minimum of 25% decrease in hours worked.

Business Email Compromise

The Problem

The number one intrusion method by adversaries is still phishing. Prior techniques involved sending malicious links and malware-filed attachments to launch an infection into the host system. The market leaders in email security can largely stop these malicious techniques. 

But what happens when your adversary doesn’t send any malicious links or attachments? How do you know they are your adversary? How can you define intent?

DataRobot uses its native Natural Language Processing (NLP) ability to classify emails and predict whether your employees are seeing fraudulent requests or influence operations.

Effective Detection

DataRobot can find correlations across multiple disparate datasets and link the chain to determine whether seemingly benign emails have malicious intent.

Proven AI

DataRobot’s unique ability to look across the enterprise is unparalleled in cybersecurity.

Decrease Expenditures

Cybersecurity leaders don’t need to purchase another platform. They can repurpose the DataRobot platform to get more bang for the buck.