4 Cloud Security Trends to Watch Out for in 2022
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When it comes to cloud security, 2022 will be the year that the past catches up with the future. Trends that businesses have been ignoring for too long will force organizations large and small to confront and control their security debt.
That’s according to Google Cloud’s own cybersecurity experts, who have identified four security trends that organizations need to watch out for—and get ahead of. We have predictions on what to expect in the coming year from MK Palmore, director of the Office of the CISO; Brian Roddy, vice president of engineering for Cloud Security; Tim Dierks, engineering director for data protection; and Panos Mavrommatis and Vikram Makhija, senior directors of security engineering for Google Cloud.
Supply chain shenanigans
“We will see continued asymmetric attacks from adversaries as they exploit supply chains and other previously ‘trusted’ third-party entities,” says Palmore.
Supply-chain problems in cloud computing should be easily solvable, right? Software versions and any vulnerabilities they contain should be trackable and patchable, but the reality of fixing software is that “just patch it” is hard to execute—just look at the challenges posed by the Log4j 2 vulnerability. Supply chain is such a huge problem that President Biden addressed it in an Executive Order in May 2021. Customers can expect the issue to be top of mind at Google Cloud.
Not exactly many happy returns (to the office)
“Return to office around the world will drive changes as office infrastructure has not been invested in for a year and a half while the focus has been on remote users. This likely will drive a short-term boom in traditional on-prem security, but it will be the last boom for that as people adapt their remote, zero-trust style strategies to a more modern on-prem approach,” says Roddy.
The misconception that on-prem infrastructure is categorically more secure than cloud is driven by the desire to have physical access to servers and backups so that only the organization which owns the data controls it and has access to it, even in cases of a catastrophic failure or successful cyberattack. In the early years of cloud computing, that may even have been true. But the conditions that drove the myth of on-prem security primacy changed years ago, and the needs driving secure cloud infrastructure help ensure that cloud stays more secure.
Paying down your security debt
“While there’s all the new hotness of cutting-edge concerns, many enterprises still carry security risks and security debt from not yet fully adopting controls which have been broadly accepted as important for years. For example, loads of companies are still not using phishing-resistant two-factor authentication such as FIDO keys,” says Dierks.
Authentication keys such as those made by Yubico and Google’s own Titan Security key support the zero-trust security principles that require user identities to be authenticated, authorized, and then continuously validated before they can access applications and data. Strong authentication is such an important part of contemporary user security that even weaker forms of it that rely on text messages are significantly more secure than not using it at all. That said, why use a less-secure standard when you can reduce risks to your data and bottom line even further by requiring a phishing-resistant hardware key?
Dierks stresses another challenging but important part of eliminating security debt: using social connections to encourage best security practices. “It’s important for CISOs to use their business relationships to emphasize the importance of baseline controls [such as 2FA] for their partners. Enterprises have close relationships that attackers can leverage, so it’s critical that partners hold each other accountable to maintain high security.”
KYD (Know Your Data)
The impact of a data breach can harm organizations as they currently are as well as far into the future. Current tough-to-crack encryption standards protecting data could become easier to decode in the years ahead, so even if cybercriminals can’t access stolen data now there’s no guarantee that paradigm will hold. This means it’s crucially important that organizations understand what data they’re storing, how they’re storing it, and where they’re storing it, say Mavrommatis and Makhija.
“You can’t secure what you don’t know about, and not all data breaches are equal. Stolen machine logs are not as bad as customer data. But how many security teams know the difference? So you have to crawl your own data to automatically classify and discover where sensitive data lives,” they say.
Makhija adds that the shared fate model requires the cloud providers and cloud customers to have a mutual understanding of the quantitative risks each faces. “Shared fate models will pick up significantly in 2022,” he says, as more organizations move to the cloud, and those already using cloud infrastructure improve their security postures.
“To date, there’s been a disparate set of tools for understanding your posture. It’s difficult for third-party tools to stitch together what cloud services should be providing from the start,” he says.
