Federal agencies face many of the same cybersecurity challenges as private-sector organizations.
These include a barrage of attacks such as malware, phishing emails used to spread malware or steal credentials, and distributed denial of service attacks that can be generated by malware-infected systems elsewhere.
Agencies also must deal with issues that businesses typically do not encounter, however, and that requires a new approach to protecting data assets.
For example, many agencies need to protect themselves against nation-state actors that are specifically targeting them using malware built solely for that purpose. Nation states continue to recruit highly skilled people whose main focus is to carry out these attacks.
In addition, a number of agencies utilize an air gap strategy, in which many of their networks are physically isolated from unsecured networks such as the Internet.
That means the systems on these networks do not have easy access to the malware signatures pushed out by antivirus vendors after they identify and analyze new malware.
To address these challenges and better protect data resources, federal agencies must transition from traditional security solutions utilizing signature-based technologies, to those that use newer machine learning based capabilities designed to stop the latest attacks.
More and more businesses and local, state, and federal government agencies are beginning to learn and see the value of predictive cybersecurity that exceeds traditional benchmarks.
This paper examines how by employing new methods and technologies, federal agencies can provide better security to both their own employees and the public they serve.