Blockchain Operational Excellence: Monitoring, Incident Response & Deployment Discipline

[0:00]Harasis: Welcome back to the show, everyone. Today we're diving into the world of operational excellence with blockchain. We'll talk about what it really takes to monitor, respond to incidents, and deploy with discipline in these distributed, high-stakes environments. I'm thrilled to introduce Priya Das, Lead Blockchain Operations Architect at ChainOps Collective. Priya, thanks for joining us.

[0:23]Priya Das: Thanks for having me! I’m excited to be here and talk about something that’s close to my heart—and, honestly, sometimes keeps me up at night.

[0:32]Harasis: I love that. Before we get into the details, can you share a bit about your background and what brought you into blockchain ops?

[0:45]Priya Das: Sure! I started in traditional IT operations, running infrastructure for financial services. But a few years ago, I got fascinated by the intersection of distributed systems and programmable money. Since then, I’ve worked on both public and private blockchains—helping teams design operations for reliability, security, and, most importantly, trust.

[1:13]Harasis: That trust piece is huge. So, when we talk about 'operational excellence' in blockchain, what does that actually mean to you?

[1:28]Priya Das: Great question. For me, operational excellence is about making sure the blockchain platform is reliable, observable, and can recover gracefully from failure. It's also about having disciplined, repeatable processes—so when something does go wrong, you’re not scrambling. In blockchain, mistakes are often public and hard to undo. Excellence means being proactive, not just reactive.

[1:53]Harasis: That’s a good point. I imagine there’s a lot of overlap with traditional ops, but also some big differences. What’s unique about blockchain from an operations perspective?

[2:11]Priya Das: Yeah, so... in traditional systems, you control the whole stack. With blockchain, especially public networks, you only operate your own node or set of nodes, but you depend on a much wider, decentralized ecosystem. Incidents can originate outside your perimeter, and changes propagate in unpredictable ways. That makes monitoring and coordination more challenging.

[2:39]Harasis: So the boundaries are fuzzier. How does that impact what you monitor?

[2:48]Priya Das: Exactly. You need to monitor what’s happening locally—CPU, disk, memory, network traffic—but also what's happening on-chain. For example, you want to track block propagation, consensus liveness, transaction throughput, and orphan rates. And you need to know how your node is interacting with the wider network, which isn’t always visible from your logs.

[3:13]Harasis: Let’s pause and define a few of those. What’s an orphan rate?

[3:20]Priya Das: Sure! In blockchain, an 'orphan block' is a valid block that doesn’t end up in the final chain—usually because two nodes found a solution at similar times. High orphan rates can mean network delays or consensus issues. Monitoring orphan rates helps catch network splits or performance problems before they spiral.

[3:45]Harasis: That’s helpful. So, you’re not just looking at server health, but also at how the blockchain itself is behaving.

[3:53]Priya Das: Absolutely. Traditional metrics are table stakes. But in blockchain, you add protocol-specific telemetry. For example, are blocks being produced on time? Are transactions getting stuck? Are validators reaching consensus? All of that impacts both user experience and system trust.

[4:18]Harasis: Let’s talk about observability. What are the practical challenges to getting the full picture in a decentralized system?

[4:33]Priya Das: Observability is tough, honestly. You can’t reach into other people’s nodes to see what they’re doing. So you have to combine what you can measure locally with what you can infer from the network—for example, monitoring mempool size, block propagation delays, or gossip protocol health. Sometimes, you rely on community monitoring tools or third-party explorers for extra visibility.

[5:05]Harasis: That sounds like you’re dealing with a lot of uncertainty and partial information.

[5:12]Priya Das: Exactly. And that’s why incident detection is so challenging. You might see an issue locally, but it could be a symptom of a network-wide event—or vice versa. So you develop a sense for 'what normal looks like' and set up anomaly detection, not just threshold-based alerts.

[5:34]Harasis: What are the most critical metrics you recommend teams track for blockchain health?