What you can do to make your organization more secure
One cloud security trend that’s ever-present is the ever-increasing importance of keeping cloud deployments secure. As cloud infrastructure becomes more commonplace across businesses and industries of all sizes, it will continue to grow as an attractive target for cybercriminals and other threat actors.
- Because enterprise data has expanded exponentially, the ability to identify and detect threats have become increasingly challenging. To better secure the enterprise software supply chain, use advanced threat detection and analysis tools—especially those designed to catch anomalies.
- The faster that organizations adopt a zero-trust architecture, the more secure the new normal can be. Zero trust helps limit the blast radius of any potential intrusion, while maximizing new enterprise access expectations. Part of adopting zero trust means many end-users can abandon legacy technology like VPNs, but the benefits of segmentation and context-aware access for both identity and device will make all the difference for large scale enterprises. When coupled with a full zero-trust approach and the use of a zero-trust maturity model for improvement, organizations will be better positioned to manage their digital risks.
- Security debt can come in many forms and one critical payoff that needs to be made is for organizations to migrate en masse to hardware two-factor authentication keys. They make user accounts significantly more resistant to takeovers, and are much harder to circumvent than two-factor authentication over SMS.
- It’s past time to get to know your data, and a bouquet of flowers and a bottle of red wine won’t help. There are third-party tools that can do this, but for Google Cloud customers who use BigQuery there’s automatic Data Loss Prevention. It continuously monitors existing tables and profiles new ones; it can be customized for selected folders or projects, or for an entire organization; and it generates data profiles in the same geographic region as the original data.
Understanding and managing the security challenges of cloud infrastructure helps maximize its benefits, and makes for a safer security landscape in 2022—and beyond.
Supercharging Security with Generative AI
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At Google Cloud, we continue to invest in key technologies to progress towards our true north star on invisible security: making strong security pervasive and simple for everyone. Our investments are based on insights from our world-class threat intelligence teams and experience helping customers respond to the most sophisticated cyberattacks. Customers can tap into these capabilities to gain perspective and visibility on the most dangerous threat actors that no one else has.
Recent advances in artificial intelligence (AI), particularly large language models (LLMs), accelerate our ability to help the people who are responsible for keeping their organizations safe. These new models not only give people a more natural and creative way to understand and manage security, they give people access to AI-powered expertise to go beyond what they could do alone.
At the RSA Conference 2023, we are excited to announce Google Cloud Security AI Workbench, an industry-first extensible platform powered by a specialized, security LLM, Sec-PaLM. This new security model is fine-tuned for security use cases, incorporating our unsurpassed security intelligence such as Google’s visibility into the threat landscape and Mandiant’s frontline intelligence on vulnerabilities, malware, threat indicators, and behavioral threat actor profiles.
Google Cloud Security AI Workbench powers new offerings that can now uniquely address three top security challenges: threat overload, toilsome tools, and the talent gap. It will also feature partner plug-in integrations to bring threat intelligence, workflow, and other critical security functionality to customers, with Accenture being the first partner to utilize Security AI Workbench.
The platform will also let customers make their private data available to the platform at inference time; ensuring we honor all our data privacy commitments to customers. Because Security AI Workbench is built on Google Cloud’s Vertex AI infrastructure, customers control their data with enterprise-grade capabilities such as data isolation, data protection, sovereignty, and compliance support.
Preventing threats from spreading beyond the first infection
We already provide best-in-class capabilities to help organizations immediately respond to threats. But what if we could not just identify and contain initial infections, but also help prevent them from happening anywhere else? With our AI advances, we can now combine world class threat intelligence with point-in-time incident analysis and novel AI-based detections and analytics to help prevent new infections. These advances are critical to help counter a potential surge in adversarial attacks that use machine learning and generative AI systems. That’s why we’re excited to introduce:
- VirusTotal Code Insight uses Sec-PaLM to help analyze and explain the behavior of potentially malicious scripts, and will be able to better detect which scripts are actually threats.
- Mandiant Breach Analytics for Chronicle leverages Google Cloud and Mandiant Threat Intelligence to automatically alert you to active breaches in your environment. It will use Sec-PaLM to help contextualize and respond instantly to these critical findings.
