As cyber threats rapidly evolve in sophistication, organizations require a fundamentally more advanced approach to identify and eradicate security vulnerabilities before they‘re exploited. Automation holds the key to dramatically enhancing the speed, scale, consistency, accuracy, and efficiency of activities like threat hunting, attack surface management, and risk analysis.
This comprehensive guide explores the transformative value automation brings to security risk assessment by:
- Examining the limitations of manual processes
- Providing an overview of automated technologies
- Detailing implementation best practices
- Reviewing real-world examples and results
- Discussing current challenges
- Delivering data-backed recommendations
Equipped with these evidence-based insights, your organization can make informed decisions when adopting automated assessment capabilities for robust and resilient cyber defenses.
The Clear Need to Graduate Beyond Manual Security Risk Assessment
Most organizations still rely on traditional manual techniques for security risk assessment involving staff poring over inventories, diagrams, and system manuals seeking vulnerabilities. This hands-on approach inevitably leads to several acute limitations including:
- Extreme time and effort intensity keeping personnel from other tasks
- Inconsistency across assessments and skill levels
- Frequent oversight of critical vulnerabilities that get exploited
- Inability to scale as assets, users, and threats expand
A 2022 Enterprise Strategy Group study discovered that only one-third of respondents conduct risk assessments more than quarterly, while over 50% assess risks just annually or less due to the workload involved. This leaves unacceptable visibility gaps into whether safeguards are adequate.
The same report revealed only 34% of organizations feel their manual assessments are extremely or very effective. Clearly, traditional techniques fail to meet the demands of today‘s rapidly evolving cyber risk landscape. Reliance on manual processes creates potential for catastrophic security gaps.
The Automated Assessment Revolution
Transitioning from manual to automated security risk assessment methods helps institutions overcome these challenges through:
- Speed – Automated tools assess environments orders of magnitude faster than humans. A SafeBreach platform slashed assessment time from 102 days to just 14.
- Scale – Automation seamlessly handles expanding users, endpoints, and assets.
- Consistency – Tools provide reliable, repeatable identification of risks unlike humans.
- Accuracy – Algorithmic analysis boosts detection rates from under 60% manually to over 85%.
This allows organizations to find more impactful savings by replacing multiple staff members with unified automation capabilities. One financial services firm achieved over $650,000 in annual expense reductions after retiring manual processes.
Now that leading analysts predict global spending on security risk assessment tools will grow from $9.3 billion in 2022 to $14.4 billion by 2026, it‘s clear automation will define the future.
An Overview of Popular Automated Security Risk Tools
Specialized platforms automate various facets of assessment from network analysis to attack simulation. Capabilities include:
Network Mapping
Network mapping tools like SolarWinds Network Topology Mapper graph out all devices, connections, and data flows across hybrid environments. This furnishes visibility to thoroughly inspect attack surfaces.
Vulnerability Scanning
Vulnerability scanners like Tenable.io continuously probe networks, web apps, cloud services, and endpoints seeking software flaws like missing patches or risky misconfigurations cyber criminals leverage as initial access avenues.
Open Source Intelligence (OSINT)
OSINT analytics platforms like Recorded Future and ZeroFOX scan through technical sources and social media using natural language processing for signs of emerging external threats, leaked data, or impending attacks focused on the organization.
Security Operations Analytics
SIEM, UEBA, and other SOC platforms housing machine learning algorithms analyze system, network, user, and application behavior to detect subtle anomalies that could reflect insider threats or initial breach activity for rapid response.
Cloud Infrastructure Entitlements Manager (CIEM)
CIEM offerings like Ermetic inspect cloud platform roles, permissions, and privileges to determine if users or services have unnecessary excessive access that could enable data exfiltration after compromise.
Attack Simulation
Breach and attack simulation (BAS) tools mimic tactics real-world attackers employ to penetrate environments. Executing safe but realistic attack scenarios with platforms like SafeBreach or Cymulate reveals if current controls will detect, delay, or fully prevent sophisticated threats.
Orchestration Frameworks
Orchestration systems like Swimlane provide a hub to connect and correlate insights from assessment tools to eliminate blindspots from siloed visibility. Case management streamlines tracking issues through remediation.
| Capability | Business Value |
|---|---|
| Network Mapping | Inventory devices to reveal unseen attack paths |
| Vulnerability Scanning | Continuously check configurations and software flaws |
| Open Source Intelligence | Gather external threat intelligence for early warnings |
| Security Operations Analytics | Detect subtle signs of compromise from user anomalies |
| Cloud Infrastructure Entitlements Management | Find excessive or unused permissions |
| Attack Simulation | Test controls against advanced threat models |
| Orchestration Frameworks | Connect insights across tools for investigation |
Best Practices for Successful Automated Capability Implementation
The first step towards harnessing automated assessment advantages involves properly onboarding and operationalizing tools. Critical success factors include:
Select Solutions Matching Cybersecurity Staff Expertise
More advanced automation platforms like breach simulators or predictive risk analytics require specialized skills for customization and output analysis. When talent is scarce, organizations should explore expert-supported or fully-managed solutions suitable for current team abilities.
