Posts Tagged ‘Cloud’
If you want to skate to where the puck is going in security today, it’s best to think big – as in Big Data. To detect stealthy breaches by advanced adversaries, you need to analyze a greater volume and variety of data, at a greater velocity – the so-called “3 V’s” of Big Data. Big Data analytics is as critical to security as to any other field, because it holds the promise of analyzing data sets too large to process in the past – in other words, solving previously unsolvable problems. In this way, it can help discover insights – such as security compromises or malicious behavior – that would have otherwise lay hidden.
The best way to obtain security analytics at Big Data scale is with a purpose-built security intelligence architecture that can scale to meet your needs, unpredictable as they might be. You want a solution that can expand as your business grows, as you analyze new types of security data, and as your security process maturity increases. One requiring minimal administration but offering maximum flexibility. In other words, a security intelligence cloud.
Just what is a security intelligence cloud? (No, it’s not a cloud-delivered security intelligence solution.)
It starts with the building blocks of security intelligence:
- Integrated capabilities for SIEM, log management, behavioral anomaly detection, configuration & vulnerability management, and forensics
- Via a pre-packaged and scalable solution, just as you would expect from a SaaS application
This contrasts with the inflexible architectures and non-scalable databases of legacy security products.
Let’s consider the most appealing characteristics of cloud computing and their role in a Security Intelligence (SI) Cloud:
- Scalability and elasticity – This is arguably the most central aspect of cloud computing, and the security intelligence cloud in particular. Through an architecture that supports high-speed data collection and real-time correlation, using a flexible and distributed database, an SI cloud not only performs security analytics at Big Data scale but also adjusts on-demand to changing needs.
- Location independence – A security intelligence cloud enables you to capture data from anywhere in your network, correlate it globally, and make it available instantaneously to users worldwide. By using a federated, distributed data architecture that abstracts physical data stores, an SI cloud eliminates underlying data management complexity – just as an IaaS cloud solution abstracts the physical locations and capacities of server hardware from the IaaS customer.
- Agility – An essential element of the cloud model, agility is critical for security intelligence deployments because the volume and variety of data monitored will grow over time, and you might need to change the types or locations of data collection sensors across your network.
- Cost structure – Whether you deploy your security intelligence cloud on a (virtualized) cloud platform might determine how much you end up substituting operational for capital expense, but either way, an SI cloud should provide a cost-effective and growth-friendly solution that doesn’t require large expenditures for incremental volume increases.
- Maintenance – An SI cloud can offer further benefit through the use of appliances that are pre-configured and require minimal infrastructure management. This allows users to focus on the task at hand: detecting the risks that matter and remediating them appropriately.
- Reliability – A modern SI cloud offers native, integrated high availability and data redundancy to enhance overall reliability, like public cloud services.
Just as server virtualization is a foundational technology for cloud computing, a security intelligence cloud can leverage virtualization for cost and agility benefits, as warranted by the organization’s preferences, existing virtual infrastructure, and provisioning speed requirements. It can run on-premise, off-premise or in a hybrid of both. While most customers find the provisioning of hardware appliances fast enough, virtual appliances provide an excellent option when on-demand capacity is needed in minutes.
What’s most important, though, is for the SI cloud to provide a highly elastic data management layer, so that actual system capacity can increase proportionately with storage and computing, rather than get bottlenecked due to architectural constraints.
Collectively, these capabilities enable a security intelligence cloud to be an agile platform for big data security analytics. And we believe QRadar provides the ideal security intelligence cloud, because it fits the requirements above so well.
Major enterprises are using QRadar today to collect and correlate billions of events and network flows per day, in deployments that span multiple locations and connect previously siloed operational groups.
- A Fortune 100 telecommunications provider collects and monitors one million events per second – more than 85 billion events per day – to ensure security and regulatory compliance across its massive customer operations.
- A global energy company uses QRadar to ensure NERC and PCI-DSS compliance (monitoring 6 million card swipes per day) while correlating 2 billion events per day. It performs real-time analysis to determine the 25-50 priority incidents that matter each day – for a roughly 40-million-to-one data reduction ratio.
With the recent release of QRadar 7.1, there are even more ways to use QRadar in the cloud, and to manage big data security analytics. For example, Index Management enables higher performance and better use of storage, through advanced reporting and tuning capabilities. QRadar is also complemented by several recently released IBM Security products that are making cloud computing safer and more effective.
For a related perspective, I also recommend my colleague Chris Poulin‘s recent paper which discusses how an organization’s security or risk management group can use security intelligence as an internal cloud service to support groups such as firewall management, systems management and network management.
