- B
- Blockchain
- C
- Customer Churn
- Counterterrorism
- Cybersecurity in the Public Sector
- Credit Card Fraudulent Transactions
- Credit Default Rates
- Conversion Modeling
- Claim Payment Automation Modeling
- Claim Development Modeling
- D
- Drug Delivery Optimization
- Disease Propensity
- Digital Wealth Management
- Direct Marketing
- E
- Estimating Sepsis Risk
- F
- Finding Duplicate Customer Records in Your Database
- Fraud detection
- Finding New Oil and Gas Sources
- Fraudulent Claim Modeling
- G
- Google AdWords Bidding
- H
- Hospital Readmission Risk
- I
- Inventory Forecasting
- Insider Threat in Public Sector
- Insurance Pricing
- L
- Loyalty Program Usage
- Life Insurance Underwriting
- M
- Multichannel Marketing Attribution
- Modeling ICU Occupancy
- N
- Next Best Offer
- Next Best Action
- P
- Product Personalization
- Q
- Quality Assurance
- S
- Supply Chain Management
- View global site search results

Insider Threat in Public Sector
Problem: Insider Threats Are Difficult to Prevent and Detect
Federal and state agencies think of threats as an external force looking for a way to intrude and exploit the organization. In reality, threats can come from ALL sides, including from within the organization. Insider fraud and insider threat cases are more common than previously thought.
From Snowden to Solarwinds, it’s not hard to find insider threat examples in the news. The mistake that agencies often make is believing that their organization is somehow exempt from insider threats. Agencies can proactively block any potential exfiltration of data by deploying machine learning to identify insider threat cases and exfiltration of sensitive information.
The magnitude of data flowing across government systems combined with the number of people and entities that have access to government systems is overwhelming. Ensuring that all personnel have the correct level of access and that no sensitive information is leaving the government network is an enormous task that cannot be done by humans alone.
Solution: Insider Threat Detection Using AI
With DataRobot, federal agencies can train models based on historical personnel roles, permissions, and information policies. These models can then identify employees with unauthorized permissions or access, and utilize natural language processing to scan and filter information prior to leaving agency gateways.
The behind the scenes predictions by DataRobot prevent the unwitting loss of information by well-intentioned employees and help security teams focus their investigations instead of combing through mountains of data to seek out insider activity.
Why DataRobot? Identify the Smoking Guns with the Help of AI
With insider threat cases, an agency has to be absolutely positive that there was an intentional misuse of data with the intent to harm the agency and/or benefit an individual or group by providing the sensitive information to unauthorized parties.
It’s serious business, and you can’t afford any missteps.
With DataRobot, organizations can leverage their enterprise usage policies and individual employee data to develop, model, and deploy algorithms that allow for the detection of security breaches, document theft or misuse, and violations of clearance responsibilities. Prevention, modeling, and pattern recognition can all help identify individuals or groups who may compromise the agency’s security. DataRobot offers the AI insider threat solutions your agency needs.