These new updates build on the existing AI in Google’s industry-leading solutions. For example, Chronicle Security Operations already uses frontline intelligence, integrated reasoning, and machine learning to identify initial infections, prioritize impact, and contain threats. Another example is reCAPTCHA Enterprise, which uses image noising capabilities to help protect your site from adversaries that leverage novel AI advances, greatly enhancing our defenses against bots.
Adding intelligence to reduce toil
At Google Cloud, we help organizations modernize security wherever they are, in part by simplifying their security tools and controls whenever possible. Advances in generative AI can help reduce the number of tools organizations need to secure their vast attack surface areas and ultimately, empower systems to secure themselves. This will minimize the toil it takes to manage multiple environments, to generate security design and capabilities, and to generate security controls. Today, we’re announcing:
- Assured OSS will use LLMs to help us add even more open-source software (OSS) packages to our OSS vulnerability management solution, which offers the same curated and vulnerability-tested packages that we use at Google.
- Mandiant Threat Intelligence AI, built on top of Mandiant’s massive threat graph, will leverage Sec-PaLM to quickly find, summarize, and act on threats relevant to your organization.
These announcements build on existing capabilities that help customers centralize visibility and control, detect targets, and improve security across their platform. For example, Security Command Center (SCC) uses always-on machine learning to detect malicious scripts executing in the customer container environment and immediately alert the customer. In addition, Cloud Data Loss Prevention leverages machine learning to find and classify data, and with Confidential Computing you can collaborate on, train, and deploy sensitive and regulated AI models in the cloud, all while preserving confidentiality.
Evolving how practitioners do security to close the talent gap
At Google, we believe that to truly democratize security, we need to first acknowledge that AI will soon usher in a new era for security expertise that will profoundly impact how practitioners “do” security. Most people who are responsible for security — developers, system administrators, SRE, even junior analysts — are not security specialists by training.
Imagine a world where novices and security experts are paired with AI expertise to free themselves from repetition and burnout, and accomplish tasks that seem impossible to us today. To help power this evolution, we’re embedding Sec-PaLM-based features that can make security more understandable while helping to improve effectiveness with exciting new capabilities in two of our solutions:
- Chronicle AI: Chronicle customers will be able to search billions of security events and interact conversationally with the results, ask follow-up questions, and quickly generate detections, all without learning a new syntax or schema.
- Security Command Center AI: Security Command Center will translate complex attack graphs to human-readable explanations of attack exposure, including impacted assets and recommended mitigations. It will also provide AI-powered risk summaries for security, compliance, and privacy findings for Google Cloud.
These new releases bolster our existing efforts to tackle these issues through capabilities like IAM Recommender, which suggests permissions better suited to actual usage patterns. We will soon be augmenting this capability to cover organizational policies, further enabling the administrator to help improve the security posture of their organization. In addition, Mandiant Automated Defense applies machine learning to help reduce the repetitive Tier 1 alert triage problem and address alert fatigue.
Offering availability
VirusTotal Code Insight, available now in Preview, is our first example of putting Security AI Workbench to work for our customers. We will be rolling out other offerings to trusted testers in coming months, and they will be available in Preview more broadly this summer. Click here for the demo.
Security AI Workbench, including Sec-PaLM and partner integrations, in addition to the product innovations described in our demo, are all building blocks for a larger effort to elevate security across the ecosystem. So far, that effort:
- Provides assistive functions to rapidly develop IT generalist talent to Tier 1 security operator status in a way that wasn’t previously feasible. Security Command Center now can summarize threat intelligence insights and findings for Google Cloud, and Chronicle can quickly generate YARA-L rules or other detections.
- Provides advanced functions such as iterative query and multivariate detection generation, conversational filtering and interaction with results, and smart case awareness to empower advanced Tier 2 and 3 security operators to focus on threat analysis instead of struggling with process and toil. Mandiant Threat Intelligence users now can elevate their core competencies to hunt, investigate, and remediate threats — using the same tools our own Mandiant experts use.