To close with another of my favorite Gretzky quotes, you miss 100 percent of the shots you don’t take! Don’t miss your chance to learn what a modern security intelligence solution can do for your business. Take the next step in our QRadar Resource Center.
With the release of QRadar Security Intelligence Platform 7.1, we’re excited to share with you a host of new advances to our family of Security Intelligence products – including QRadar SIEM, QRadar Log Manager and QRadar Risk Manager. These innovations are making it easier for users to leverage cloud investments, simplify management, collect and manage data more flexibly, and replicate or extend QRadar deployments. As a result, QRadar users will receive even greater insight and visibility, further reduce manual work and gain higher system performance. Let’s dive in!
Leverage Cloud Investments
We know many of you have built significant private and public cloud infrastructures and are looking for new virtual workloads to deploy in the cloud. With QRadar 7.1 you now have an additional type of appliance – the Event Collector – that you can deploy virtually, providing more ways to use your cloud environment to gain richer security intelligence.
Event collectors – which come in both virtual and hardware appliance form – provide continuous event logging capabilities, even when network connectivity is unreliable. They collect event logs and forward them to an event processor or all-in-one appliance for correlation, analysis and long-term storage. If network connectivity is lost, they can queue events in a storage buffer and then forward them upon re-connecting. (We call this “store and forward.”) In addition to serving locations with intermittent network connections (like naval vessels), event collectors are well-suited for collecting logs in distributed locations with low to moderate event volumes, such as retail stores and satellite offices. A large retailer, for example, might have hundreds of stores in which they want to collect event data, but the data generated in each location is modest enough that event processors (with terabytes of storage per appliance) aren’t required.
With this release, you now have access to a full complement of virtual appliances – console & all-in-one, event processor, flow processor, VFlow collector, and event collector – to best utilize your current and future cloud infrastructures. Even better, appliances can be mixed and matched among virtual appliance, hardware appliance and traditional software form factors, to meet your specific needs.
Simplify Management – Especially for Big Data
As we and others like Scott Crawford and Jon Oltsik have written, information security is truly a big data analytics challenge today. With its heritage in network flow collection and anomaly detection, QRadar has been collecting and correlating massive data sets in real-time since before big data became a white-hot phenomenon. Critical infrastructure and tier-one telecommunications providers, banks, and energy and utility companies are using QRadar to correlate as many as one million events per second (EPS) in real-time, thanks to QRadar’s purpose-built, embedded Ariel database. But with such massive data volumes come management challenges.
In response, we developed new Index Management capabilities in QRadar 7.1 that provide more refined data management and ultimately better performance. As the volume of stored data explodes, challenges inherent in querying big data become more pronounced – and so do the benefits of optimizing indexes for the queries most often run. QRadar’s default search indexes have always followed the 80/20 rule, providing out-of-the-box indexing for the most commonly used properties. Now we’re taking indexing a step further, enabling deep customization and tuning.
With QRadar 7.1, users have granular control over the creation of search indexes that enable speedy querying. While the fixed database indexing configuration that QRadar has historically provided works well for most scenarios, some clients would benefit from additional or different indexes. That’s why we added the ability to customize the indexing scheme for the event and flow database – so users can drop existing indexes to free up system resources or create new indexes to optimize the system for their specific needs.
QRadar also provides invaluable visibility into the use of indexes – with statistical reporting on the frequency of searches involving each property, how often each property’s index is used, and the size of each index – to help inform indexing decisions. This enables more efficient storage utilization and superior search performance.
Do you suspect one property is getting searched a lot? Get the data.
Do you wonder how big an index has grown? Find out.
Want to start indexing a custom property and see how often that index is used? No problem.
Another new capability that simplifies management is QRadar Risk Manager’s Enhanced Policy Monitoring. Risk Manager excels at monitoring network configurations and system vulnerabilities for potential security and compliance violations, and has always alerted when a policy is violated. Now it takes monitoring a step further with the ability to automatically notify when a policy is passed, providing positive evidence of compliance with external regulations and internal corporate policies. For example, you might want a positive notification when the percent of regulatory assets with Internet exposure vulnerabilities is within policy, or when the percent of regulatory assets with client side vulnerabilities that have communicated with the Internet is within policy. Now you can gain affirmative proof of such compliance.
