The endpoint has become the ultimate prize for attackers, as compromising devices provides a stealthy foothold into corporate networks. However, most organizations still rely on traditional antivirus and firewalls that are inadequate for today’s threats.

For SOCs, transitioning to modern endpoint security needs to become an urgent priority. In this guide, we’ll explore key capabilities and strategies to help SOCs champion and enable improved endpoint defenses.

Focus on Prevention Through Zero Trust

Prevention must become the priority over reactive approaches. Zero trust models provide the foundation by isolating devices, limiting lateral movement after breaches, and securing identities.

SOCs should advocate for expanding zero trust controls like microsegmentation, multi-factor authentication, and least privilege access across endpoints. Reducing dwell time blocks threats before they trigger alerts.

Adopt Cloud-Delivered Protections

Legacy on-premises tools lack the scalability, analytical capabilities, and rapid innovation of modern cloud-delivered endpoint security suites. These include Microsoft Defender for Endpoint, CrowdStrike Falcon, and SentinelOne.

Cloud platforms apply AI and automated response at scale to defeat advanced attacks. SOCs can instantly benefit from collective threat intelligence and mitigation measures applied across the cloud vendor’s entire customer base.

Simplify Deployments with Unified Platforms

Consolidating disjointed point solutions into unified cloud platforms streamlines management while expanding protection. Platforms like Microsoft 365 Defender integrate capabilities for identities, endpoints, cloud apps, email, and networks.

SOCs avoid having to integrate and coordinate across multiple consoles. Unified data lakes and automation policy engines bolster AI-driven threat detection, investigation, and response.

Champion Employee Security Awareness

Humans represent a major endpoint vulnerability. SOC teams should encourage security awareness training to be mandated for all employees. Well-executed simulated phishing campaigns consistently applied keep security top of mind.

Evaluate options like ThinkCyber ZeroFox for measurable improvement in phishing resilience. Reduced clicks demonstrate quantifiable gains in human firewall strength.

Prepare Incident Response Playbooks

Despite best efforts, some threats will evade defenses. Ensure detailed response playbooks are ready for critical scenarios like ransomware, data exfiltration, and domain admin compromise that necessitate urgent response.

Tabletop exercises to walk through simulated scenarios help evaluate and refine plans. When incidents do occur, swift containment guided by playbooks is essential.

Modernizing endpoint protection is a key initiative SOC leaders must champion given endpoints’ importance in security chains. Contact DBGM to discuss navigating your endpoint security transformation journey.

Migrating business systems to the cloud unlocks game-changing agility and innovation. But it also introduces new security challenges that require robust cloud-centric defenses. In this guide, we’ll outline 5 critical best practices for securing your cloud transformation journey.

1. Adopt a Zero Trust Approach

The perimeter-focused security models of the past must be replaced with a zero trust approach for the cloud era. Zero trust ensures all access is securely authenticated and authorized while limiting lateral movement after breaches.

Key zero trust capabilities like multi-factor authentication, microsegmentation, and least privilege access should be implemented across your cloud environment. As your organization adopts SaaS apps, take advantage of identity and access management tools to control access centrally.

2. Automate Security Hygiene and Compliance

Cloud environments are dynamic, so security controls and compliance checks need to be automated as code. Leverage infrastructure as code tools like Ansible, Terraform, and Kubernetes to repeatedly deploy hardened configurations.

Use policy as code tools like Chef InSpec to continuously validate that configurations adhere to security standards. Taking this DevSecOps approach embeds security into cloud management workflows.

3. Protect All Access Paths

While the cloud provider secures the physical infrastructure, you’re responsible for protecting access to your environments and data. Lock down admin console access, remote management ports, customer-facing services, and other endpoints.

Analyze network traffic patterns and limit access to only essential ports. Enforce multifactor authentication across every possible entry point. Authentication errors and access denied events should also be monitored as warnings.