- Fuses threat intelligence and AI-based analytic capabilities, which are unsurpassed in the market. VirusTotal Code Insight enables security teams to help gain insights and identify threats in suspicious code. This can significantly enhance their ability to detect and mitigate potential attacks.
However, this is just an initial step. We’ll continue to iterate and innovate, and we encourage customers and partners to leverage Security AI Workbench in new and exciting ways. Moving forward, we anticipate many new use cases to emerge over time.
Building a safer future
While generative AI has recently captured the imagination, Sec-PaLM is based on years of foundational AI research by Google and DeepMind, and the deep expertise of our security teams. This work includes new efforts to expand our partner ecosystem to provide businesses with security capabilities at every layer of the cybersecurity stack. We have only just begun to realize the power of applying generative AI to security, and we look forward to continuing to leverage this expertise for our customers and drive advancements across the security community.
The Many Risks of Ignoring the Impact of a Tighter Cloud Security Framework
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The perimeter has disappeared and access to company data is no longer limited to your physical office or your employees. Instead, in today’s transformed workforce – increasingly connected, collaborative and in the cloud – the security perimeter has become dispersed and elastic, wrapped around each user and device.
Moreover, ‘users’ no longer refers to simply employees, but also vendors, partners, contractors, and customers. Each of these groups has its own requirements – access to different information and applications, from different locations and different devices. In this ever-evolving ecosystem of users, apps, and devices, traditional approaches to identity and access management aren’t ready for this new environment.
Time-consuming and complex, these approaches were built for the on-premise world (think cumbersome VPNs, limited device access and inconvenient authentication), instead of today’s cloud-first world.
Organizations are also facing increased pressure for digital transformation, higher compliance standards to prevent the loss of company data, and more sophisticated cyber-attacks – it’s no wonder organizations are grappling with these unprecedented pressures. Clearly, a new approach to identity management is needed.
That’s where Cloud Identity can help – an identity, access and device management (IAM/EMM) platform that helps organizations maximize user and IT efficiency, protect company data with Google-grade security, and transition to a digital workspace at their own pace.
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How to Retrace the Steps of a Potential Phishing Attack with Threat Analytics
The majority of cyber attacks begin with phishing emails and websites. Attackers use many tricks, including by leveraging enterprise brand assets, such as company names and logos, to develop phishing websites that appear authentic and lure internet users to enter valuable information such as user names and passwords.
Experts say that today’s CISOs need to treat a potential phishing email like a crime scene. We need to use the evidence that we have to carry out the investigation. A common phishing email will sound like this: “Hello! Your security software has expired. Please click this link to renew it.”
This is where Google’s Backstory makes an entry. Find out how it can help trace the steps of what happened to conduct a thorough threat analysis of your network.
Google Cloud Announces General Availability of BigQuery Row-level Security
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Data security is an ongoing concern for anyone managing a data warehouse. Organizations need to control access to data, down to the granular level, for secure access to data both internally and externally. With the complexity of data platforms increasing day by day, it’s become even more critical to identify and monitor access to sensitive data. In many cases, sensitive data is co-mingled with non-sensitive data, and access restrictions to sensitive data need to be enabled based on factors like data location or presence of financial information. There may also be nuances where data is sensitive for some groups of users, while for others, it is not.
Today, we’re pleased to announce the general availability of BigQuery row-level security, which gives customers a way to control access to subsets of data in the same table for different groups of users. Row-level security (RLS) extends the principle of least privilege access and enables fine-grained access control policies in BigQuery tables. BigQuery currently supports access controls at the project-, dataset-, table- and column-level. Adding RLS to the portfolio of access controls now enables customers to filter and define access to specific rows in a table based on qualifying user conditions—providing much needed peace of mind for data professionals.
“Our digital transformation and migration of data to the cloud magnifies the business value we can extract from our information assets. However, granular data access control is essential to comply with international regulatory and contractual requirements. BigQuery row-level security helps us comply with data residency and export restrictions,” says Jarrett Garcia, Iron Mountain’s Enterprise Data Platform Senior Director. “It enables us to manage fine-grained access controls without replicating data. What used to take months for approval and access provisioning can now be done more efficiently and effectively. We are looking forward to implementing additional data security capabilities on the BigQuery roadmap to address other critical business use cases.”