Collect and Manage Data More Flexibly
QRadar 7.1 also offers new capabilities for collecting and managing data with greater flexibility. These include WinCollect – a versatile and scalable new QRadar capability for Windows event collection. WinCollect provides a superior and agentless means for collecting events from large numbers of systems. Installed on a Windows server of the customer’s choice, WinCollect can use the Windows Event Log API to pull events from target systems and then forward them to QRadar, or use Windows event forwarding and allow target systems to automatically push events to it and then forward them to QRadar. WinCollect complements existing collection mechanisms, including Q1 Labs’ own ALE solution, third-party approaches, and native Windows Server capabilities. In a subsequent blog post, we’ll explain the advantages of each approach and the value of having a broad set of choices.
Event collectors (described earlier) also help simplify data collection and management, in addition to leveraging cloud infrastructure and enabling event collection under unreliable connectivity. To begin with, their ability to “store and forward” data not only applies when a network connection is lost; it can also be used proactively for policy-based event forwarding. In some cases, a remote location might have reliable but limited network bandwidth, and you might want to limit the collector’s use of bandwidth to specific (less busy) times. With QRadar 7.1, you can limit forwarding by bandwidth utilization (e.g., never consume >1MB/second), and/or set an hourly, daily or weekly forwarding schedule. In addition, event collectors can filter event data before it is forwarded for correlation, reporting and long-term storage.
Additionally, we have released more than a dozen new product integrations (device support modules) that enable users to normalize and analyze even more types of security telemetry. These include IBM Security zSecure Audit, which allows sending z/OS, RACF, ACF2, Top Secret, DB2, and CICS events from the System Management Facilities (SMF) log to QRadar (in addition to the native z/OS logs that QRadar already collects). We have also completed integrations with many third-party products, such as Verdasys Digital Guardian, AppSecInc DbProtect and Trend Micro Deep Discovery.
Build Extended Solutions and Replicate Existing Deployments
Lastly, we are enabling clients to build extended security intelligence solutions and replicate existing deployments. With Security Intelligence Content Importing/Exporting, you can export correlation rules, building blocks, reference sets, report templates, dashboard widgets and more from a QRadar system to an external device, and subsequently import them into another QRadar system. This enables quick deployment of a new QRadar system based on an existing system or template, as well as sharing of security intelligence content across systems.
We see this being used in several ways:
- Enabling clients to copy custom-built security intelligence content from one deployment to another (across business units or geographies)
- Enabling clients to copy content from a development or test environment to a production system
- Enabling solution providers and system integrators to build unique Security Intelligence intellectual property that they can distribute to their customers.
While QRadar already delivers thousands of rules, report templates, dashboard widgets and saved searches out-of-the-box, many business partners have additional expertise to offer to clients, and have been eagerly awaiting this capability.
To Learn More
With this hefty release completed, we’re gearing up to bring some fantastic new innovations to market in 2013. In the meantime, please try QRadar 7.1 for yourself and let us know what you think. We also encourage you to learn about the other IBM Security product releases just announced, which include capabilities for securing big data environments (including IBM InfoSphere BigInsights and Cloudera), risk-based access control for mobile users in BYOD environments, and privileged identity management.
To read more about using SIEM for targeted attack detection (APT’s), you can also download this Gartner report. Or see how organizations are using network flow analytics for better threat detection and network visibility with this Q1 Labs paper. Best wishes in your security journey!
While I only have one first cousin, we have bizarre similarities and notable differences. First off, she’s 12 and about ten times smarter than I am (yes, I set myself up with that one). We share some slightly similar facial features, personality traits, and food tastes that favor northern Italian cuisine. She is an accomplished violinist already. I hack at my guitar every once in a blue moon. Anyway… enough kicking myself in the teeth.
What does this have to do with SIEM and cloud computing? Similar to my previous “cloud security” themed post, I will again reference the best practices paper by Q1 Labs’ CSO Chris Poulin. In this, he suggests that SIEM itself provides a cloud-type capability and is structurally similar. I find this a very interesting correlation and pretty darn accurate in many ways. Lets get into it.
A classic SIEM is fed data from all around an organization via different groups with varying requirements and responsibilities. These groups cross organizational divides and often have very different interests, data types, and use cases. SIEM has definitive customers and providers, as do cloud providers. For example, the systems management group may feed Microsoft Windows Active Directory events into the SIEM to be alerted on user login failures, signaling a brute-force password attack or escalation of privileges attempt.
Cloud providers are fed data from different customers, expecting their data to be protected, segmented from other customers, controlled, secured, and monitored. A cloud provider is also expected to not access customer data for their use or benefit unless allowed by the customer. While this may not 100% correlate to a SIEM environment, there are contractual obligations between the operational management function and SIEM consumers to ensure processes are in place to handle potential incidents, empowering the data owners and developing a clear escalation process.