4. Centralize Security Monitoring and Controls

The sprawl of cloud environments creates new monitoring and management challenges. Unify visibility and security controls across on-prem, hybrid, and multi-cloud through security platforms like Microsoft Sentinel.

This enables threat detection, behavioral monitoring, automated response playbooks, and more to be orchestrated from a single pane of glass. Prioritize integrations with cloud access control, workload protection, and analytics services.

5. Plan for Security Incidents

Despite best efforts, some threats may evade defenses, so cloud incident response planning is crucial. Document plans for critical scenarios, roles and responsibilities, technical playbooks, and communications.

Test effectiveness through simulated incidents, and conduct debriefs for continuous improvement. Planning for inevitable incidents enables agile, assured responses.

Securing cloud transformations requires aligning security models, controls, and processes to the new environment. Adopt the 5 best practices outlined above to enable security at cloud speed and scale. Partnering with experienced cloud security consultants like DBGM can help you implement these measures while avoiding missteps. Let us guide your cloud security journey.

Most organizations focus enormous efforts securing their internal environments. However, the exposed external attack surface requires equal attention, as this is how attackers initially access victims before pivoting internally. In this guide, we’ll explore how continuously monitoring your external attack surface needs to become a security priority.

Map All Internet-Exposed Assets

The first step is creating a comprehensive inventory of external facing systems like domains, networks, servers, services, cloud buckets, and applications. Traditional asset management tools only show internal assets, so specialized attack surface management platforms are required.

Attack surface analyzers like Pentest as a Service use combinations of active scans, crawling technology, and threat intelligence to build automated live maps of your exposed attack surface. This inventory becomes the foundation for ongoing monitoring.

Detect New or Misconfigured Assets

With an inventory established, attack surface monitoring can detect rogue exposures like domains registered without authorization, misconfigured cloud instances, or new ports opened by employees.

By alerting on any changes from the known good baseline, security teams can identify and mitigate emerging risks before attackers discover them. Integrate with IT workflows to automate remediation when deviations occur.

Assess Vulnerabilities Continuously

Map not just the presence of external assets but their security posture via continuous vulnerability scanning. Prioritize investigation for externally facing systems containing known vulnerabilities like unpatched servers.

Testing production assets typically requires using non-intrusive scanning techniques to avoid disruptions. Solutions like Randori and Intruder help find vulnerabilities in live environments safely.

Uncover Blind Spots Across Environments

Maintain comprehensive coverage across hybrid environments, from legacy network equipment to multiple cloud providers. Partnerships with security vendors like Microsoft and CrowdStrike bolster threat telemetry.

By correlating insights across internal and external data sources, you gain a unified view of cyber risk that spans environments. Eliminate blind spots that could be overlooked by siloed monitoring tools.

Quantify Changes in Cyber Risk

Analyze trends in your overall attack surface exposure to quantify whether cyber risk is increasing or decreasing over time. Factors like new domains, open ports, and detected exploits indicate heightened risk.

This empowers leadership discussions on risk using hard metrics versus subjective opinions. Make reducing attack surface exposure a tangible goal – for example, shrinking risk surface by 20%.

Continuous monitoring and mitigation of the external attack surface has become imperative as hybrid and cloud adoption expand organizational perimeters. Partner with our cybersecurity experts at DBGM to implement advanced attack surface management and harden your exterior defenses.

The advanced persistent threats facing organizations today have outpaced the capabilities of traditional security tools. These attacks utilize dynamic tactics, leverage zero-days, and needle-in-a-haystack anomalies that evade rules-based defenses.

Artificial intelligence and machine learning offer great promise to arm security teams with the sophisticated analytical capabilities needed to uncover today’s most dangerous threats. In this article, we’ll explore key use cases where AI can help transform security operations and threat detection.

Boosting SOC Analyst Productivity

A major pain point for modern SOCs is the overwhelming volume of alerts generated across the environment. Tired analysts struggle to pinpoint actual incidents amidst alerts flooding their dashboards every minute of every day. It’s difficult to spot the signals in all that noise.

