cybersecurity (pinkeyes/


AI, machine learning and automation in cybersecurity: The time is now

The cybersecurity skills shortage continues to plague organizations across regions, markets and sectors, and the government sector is no exception. According to (ISC)2, there are only enough cybersecurity pros to fill about 60% of the jobs that are currently open -- which means the workforce will need to grow by roughly 145% to just meet the current global demand.

The Government Accountability Office states that the federal government needs a qualified, well-trained cybersecurity workforce to protect vital IT systems, and one senior cybersecurity official at the Department of Homeland Security has described the talent gap as a national security issue. The scarcity of such workers is one reason why securing federal systems is on GAO’s High Risk list. Given this situation, chief information security officers who are looking for ways to make their existing resources more effective can make great use of automation and artificial intelligence to supplement and enhance their workforce. 

The overall challenge landscape 

Results of our survey, “Making Tough Choices: How CISOs Manage Escalating Threats and Limited Resources” show that CISOs currently devote 36% of their budgets to response and 33% to prevention. However, as security needs change, many CISOs are looking to shift budget away from prevention without reducing its effectiveness. An optimal budget would reduce spend on prevention and increase spending on detection and response to 33% and 40% of the security budget, respectively. This shift would give security teams the speed and flexibility they need to react quickly in the face of a threat from cybercriminals who are outpacing agencies’ defensive capabilities. When breaches are inevitable, it is important to stop as many as possible at the point of intrusion, but it is even more important to detect and respond to them before they can do serious damage.

One challenge to matching the speed of today’s cyberattacks is that CISOs have limited personnel and budget resources. To overcome these obstacles and attain the detection and response speeds necessary for effective cybersecurity, CISOs must take advantage of AI, machine learning and automation. These technologies will help close gaps by correlating threat intelligence and coordinating responses at machine speed. Government agencies will be able to develop a self-defending security system capable of analyzing large volumes of data, detecting threats, reconfiguring devices and responding to threats without human intervention.

The unique challenges

Federal agencies deal with a number of challenges unique to the public sector, including the age and complexity of IT systems as well as the challenges of the government budget cycle. IT teams for government agencies aren’t just protecting intellectual property or credit card numbers; they are also tasked with protecting citizens’ sensitive data and national security secrets.

Charged with this duty but constrained by limited resources, IT leaders must weigh the risks of cyber threats against the daily demands of keeping networks up and running. This balancing act becomes more difficult as agencies migrate to the cloud, adopt internet-of-things devices and transition to software-defined networks that have no perimeter. These changes mean government networks are expanding their attack surface with no additional  -- or even fewer—defensive resources. It’s part of the reason why the Verizon Data Breach Investigations Report found that government agencies were subjected to more security incidents and more breaches than any other sector last year.

To change that dynamic, the typical government set-up of siloed systems must be replaced with a unified platform that can provide wider and more granular network visibility and more rapid and automated response.  

How AI and automation can help 

The keys to making a unified platform work are AI and automation technologies. Because organizations cannot keep pace with the growing volume of threats by manual detection and response, they need to leverage AI/ML and automation to fill these gaps. AI-driven solutions can learn what normal behavior looks like in order to detect anomalous behavior. For instance, many employees typically access a specific kind of data or only log on at certain times. If an employee’s account starts to show activity outside of these normal parameters, an AI/ML-based solution can detect these anomalies and can inspect or quarantine the affected device or user account until it is determined to be safe or mitigating action can be taken.

If the device is infected with malware or is otherwise acting maliciously, that AI-based tool can also issue automated responses. Making these tactical tasks the responsibility of AI-driven solutions frees security teams to work on more strategic problems, develop threat intelligence or focus on more difficult tasks such as detecting unknown threats.

IT teams at government agencies that want to implement AI and automation must be sure the solution they choose can scale and operate at machine speeds to keep up with the growing complexity and speed of the threat. In selecting a solution, IT managers must take time to ensure solutions have been developed using AI best practices and training techniques and that they are powered by best-in-class threat intelligence, security research and analytics technology. Data should be collected from a variety of nodes -- both globally and within the local IT environment -- to glean the most accurate and actionable information for supporting a security strategy.

Time is of the essence

Government agencies are experiencing more cyberattacks than ever before, at a time when the nation is facing a 40% cybersecurity skills talent shortage. Time is of the essence in defending a network, but time is what under-resourced and over-tasked government IT teams typically lack. As attacks come more rapidly and adapt to the evolving IT environment and new vulnerabilities, AI/ML and automation are rapidly becoming necessities. Solutions built from the ground up with these technologies will help government CISOs counter and potentially get ahead of today’s sophisticated attacks.

About the Author

Jim Richberg’s role as a Field Chief Information Security Office (CISO) at Fortinet leverages his 35 years’ experience leading and driving innovation in cybersecurity, threat intelligence, and cyber strategy. He currently focuses on measuring cybersecurity performance (ROI) and cyber risk management within government and companies, on improving election security, and on helping public and private sector organizations maximize their IT efficiency and security post-COVID-19 in the face of increasing operational complexity and budgetary pressure to “do more with less”.

Prior to joining Fortinet, Mr. Richberg served as the National Intelligence Manager for Cyber, the senior Federal Executive focused on cyber intelligence within the $80B+/100,000 employee US Intelligence Community (IC). He led creation and implementation of cyber strategy for the 17 departments and agencies of the IC, set integrated priorities on cyber threat, and served as Senior Advisor to the Director of National Intelligence (DNI) on cyber issues. He brings a broad enterprise-level approach to cybersecurity honed as a member of the Executive team which created and oversaw implementation of the multi-billion dollar whole-of-government Comprehensive National Cybersecurity Initiative (CNCI) that generated new Government cyber capability and enhanced cybersecurity in the private sector and critical infrastructure.

Mr. Richberg’s broad operational, analytic and leadership experience –including his 20 years at CIA-- gives him practical insight into difficult cyber problems ranging from advanced threat capabilities to supply chain integrity and insider threat. He has extensive experience engaging with audiences ranging from Heads of State and CEO’s to analysts and IT staff. He brings a strong focus on strategic problem solving (identify and solve the key problem vs. the most visible one) and on framing complex problems in comprehensible terms that facilitate analysis and formulation of solutions.


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