Related: What’s in a cloud security plan?
This points out one of the differences between cloud and SIEM, and why they might be cousins, yet only distant cousins. The SIEM provider generally has total context and an overarching security responsibility, otherwise known as security intelligence, that spans across data from all groups. For example, correlating vulnerability scanner results with firewall logs and network activity to detect an active threat. In the case of cloud services, there is a clear dividing line between roles and responsibilities; especially involving customer data. The data belongs to the customer and has to be treated differently. An example is GMail. Most likely, it wouldn’t be accepted if Google started reading our email or forwarding it to other GMail users. Okay, they are reading it, but hopefully not forwarding.
What do you think, are there other similarities between cloud and SIEM? Besides SIEM being a lot smarter than cloud, that is.
Learn more about IT Security best practices in cloud environments.
Q1 Labs’ CSO, Chris Poulin, recently authored a paper defining best practices for IT Security in a cloud environment. In this, he covers some interesting viewpoints on various hurdles expected when organizations secure their public or private cloud environments, as well as the steps necessary to create an effective security policy, and the similarities between SIEM and cloud environments.
What are a few of the steps cloud providers and customers can take when building out their own cloud security plan? One major chunk of the process is to start with an assessment of risk. That is, understand your current data types, locations, business processes, and information flow. Understand where the critically sensitive data is. Just like any other enterprise, cloud computing requires customers and cloud providers to define their own information topology before any reasonable security policy can be defined and implemented.
Step 1: Discovery
Know where all of your data is, no matter how you classify it. The key is uncovering the difference between the data that can and cannot be housed in the cloud. An eDiscovery process is recommended to locate buried and even misplaced data. Too often organizations find that Personally Identifiable Information (PII) is mixed with less critical data and matched with the wrong security protocols.
Step 2: Classification
After understanding where your data is, it needs to be classified appropriately and distributed to systems with security controls to match the data sensitivity. This step alone can help you make progress meeting various compliance regulations.
Step 3: Data transit
SIEM can help define your data transit policy by monitoring endpoints, firewalls, and network activity to govern if the data should be allowed to proceed to the cloud or not. Content-aware network profiling from Data Loss Prevention (DLP) solutions can fed to the SIEM to perform more complex correlations with other data feeds. For example, watch for PII such as a social security number in a patient healthcare record and combine that with the firewall logs and network activity found within a SIEM to gain a bigger picture of malicious activity.
As Chris Poulin has blogged, there is no question that more modern SIEM (a.k.a. Security Intelligence) solutions have their place in the cloud. It’s not a matter of if SIEM is ready for the cloud, but if the cloud is ready for SIEM. For more on IT Security best practices in cloud environments, take a spin through Chris’ complete writeup.
Related: SIEM and Cloud might be cousins
Day 1: we participated in a panel at the William Blair Technology Symposium, “The Future of Cloud Computing.” The panel was entitled, “How the Cloud Changes Security”. Great topic, great panel, staffed by security solutions suppliers. Major takeaway based on the questions asked of the panel, by the investment community, not end-users: confusion reigns supreme, and most likely due to the outrageous amount of hype surrounding “cloud”. Usually the questions were about virtualization, but using cloudy(ed?) language.
Day 2: Q1 Labs Customer Council. The topic of Cloud came up twice: once in the form a customer’s presentation of one of his major use cases: SIEM as the security intelligence platform for his companies cloud-based services offerings. He relies on QRadar for visibility/compliance and intelligence/threat management to both ensure the integrity of his brand and to provide proactive threat management. And once from another customer in the form of a question, essentially: “What is the role of SIEM in the Cloud, in your opinion?” This generated a very grounded discussion of the various cloud types (private, public, hybrid, multitenant) and how SIEM, Log Management, Vulnerability Management play a role. Clarity prevailed: one size does not fit all use cases.
Prosodie (France) another customer, recently announced their adoption of our QRadar Security Intelligence Platform for both visibility/compliance for their cloud-based services and in addition uses QRadar SIEM from Q1 Labs for intelligence/threat management of their internal network, similar to the customer referenced above.
So, customers view cloud clearly (like how I did that?) as a potentially viable business proposition worthy of examination, versus a cool technology shift: if cloud adoption enables them to do their jobs better and grow their businesses, great. If not, it drops off the list of priorities.
And it would appear that while the market observers might be a bit confused about the exact utility and deployment model of the cloud, customers are not, and seem to have a pretty clear vision for SIEM’s role in securing it.