[5:43]Priya Das: I’d say: block production times, block height lag, peer count, transaction throughput, and orphan rates. For validators, you also want to monitor missed attestations or proposals. And for smart contract platforms, watch gas usage, failed transactions, and pending transaction queues.

[6:10]Harasis: Are there metrics that are often overlooked?

[6:19]Priya Das: Yes—one is peer churn, meaning how often your node’s peer set changes. Sudden drops can indicate network partitions. Another is chain reorg depth. If you see frequent or deep reorganizations, it might signal instability or even an attack.

[6:42]Harasis: Can you share an example of when missing a key metric led to trouble?

[6:51]Priya Das: Definitely. There was a time when a team I worked with didn’t monitor orphan rates closely enough. A minor network upgrade introduced latency, and blocks started being orphaned at a higher rate. No one noticed until a major fork occurred and transactions were lost. It took hours to recover, and trust took weeks to rebuild.

[7:17]Harasis: Wow. That’s a costly lesson. How did you recover from that?

[7:24]Priya Das: First, we improved our block-level monitoring and set up real-time alerts for orphan rates. We also established a process for quickly rolling back changes and validating network health after upgrades. Most importantly, we learned to treat protocol-level metrics as first-class citizens, not just infrastructure stats.

[7:48]Harasis: You mentioned upgrades. How do you approach deploying changes in blockchain environments, where mistakes can be so visible?

[8:01]Priya Das: Carefully! Deployment discipline is crucial. We use staged rollouts—starting with testnets, then a small set of nodes in production. We monitor each stage before moving forward. Canary deployments work, but you have to be mindful of consensus: one buggy node can cause issues if it’s a validator. And, of course, we always have rollback plans.

[8:31]Harasis: Let’s talk about incident response. Say you detect something’s wrong—maybe a consensus failure. What’s next?

[8:43]Priya Das: First, confirm the scope. Is it local, or network-wide? We’ll reach out to other operators to cross-check. If it’s network-wide, we coordinate on an incident bridge—sometimes in public forums, sometimes in private operator channels. The key is clear communication and fast triage. Sometimes, that means pausing block production or disabling faulty nodes until we know what’s going on.

[9:15]Harasis: Sounds a bit like war rooms in traditional ops, but with more players and higher stakes.

[9:23]Priya Das: Exactly. Plus, incidents are often public—users, exchanges, and other stakeholders are watching block explorers and social channels. So you have to balance transparency with caution. Too much information too soon can cause panic. Too little, and you lose trust.

[9:47]Harasis: How do you decide what to disclose during an incident?

[9:54]Priya Das: We follow a principle of responsible transparency—share enough so users know what’s happening, but hold back technical details that could help attackers until the issue is contained. We also coordinate messaging among node operators to avoid confusion.

[10:17]Harasis: Have you ever disagreed with other operators about how to respond to an incident?

[10:26]Priya Das: Absolutely. Sometimes, one group wants to halt the chain immediately, while others argue for more investigation. For example, during a smart contract bug, some wanted to freeze contracts, but others worried about overreach. In the end, we compromised—pausing only the affected contracts, not the whole network, and communicating the rationale clearly.

[10:54]Harasis: That’s a tough balance. What’s your advice for navigating those disagreements?

[11:03]Priya Das: Have clear escalation procedures and pre-agreed playbooks. Practice incident drills, so people know their roles. And foster a culture where raising concerns early is valued, not penalized.

[11:25]Harasis: Let’s zoom in on root cause analysis. How do you approach it in blockchain, where bugs can be in smart contracts, infra, or the protocol itself?

[11:37]Priya Das: It’s multi-layered. We start with logs and metrics—was it a host failure, a protocol bug, or a bad contract? For smart contracts, we use on-chain analytics to trace failing transactions. For infra, we dig into node logs and peer connectivity. Sometimes, it’s a combination—like infra issues exposing a latent contract bug.

[12:06]Harasis: Can you share a quick case study where a root cause surprised you?