Integrate Tools With Existing Infrastructure
For comprehensive visibility, platforms must interface with adjacent systems like identity providers, endpoint managers, firewalls, and cloud access brokers. Modern APIs simplify integration so insights enrich rather than displace controls.
Define Methodology, Metrics, and Compliance Mandates
Creating playbooks detailing what risks require automated or manual verification, usage and response protocols, effectiveness measurement key performance indicators (KPIs), and regulatory compliance mandates helps sustain consistency as personnel and environments evolve.
Establish Dedicated Automation Management Function
Make certain to appoint security team members to continually oversee tool efficiency, audit logs, upgrade processes, incident escalation workflows and executive and regulator reporting cadences to ensure maximum, continuous value.
Iteratively Scale and Refine Programs
Once initial tools tackle the risk areas of highest potential impact, progressively expand assessments across wider hybrid attack surfaces while tuning policies and algorithms to eliminate excessive false positives and noise for actionable threat intelligence.
Real-World Results Showing the Efficacy of Automated Assessments
Industry examples empirically exhibit how employing specialized platforms for algorithmic risk analysis consistently identifies more dangers faster than manual approaches.
According to a 2021 Ponemon Institute study, average assessment completion time plummeted from 102 days to just 14 days using the SafeBreach automated platform – an 87% improvement. SafeBreach also uncovered 26% more previously undetected critical risk areas compared to manual testing.
Research by the City University of Hong Kong demonstrated machine learning systems could predict 85% of software vulnerabilities contrasted with less than 60% found by human code audits. The algorithms also produced meaningfully fewer false positives.
These findings powerfully illustrate that automated techniques systematically outpace manual risk assessment across essential metrics like velocity, accuracy, and cost. Adoption continues accelerating as more leaders witness empirical results firsthand.
The Path to Risk Quantification
Looking ahead, by combining automated vulnerability data with mathematical models, organizations can even forecast potential annual financial losses from cyber incidents based on threat actors, gaps, and asset values.
This emerging automated discipline of risk quantification leverages algorithms originating from the financial sector around exposures, probabilities, correlations, and non-linear sensitivities.
Quantitative techniques like FAIR and Factor Analysis of Information Risk (FAIR) transform subjective qualitative assessments into objective potential loss forecasts for better informed business decision making and cyber insurance needs.
Remaining Obstacles and Considerations with Automated Security Risk Assessment
Despite immense progress, organizations must still thoughtfully navigate some present challenges when scaling automated risk assessment including:
Detection Difficulties with Unknown Threats
Tools rely on databases of historical attack patterns, malware signatures, software flaws and misconfiguration templates to guide analytics. The continuous introduction of exotic cybercriminal techniques can temporarily impact identification rates until reference repositories update.
Augmenting signature-based scanning with behavioral analytics and anomaly detection provides one mitigation to this issue.
Friction with Application Development Teams
High false positive rates from some methods frustrate coders as application bugs automatically prioritized for remediation aren’t actual vulnerabilities. Misconfigured tools could hamper software release velocity.
Collaborative alignment of risk tolerance thresholds and exceptions between security and development staff alleviates much of this friction. Code scanning integrated within CI/CD pipelines also improves developer experience.
Inability to Judge Business Impacts
While excellent at technically analyzing risks, algorithms still struggle to fully determine how a given gap might tangibly impact business operations, revenue, or compliance standing without human insight.
Supplementing quantitative scanning with expert human intelligence provides essential context for prioritizing the most impactful risks for remediation and the most critical assets for added safeguards.
Despite these factors, automated platforms grow more precise and predictive with ongoing machine learning refinements to address current limitations.
Conclusions and Recommendations for Success
Here are research-backed best practices for organizations pursuing automated security risk assessment:
- Catalog your most vital data, systems and services for tools to focus on first
- Pursue managed solutions with specialized support if cyber skills are scarce
- Integrate tools into the security ecosystem via APIs to avoid blindspots
- Develop protocols for triaging findings by severity and asset criticality
- Start with a focused pilot program before scaling and optimizing
By combining the right automated assessment capabilities guided by prudent adoption strategies, organizations can elevate visibility, agility, and resilience against increasingly cunning attackers. The future of effective enterprise security hinges on embracing automation to your advantage before risk surfaces expand beyond control.
