Data Vu: Why Breaches Involve the Same Stories Again and Again

2022-07-29 13:18:48
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In the classic comedy Groundhog Day, protagonist Phil, played by Bill Murray, asks “What would you do if you were stuck in one place and every day was exactly the same, and nothing that you did mattered?” In this movie, Phil is stuck reliving the same day over and over, where the events repeat in a continual loop, and nothing he does can stop them. Phil’s predicament sounds a lot like our cruel cycle with data breaches.

Every year, organizations suffer more data spills and attacks, with personal information being exposed and abused at alarming rates. While Phil eventually figured out how to break the loop, we’re still stuck: the same types of data breaches keep occurring with the same plot elements virtually unchanged.

Like Phil eventually managed to do, we must examine the recurring elements that allow data breaches to happen and try to learn from them. Common plotlines include human error, unnecessary data collection, consolidated storage and careless mistakes. Countless stories involve organizations that spent a ton of money on security and still ended up breached. Only when we learn from these recurring stories can we make headway in stopping the cycle.

The main plotline of so many data breach stories is human error. Over and over, people fall for phishing scams, fail to patch vulnerable software promptly, lose devices containing vital data, misconfigure servers or slip up in any number of other ways.

Hackers know that humans are the weak link. Many break-ins to company databases occur less by technological wizardry and more by con artistry. For instance, hackers can trick an organization’s employees by sending an e-mail that looks like it’s coming from one of their supervisors. Doing so is easy: anyone can readily learn the names of supervisors by looking them up on LinkedIn and can then spoof an e-mail address. Essentially, hackers hack humans more than they do machines.

Despite the fact that human error is an aspect of most data breaches, many organizations have failed to train employees about data security. As for the organizations that do, they often use long and boring training modules that people quickly forget. Not enough attention is paid to making training effective.

It’s reasonable to expect that even with a well-trained workforce, some people will inevitably fall for hacker tricks. We must approach data security with realism that people can be gullible and careless, and human nature isn’t going to change. That means we need systems and rules in place that anticipate inevitable breaches and minimize their harm.

In many data breaches, an enormous amount of information is lost all at once. because hacked organizations were collecting more data than absolutely necessary, or keeping such information when they should have been deleting it.

Over time, organizations have been collecting and using data faster than they have been able to keep it secure—much like in the 19th-century industrial revolution when factories sprouted up before safety and pollution controls were introduced. Instead of hoarding as much information as possible, they should enact policies of data minimization to collect only data necessary for legitimate purposes and to avoid retaining unnecessary data.

To make matters worse, many organizations have stored the vast troves of information they amass in a single repository. When hackers break in, they can quickly access all the data all at once. As a result, breaches have grown bigger and bigger.

Although many organizations fear a diabolical hacker who can break into anything, what they should fear most are small, careless errors that are continually being made.

For instance, an entirely predictable mistake is a lost device. Lost or stolen laptops, phones and hard drives, loaded up with personal data, have played a big role in breaches. Companies should assume that at least some losses or thefts of portable devices will occur—and to prevent disaster, they should require that the data on them be encrypted. Far too often, there is no planning for inevitable careless mistakes other than hoping that they somehow won’t happen.

Money alone is not enough to stop hackers. In fact, many of the organizations that have had big data breaches were also big spenders on data security. They had large security teams on staff. They had tons of resources. And yet, their defenses still were breached. The lesson here is that money must be spent on measures that actually work.

In the case of the Target breach in 2013, the company had spent a fortune on a large cybersecurity team and on sophisticated software to detect unusual activity. This software worked and sent out alerts—but security staff members were not paying enough attention, and reportedly they had turned off the software’s automatic defenses. Having the best tools and many people isn’t enough. A security team must also have a good playbook, and everyone must do their part.

Although at the surface, data breaches look like a bunch of isolated incidents, they are actually symptoms of deeper, interconnected problems involving the whole data ecosystem. Solving them will require companies to invest in security measures that can ward off breaches long before they happen—which may take new legislation.

With a few exceptions, current laws about data security do not look too far beyond the blast radius of the most recent breach—and that worsens the damage that these cyberattacks cause. Only so much marginal benefit can be had by charging increasing fines to breached entities. Instead, the law should target a broader set of risky actors, such as producers of insecure software and ad networks that facilitate the distribution of malware. Organizations that have breaches almost always could have done better, but there’s only so much marginal benefit from beating them up. Laws could focus on holding other actors more accountable, so responsibility is more aptly distributed.

In addition to targeting a wider range of responsible entities, legislation could require data minimization. With reduced data, breaches become much less harmful. Limiting data access to those who need it and can prove their identity is also highly effective. Another underappreciated important protection is data mapping: knowing what data are being collected and maintained, the purposes for having the data, the whereabouts of the data and other key information.

Government organizations could act proactively to hold companies accountable for bad practices before a breach occurs, rather than waiting for an attack. This strategy would strengthen data security more than the current approach of focusing almost entirely on breached organizations.

