Introduction to Securing AI Applications
Security organizations are better able to secure the enterprises they defend when they are given a chance to act strategically, rather than react tactically. When it comes to application security, that necessitates involving the security team and building in security much earlier in the software development lifecycle.
In recent years, most security practitioners have been watching the AI hype cycle very carefully. Indeed, the explosion of AI onto the scene brought with it many unresolved questions around governance, risk, and compliance. While security practitioners considered these questions strategically and carefully, they were left wondering why, if AI was such a hot topic, they weren’t seeing it affect their lives very much operationally.
The Challenge of Securing AI Applications
Recently, one reason why this may be has become clear. Unfortunately, as we are very well accustomed to in the security field, security seems to have been an afterthought in many instances. While there are exceptions, in many enterprises, security was not in the loop with application owners, development teams, and others that were experimenting with AI use cases.
Not surprisingly, when some of these AI use cases showed value, enterprises began moving those AI use cases to production. This phase has been happening more in recent months than it had previously, and not surprisingly, the security team has often not been in the loop. As noted above, being caught by surprise is far from ideal.
Data-Driven Discussions
Most security teams do not have as good a relationship with the application owners and development teams as they would like. They also know that improving this relationship is an important component to involving security much earlier in the software development lifecycle. Leveraging real data to drive data-driven discussions can help.
Approaching application owners and development teams with specific numbers around potential monetary loss, brand reputation damage, or other risks, along with specific vulnerability data, sensitive data exposures, or other threats, is far more likely to serve as a catalyst to kick-off productive discussions that will pave the way for improving these important relationships.
Agility
It is no secret that modern enterprise environments are far more complex than they used to be. The on-premises world was relatively straightforward compared with today’s hybrid and multi-cloud world. While this evolution has brought numerous advantages with it, most notably the ability to bring features and improvements to market much more quickly, it has created more than a few security challenges.
Security agility is the key here – security teams, unfortunately, need to prepare for and set themselves up to be able to operate in this type of environment. Simplifying the complexity becomes a necessary tool when it comes to being able to defend AI applications.
Operational Workflow
If the security operations workflow is sufficiently robust and mature, it makes it easier to integrate new data, events, alerts, and other information from AI applications. As you might imagine, this helps greatly with the security team’s ability to rapidly integrate AI applications and their accompanying data into the operational workflow.
Future-Proofing
While AI applications have some AI-specific components, large portions of these AI applications are built on top of existing application and API technology stacks. Because of that, much of the security we need to properly secure AI applications is already present in existing application and API security stacks.
What we need to do is ensure that these stacks are future-proofed to the best extent possible. If we do this properly, then we’ll simply be able to “turn on” or integrate new AI-layer specific security measures that our existing security layers don’t provide.
Proactivity
Good security hygiene is a must, and an important part of this hygiene is continuous scanning of application security, API security, and AI security layers. This enables us to identify and mitigate risks, vulnerabilities, exposures of sensitive data, and other issues before they become a far more serious problem.
Contextual Awareness
The AI layer requires unique security capabilities above and beyond what we already have at the application and API layers. In addition to continuously and proactively identifying security issues, we must also be prepared to identify and respond to runtime security issues.
Doing so requires a tremendous amount of contextual awareness. This requires specialized technological capabilities that understand how to parse, analyze, and understand the AI layer in context, and to use that understanding to identify attacks, abuse, fraud, DDoS, and other issues in near real-time.
Security teams are bound to be blindsided by AI applications moving from the experimentation phase into production. There are a number of steps security organizations can take to improve their readiness in these instances. While this state of affairs is far from ideal, by taking several important strategic steps, security teams can greatly improve their ability to respond quickly, agilely, and appropriately.
- Data-driven discussions
- Agility
- Operational workflow
- Future-proofing
- Proactivity
- Contextual awareness
Source: SecurityWeek