Government cybersecurity teams face an overwhelming challenge of perpetually having too many priorities but too few resources to address them all. Instead of focusing on strategic threat mitigation, cybersecurity teams are spending their time deconflicting alerts, chasing false positives, and struggling with visibility gaps. This can lead to higher costs, inefficiencies, alert fatigue, and a dangerous lack of visibility into potential risks.
Artificial intelligence has the power to help government cybersecurity teams overcome these challenges. AI can make cybersecurity processes more efficient across the entire agency, from providing remediation recommendations to automating compliance.
A great example of the benefits of AI for cybersecurity operations is user behavioral analytics (UBA), where the technology can help evaluate user traffic patterns to create a baseline of known behaviors and flag unexpected or suspicious behavior that may indicate compromise for the security team to investigate. In the area of identity and access management, automated entitlement reviews ensure users have the appropriate level of access based on their role, while AI-driven role mining strengthens security principles such as least privilege and separation of duties.
Government cybersecurity teams must lean on AI to stay ahead of sophisticated adversaries and the ever-expanding attack surface. To successfully integrate AI into their workflow, these teams must understand how to best use the technology before, during, and after an incident.
Pre-Incident: Predicting and Preventing Attacks
Government cybersecurity teams can leverage AI before an incident occurs to help accomplish one of their biggest goals — becoming more predictive. While agencies have access to a lot of these tools now, AI can augment existing capabilities by providing the ideal level of unified visibility across the enterprise.
AI-enabled risk analysis should be used to identify which systems are potentially most vulnerable and where sensitive data is located. Automated penetration testing that uses AI and machine learning capabilities can then help teams identify vulnerabilities.
AI can also help cybersecurity teams determine the likelihood of a potential threat by correlating data, including real-world attack data, deep web chatter, and government alerts. AI can then provide teams with real-time risk scoring. Additionally, AI can right size the risk scoring for the organization by automating the recognition of mitigating factors and compensating controls.
Once risks are established, these tools can offer prioritized recommendations and develop comprehensive response plans that consider factors humans often overlook, such as application interoperability and even personnel familiarity with tools and processes. This allows the AI to make prioritized recommendations for remediation while minimizing the potential for negative impact to the organization.
Incident Response: Speed and Accuracy with AI
When an incident does occur, AI should be used to support overwhelmed cybersecurity teams by creating more meaningful and accurate alerts. Once the alert goes out, automating actions like incident triage and system quarantine as much as possible can help decrease the mean time to resolution. This can occur before or after human review, depending on agencies’ operational requirements.
Cybersecurity teams can then leverage AI to tweak response plans based on environmental context and the specific threat. The machine learning solutions used to create these plans should be trained by humans to include simplified steps for faster containment, eradication, and recovery, as well as provide recommendations to lower the risk of re-occurrence.
One of the biggest challenges government cybersecurity teams face during incident response is the high volume of data associated with each event. AI should be used to identify and correlate the most useful events across larger data sets, reducing the time cyber professionals need to start remediation. Generative AI simplifies investigations even further by translating analysis and answering questions in natural language, cross-correlating activity, and generating hypotheses to support informed decision-making.
To maximize AI for incident response, the technology must have access to all the data related to the event. This ensures the tools can successfully correlate threat activity that may not be apparent to the human eye — such as events that took place days apart or on disparate parts of the network. However, this can create a challenge with existing security information and event management (SIEM) tools, which often require teams to cultivate data before ingesting to minimize false positives and reduce the cost associated with higher data volume. Cybersecurity teams should keep this in mind when developing their AI strategies for incident response.
Post-Incident: Learning and Adapting With AI
Once an attack has been addressed, AI’s role doesn’t end. Post-event investigations are critical in understanding what happened during an attack and training the AI to better detect threats and prepare for the future.
AI should be used to generate an after-action report during the triage and remediation process to help inform agency leadership on next steps, including how to notify the public of the incident if needed, and better understand the cause of the event. Automated reports also help capture a more accurate representation of the event and save analysts’ time, allowing them to focus on more important tasks.
To preserve forensic evidence for potential legal investigations and avoid human error, cybersecurity teams should automate tasks such as data recovery and creation of hash calculations on information to show forensic proof of any digital evidence tampering. Cybersecurity teams should also use AI to help law enforcement identify and analyze digital evidence that can help identify the malicious actor(s).
As cyber adversaries become more sophisticated in their attacks, AI is no longer just an advantage — its potential capabilities are a necessity. The future of government cybersecurity relies on AI and human expertise working in tandem to stay ahead of threats and protect mission-critical systems.