But the law keeps on serving up the same tired consequences for breached companies instead of trying to reform the larger data ecosystem. As with Phil, until lawmakers realize the errors of their ways, we will be fated to relive the same breaches over and over again.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

参考译文
数据Vu:为什么入侵事件一次又一次涉及相同的故事
在经典喜剧《惊天营救》中,由比尔·默瑞扮演的主角菲尔提出了这样一个问题:“如果你被困在一个地方,每天完全重复,你做的一切都没有意义,你会怎么做?”在电影中,菲尔被困在一天的重复轮回中,事件不断循环,他无论做什么都无济于事。菲尔的处境听起来非常类似于我们面对数据泄露的残酷循环。每年,组织都遭受越来越多的数据泄露和攻击,个人信息以惊人的速度被泄露和滥用。虽然菲尔最终发现了打破循环的方法,但我们仍然被困在轮回中:相同类型的数据泄露不断发生,情节几乎毫无变化。正如菲尔最终所做的那样,我们也必须审视那些导致数据泄露反复发生的共同元素,并尝试从中吸取教训。常见的情节包括人为错误、不必要的数据收集、集中存储以及粗心大意的失误。无数案例显示,有些组织投入了大量资金在安全上,但最终仍然遭遇攻击。只有当我们在这些反复出现的事件中吸取教训时,才能打破这一循环。许多数据泄露故事的主要情节是人为错误。一而再、再而三地,人们落入网络钓鱼的骗局,没有及时修复存在漏洞的软件,丢失了存储重要数据的设备,服务器配置失误,或者以其他各种方式出错。黑客们知道,人类才是最薄弱的环节。许多对数据库的入侵并非依靠高超的技术,而是依赖欺骗手段。例如,黑客可以通过发送看起来像是来自主管的电子邮件来欺骗组织的员工。这其实很容易:任何人都可以通过LinkedIn查到主管的名字,然后伪造一个电子邮件地址。换句话说,黑客其实攻击的是人类,而不是机器。尽管人为错误是大多数数据泄露事件中的一个组成部分,但许多组织并没有对员工进行数据安全方面的培训。而有些组织即使进行了培训,也往往是冗长乏味的模块,人们很快就会忘记。很少有人重视培训的有效性。即使员工经过充分培训,一些人仍然不可避免地会中黑客的圈套,这也是可以理解的。我们必须以现实的态度看待数据安全,承认人类容易轻信和粗心,而人性不会轻易改变。这意味着我们需要建立机制和规则,以预见不可避免的泄露并尽量减少其危害。在许多数据泄露事件中,大量信息被一次性丢失,因为被入侵的组织收集了远超必要范围的数据,或者在本应删除时仍然保留了这些信息。随着时间推移,组织收集和使用数据的速度快过了保护数据的能力——这类似于19世纪工业革命时期,工厂在安全和污染控制措施出台之前就纷纷建立起来。与其尽可能地囤积信息,不如实施数据最小化政策,只收集具有正当用途的必要数据,避免保留不必要的信息。更糟糕的是,许多组织将海量数据存储在一个单一的数据库中。一旦黑客入侵,他们可以迅速获取全部数据,因此数据泄露的规模也变得越来越大。尽管许多组织害怕那种能入侵任何系统的恶魔式黑客,但实际上他们最应该害怕的,是那些不断出现的小而粗心的错误。例如,设备丢失就是一个完全可以预料的错误。装载有个人数据的笔记本电脑、手机和硬盘的丢失或被盗,已经在许多数据泄露事件中扮演了重要角色。公司应该假设至少会有一部分便携设备丢失或被偷——为了防止灾难发生,他们应该要求这些设备上的数据必须加密。然而,很多时候除了希望这些粗心大意的错误不会发生外,几乎没有其他应对措施。金钱本身并不足以阻止黑客。事实上,很多遭遇重大数据泄露的组织同时也是在数据安全上投入巨大的企业。他们拥有庞大的安全团队,有丰富的资源。然而,他们的防御系统仍然被攻破了。这个教训告诉我们,金钱必须被用于真正有效的措施。以2013年塔吉特百货公司的数据泄露为例,该公司投入了巨额资金用于组建大型网络安全团队和购买先进的软件来探测异常行为。这些软件确实发挥了作用并发出了警报,但据报道,安全人员没有给予足够的重视,甚至关闭了软件的自动防御功能。拥有最好的工具和大量人员是不够的。安全团队还必须拥有良好的应对策略,并且每个人都必须各司其职。尽管表面上看数据泄露事件是一系列孤立的事件,但实际上它们是更深层次、相互关联的问题的体现,这些问题是整个数据生态系统的一部分。要解决这些问题,企业必须投资于能够提前防范数据泄露的安全措施——这可能需要新的立法。除了极少数例外,目前的数据安全法规并没有超出最近一次数据泄露的范围太远——这反而加剧了这些网络攻击所造成的损害。仅仅对受攻击的企业不断增加罚款所能带来的边际效益是有限的。相反,法律应该针对更广泛的风险方,比如生产不安全软件的公司,或者助长恶意软件传播的广告网络。发生数据泄露的组织几乎总是可以做得更好,但一味惩罚它们所带来的边际效益也是有限的。法律可以将责任更多地分配给其他相关方,比如生产不安全软件的公司,从而促使他们承担更多责任。除了针对更广泛的责任主体外,立法也可以要求数据最小化。数据越少,数据泄露带来的危害就越小。限制只有那些真正需要数据并且能够证明自己身份的人才能访问这些数据,也是一种非常有效的做法。另一个被低估的重要保护措施是数据映射:了解正在收集和保存的数据、收集数据的目的、这些数据的存储位置以及其他关键信息。政府机构可以在数据泄露发生之前主动追究企业在不良实践方面的责任,而不是等到攻击发生后再采取行动。这种策略比目前几乎完全针对受攻击企业的做法更能增强数据安全。但法律仍然反复使用相同的陈旧后果来惩罚受攻击的企业,而不是尝试改革整个数据生态系统。就像菲尔一样,除非立法者意识到自己的错误,否则我们注定要一次又一次地经历同样的数据泄露。本文是一篇观点与分析文章,文中作者的观点未必代表《科学美国人》的立场。
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