A Risk Management Analysis of Google's A2A and Anthropic's MCP

Everything old is new again, including the risks.

Have you noticed it’s been hard to keep pace with AI news lately? Google recently announced Agent2Agent (A2A), an open protocol designed to enable seamless collaboration between AI agents across frameworks and vendors. This largely complements Anthropic's Model Context Protocol (MCP), which was launched in November 2024. While Anthropic’s MCP provides a standardized way for AI models to connect with tools and data, Google’s A2A focuses on enabling agent-to-agent communication. That doesn’t tell the whole story, so let’s unpack it through the lens of risk management.

Risk Identification

1. Security

Google claims A2A is "secure by default" with enterprise-grade auth/auth support that matches the OpenAPI schemes we all know and love. The protocol was designed with security as a core principle, sure… but there are still inherent security risks. A2A handles sensitive data exchanges between agents, so what’s unclear (to me, anyway) is if those schemes plus RBAC will be enough.

MCP already introduces potential vulnerabilities where compromising a single (MCP) server could grant attackers broad access to a user's digital ecosystem or an org’s resources. It leads the imagination to prompt injection risks, so needless to say there are a number of variables at play.

While MCP focuses on protecting the gates to your data kingdom with API-level security and access tokens, A2A is more about securing the channels between entities; same security headache, different threat vectors, and twice the attack surface if you're running both.

2. Data Privacy

MCP is kind of amazing in that it provides a universal connection to various data sources. A Universal Translator of epic proportions, unreal orchestration potential to say the least, and each opportunity a potential hole in the old security dam. To mitigate, orgs need some pretty robust access controls per resource and (likely automated) data classification to ensure both security and GRC alignment. While devs can get started without many of the larger considerations, implementation needs to be more structured.

Then you add A2A to the mix, and we start to develop privacy concerns. The protocol enables agents to share and process multimodal data (text, audio, video, etc) in unified workflows, which increases the scope of potential data exposure. Basically every shred of available data comes into scope. (Ask your friendly neighborhood CIO or CISO how nervous that part makes them.)

3. Ops

A2A is designed to support ‘opaque’ agents that don't expose their internal reasoning or memory, monumentally important for enterprise use cases where security, modularity, or vendor abstraction are required. It sort of harkens back to the early days of LLMs (well, of ChatGPT) where we had to stuff the rabbit back in the hat with policies and staff training until we had private instances for staff to use. Anyway, rather than syncing internal states, agents share context via "Tasks," which include inputs, instructions, results ("Artifacts"), and live status updates. That last term (Artifacts) should remind you of how Anthropic approached storing work products in Claude in 2024.

For MCP, ops risks include integration complexity - you are perhaps composing a monolith after all - and potential maintenance challenges. Without standardized protocols, every AI integration must be approached (and maintained!) as a custom solution, creating significant overhead for orgs. “Traditional” cloud solutions architects might have a hard time adapting, but I don’t see a clear alternative in this adoption curve.

4. Governance

There's a governance risk related to all fragmentation, in this case over the protocols themselves. With some competition in agentic interop standards emerging (A2A, MCP, AGNTCY), it’s tough to know which adoption cycle to follow, or at least to follow first. Google acknowledges this and has stated they will "look at how to align with all of the protocols" and incorporate good ideas from other standards. Now, I don’t know about you, but I take that statement with a grain of salt given Google's history with similar commitments.

Remember XMPP (Extensible Messaging and Presence Protocol), that amazing open messaging standard that Google initially championed with Google Talk? And then with Google Hangouts in May 2013 they dropped federation support? After that amazing “open offer to interoperate forever”? That last comment and their eventual actions stuck it to Microsoft in particular, in my opinion an ironic twist given that they killed federation just days after Microsoft announced XMPP support in Outlook.com. (Ouch, guys. Just… ouch.)

Risk Assessment

A2A addresses a key challenge in AI: the lack of a common mechanism for agents to discover, communicate, and coordinate across ecosystems. In many industries, orgs deploy multiple AI systems for specific functions, but without a clear integration pattern. I’ve followed that siloed path myself in the last couple of years, with Claude Sonnet 3.x serving the most in terms of functional departments served.

But if A2A gains traction, it could accelerate an even broader agent ecosystem along the lines of Kubernetes or OAuth. Yes, that fundamental. (Potentially.) By solving interop at the protocol level, A2A lowers barriers for orgs to mix-and-match agents from different providers. In topology terms, while MCP focuses on the application layer vertically integrated with the LLM, A2A provides horizontal integration between agents. I don’t think the resulting math is a mere multiplier, folks: It’s an exponent.

Think about it this way: If MCP gives each agent access to n data sources, and A2A connects a disparate agents, we're not just looking at n * a connections — we're talking about n^a potential workflows as each chain of agent collaborations can access different combinations of data in different sequences. That's the stuff of sleepless nights right there.

But perfect synergy, right? Actually, I can see some potential for tension. Google’s positioning A2A as complementary to MCP, but there’s a little, uh… “overlap”. (Always read the README to the end!)

Risk Treatment

I sideways-mentioned this at the top, but orgs looking at implementing A2A should focus on the security aspects of the protocol, particularly RBAC and data encryption on the wire. It’s a great opportunity, before plugging it in, to seek breach prevention or at least mitigation. If ever there was a case for top-down, this is it.

For MCP implementations, I think orgs should implement identity-centric security approaches where AI agents access data using the end user's identity (or perhaps RBAC again!), limiting access to only what the end user (or RBAC) is authorized to see. If you can’t federate identity, the least-privilege principle is a great start. But honestly, let’s get real here - if you’re looking to do this level of magic, practice some due care with your data already.

Governance

You can’t reasonably roll these together into a full implementation with disparate governance frameworks. Well, you could, and at first you’re going to have to. And until Google truly open-sources contributions to A2A, devs have to assign contributions to Google, then take a shower.

Until we have a shared framework, though, adopting orgs need to monitor the evolution of both protocols, as they may eventually merge into a single standard. A man can dream. The current proliferation of competing standards mentioned above suggests a consolidation phase may occur as the market matures, similar to other standards that we enjoy today. How soon? Well, not “fast” I wouldn’t say, but the rate of change we’ve been seeing these past few years might accelerate that consolidation, or force some proposed standards to fail fast.

Business Impact

End of the day, A2A and MCP aim to address the challenges of siloed systems without extremely heavy integration projects. I would say that it’s a “Zapier (et al) Killer”, except things like Zapier MCP have emerged. There might be too little incentive to involve something like that except for the case where you’re trying to leverage existing connections therein. Maybe. Definitely not too late, though. I’ll eventually have to build a decision matrix for the case, but I’m not looking forward to how short-lived that’ll be if it happens before some of the market maturity I’ve already teased as likely.

So What Now?

From a risk management perspective, Google's A2A and Anthropic's MCP represent both opportunities and challenges. They promise to standardize and simplify AI agent interactions and data access. Nothing wrong with that. But they also introduce new security, privacy, operational, and governance risks that orgs (CIOs and CISOs) must address.

The success of these protocols, a merged standard, or whatever shape this takes will largely depend on a balance between openness and security, obviously. But for now, orgs should approach implementation with careful risk assessment and mitigation strategies at the ready. And always, always, read that README.

CREDITS: image OpenAI Sora; editorial review Anthropic Claude Sonnet 3.7