AI-driven security analytics platforms leverage unsupervised learning algorithms to ingest huge volumes of security data from across domains. By discovering subtle but meaningful patterns and correlations, AI solutions can identify combinations indicative of emerging threats. This distills billions of data points down to a high-fidelity stream of priority incidents for analyst review.

platforms like Microsoft Azure Sentinel and Splunk apply this AI filtering to elevate the most critical threats from massive data lakes. Analyst productivity improves drastically when high-confidence alerts are automatically surfaced rather than getting buried in noise. AI becomes a SOC’s best friend.

Uncovering Stealthy Adversary Tradecraft

Today’s most dangerous threat groups like APTs and nation-state actors utilize advanced tradecraft to evade detection during campaigns. This includes techniques like “living off the land” using built-in system tools, slow crawling across networks, and blending-in amidst normal user behaviors.

Behavioral analytics powered by artificial intelligence are essential to recognizing these stealthy activities that specification-based defenses miss. By establishing profiles of normal behavior across users, devices, and systems, AI can detect anomalies indicative of emerging threats that humans would easily overlook.

User behavior analytics (UBA) solutions profile typical access patterns for users like time-of-day and peer groups. Machine learning algorithms identity outliers suggesting credential theft or insider threats. Network traffic analytics search for covert exfiltration in legitimate flows. Together, behavioral analytics expose threats trying to hide in plain sight.

Identifying Zero-Day Vulnerabilities

By definition, zero-day exploits take advantage of previously unknown software vulnerabilities. Without awareness of the vulnerability, defenses lack the signatures required to block attacks abusing these flaws. AI techniques offer hope for getting ahead of zero days.

Algorithms can be trained using vast databases of historical vulnerability data to discern patterns predictive of vulnerabilities in source code. AI evaluates code syntax and semantics to flag high-risk segments likely to be susceptible to memory corruption issues, injection flaws, or logical errors. AI-driven code reviews surface these trouble spots for remediation before releases.

On the defensive side, AI behavioral models recognize activity deviations indicative of possible zero-day attacks such as unusual registry or system file access. By flagging suspected zero-day activity, incident responders can contain intrusions stemming from as yet unknown vulnerabilities.

Scaling Threat Hunting Capabilities

Proactive threat hunting led by specialized security analysts is vital for preempting threats before they trigger alerts. But thorough hunting requires considerable time and expertise to pore through massive data sets seeking hidden red flags.

AI and machine learning help automate aspects of the hunting process to enable more extensive, rapid campaigns. Data science techniques identify relationships and anomalies that hunters can pivot off of to drive deeper investigations.

Tools like Deep Instinct accelerate hunting by allowing virtual assistants, chatbots, and automated queries to surface leads from across endpoints, networks, clouds, and applications. AI amplifies the productivity of expert hunters, allowing them to search faster and wider.

The Path Forward with AI

While AI shows enormous potential across these use cases, current solutions still have limitations. Algorithms require extensive training data that sufficiently represents the deploying organization’s environment and threat landscape. Models need continuous maintenance and tuning as new tactics evolve.

However, the machine learning journey starts with initial pilot projects focused on high-value challenges like phishing detection or insider threat discovery. Early successes demonstrate practical benefits, which builds trust in AI among stakeholders. A strategic roadmap accounts for incremental advances in capabilities, data science skills, and model explainability.

Over time, organizations expand AI deployments across core threat detection and response workflows. The future SOC platform will integrate AI engines applied across security telemetry feeds. Threats attempting to hide will eventually have nowhere to run or hide from persistent, multipronged AI.

Partnering with MSSPs and AI-focused security consultancies like DBGM accelerates success. Our data scientists and SOC experts help architect the foundations for an AI-enabled SOC, deliver pilot projects, and provide ongoing tuning of algorithms aligned to your environment. Contact DBGM today to chart your AI in security journey.