How BigQuery row-level security works
Row-level security in BigQuery enables different user personas access to subsets of data in the same table. Customers who are currently using authorized views to enable these use cases can leverage RLS for ease of management. To express the concept of RLS, we have introduced a new entity in BigQuery called row access policy. Row access policies map a group of user principals to the rows that they can see, defined by a SQL filter predicate.
Secure logic rules created by data owners and administrators determines which user can see which rows through the creation of a row-level access policy. The row-level access policies created on a target table by administrators or data owners are applied when a query is run on the table. One table can have multiple policies applied to it.
Below is an example, where row-level access policies have been created to filter data based on users’ “region”.
In the illustrated scenario above, row-level access policies have been created to verify a querying user’s region and to give them access only to the subset of data relevant to that region. Access policies are granted to a grantee list which support all types of IAM principles such as individual users, groups, domains or service accounts. In this example, when a user queries the table, row-level access policies are evaluated to assess which, if any, policies are applicable to that user. The group ‘sales-apac’ is granted access to view a subset of rows where region = ‘APAC’ whereas the group ‘sales-us’ is granted access to view a subset of rows where the region = ’US’. Likewise, users in both groups will see rows in both regions, and users in neither group will not see any rows.
Row-level access policies can also be created using the SESSION_USER() function to restrict access only to rows that belong to the user running the query. If none of the row access policies are applicable to the querying user, the user will have no access to the data in the table.
When a user queries a table with a row-level access policy, BigQuery displays a banner notice indicating that their results may be filtered by a row-level access policy. This notice displays even if the user is a member of the `grantee_list`.
When to put BigQuery row-level security to work
Row-level access policies are useful when you have a need to limit access to data based on filter conditions. The row-access policies’ filter predicate supports arbitrary SQL, and is conceptually similar to the WHERE clause of a SQL query. Filter predicates support the SESSION_USER() function to restrict access only to rows that belong to the user running the query. If none of the row access policies are applicable to the querying user, the user will have no access to the data in the table. Currently, the column used for filtering must be in the table, but we anticipate adding support for subqueries in the filter expression, opening up access to use cases where data is filtered based on lookup tables and calculated values. Row-level access policies can be created, updated and dropped using DDL statements. You will be able to see the list of row-level access policies applied to a table using the BigQuery schema pane in the Cloud Console, which simplifies the management of policies per table, or by using the bq command-line tool.
Row-level security is compatible with other BigQuery security features, and can be used along with column-level security for further granularity. Since row-level access policies are applied on the source tables, any actions performed on the table will inherit the table’s associated access policies, to ensure access to secure data is protected. Row-level access policies are applicable to every method used to access BigQuery data (API, Views, etc).
Try it out
We’re always working to enhance BigQuery’s (and Google Cloud’s) data governance capabilities, to provide more controls around managing your data. With row-level security, we are adding deeper protections for your data. You can learn more about BigQuery row-level security in our documentation and best practices.
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AppSheet: Reduce Shadow IT and Accelerate Development of Enterprise-grade Apps
Google Cloud findings suggest that nearly 40 percent of organizations’ investments are consumed by shadow IT and can be a detractor to the adoption of cutting-edge tools and solutions. Also, about 51 percent of the surveyed executives are of the opinion that the inability to adapt to digital transformation trends and practices are at the risk of going out of business in the next 3-4 years. However, enterprises need not be blindsided by the mounting expenses involved with the implementation and optimization of solutions and tools meant for empowering employees. AppSheet, Google Cloud’s no-code application development and automation platform is at the helm of empowering organizations to custom build apps for employees without relying on third-party services.
Watch the video from the Google Workspace sessions of Next ’21 to hear experts’ insights on AppSheet to effectively govern workforce and ward off security threats to helps employees build enterprise-grade applications!
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