[12:15]Priya Das: Sure. There was a situation where we kept seeing missed blocks. At first, we blamed network instability. But digging deeper, we found that a rogue smart contract was creating massive transaction spam, overloading mempools and slowing block production. Fixing it required patching both the contract and our node mempool configuration.

[12:41]Harasis: So sometimes it’s not where you expect. How do you avoid tunnel vision during incidents?

[12:50]Priya Das: Diverse teams help—a mix of infra, protocol, and smart contract folks. Also, having a blameless postmortem process encourages open investigation instead of finger-pointing.

[13:06]Harasis: Let’s talk about alerting. How do you balance automated alerts versus manual investigation?

[13:16]Priya Das: Automation catches the obvious—node down, high latency, block lag. But blockchain is full of subtle signals. Manual investigation is still essential, especially for anomalies that cross protocol boundaries. We try to automate what we can, but keep humans in the loop for judgment calls.

[13:41]Harasis: Have you ever been burned by too much automation?

[13:48]Priya Das: Yes, once we set up auto-remediation for node crashes. Turns out, it masked an underlying consensus bug—nodes kept restarting, but the chain was stuck. We learned to always pair automation with careful review.

[14:11]Harasis: Let’s recap where we are so far. We’ve talked about monitoring, incident response, and some challenges unique to blockchain. Up next, I want to dig into deployment discipline—how to make changes safely. But before we switch gears, any final thoughts on incident response?

[14:26]Priya Das: Just that communication is everything. Even the best monitoring can’t help if the right people don’t know what’s happening. Build relationships with other node operators before you need them.

[14:39]Harasis: That’s gold. Alright, let’s move into deployment discipline. What’s the biggest difference deploying code or config changes in blockchain versus other systems?

[14:53]Priya Das: Immutability is the big one. In many blockchains, once data is written, you can’t just 'fix' it. So upgrades have to be carefully planned and coordinated—especially for protocol changes that affect consensus. And because nodes are decentralized, you can’t count on every operator upgrading at the same time.

[15:18]Harasis: Does that mean blue-green or canary deployments are harder to execute?

[15:27]Priya Das: They’re possible, but the execution is different. For example, you might run a canary node with the new version and monitor its behavior on the network. But, with consensus upgrades, you often need a critical mass to switch at once. Blue-green works best for non-consensus changes, like APIs or dashboards.

[15:55]Harasis: Let’s dig into a real-world mistake. Can you share a deployment that went wrong?

[16:04]Priya Das: Absolutely. There was an incident where a hotfix was rolled out to address a smart contract bug. The fix hadn’t been tested on a shadow network and introduced a new bug that caused transaction processing to halt. Because rollback wasn’t possible, we had to coordinate a second fix and guide all node operators through a manual upgrade under pressure. Stressful, but a huge lesson.

[16:38]Harasis: Ouch. What would you do differently now?

[16:45]Priya Das: Never skip shadow testing, even for urgent fixes. And always have a clearly documented rollback or mitigation plan—even if you hope you never need it.

[16:55]Harasis: Let’s pause there. For listeners new to the term: what’s shadow testing?

[17:02]Priya Das: Shadow testing is when you run the new code in parallel with production, but it doesn’t affect users. You can compare results and catch issues before going live. For blockchain, you might replay real transactions on a testnet or in an isolated environment.

[17:26]Harasis: That’s super practical. What about rollback strategies? Given blockchain’s immutability, what’s realistic?

[17:37]Priya Das: For infra or node software, you can often roll back to a previous version, as long as the data format is compatible. For protocol or smart contract bugs, it’s much harder—sometimes, you need to coordinate a hard fork or use governance to roll forward with a fix. Prevention and thorough testing are really your best tools.

[18:00]Harasis: Do you ever see teams get too conservative because of rollback fears?

[18:08]Priya Das: Definitely. Some teams delay critical upgrades because they’re afraid of getting stuck. But that can actually increase risk—unpatched bugs linger. It’s about finding the right balance: thorough testing, staged rollouts, and clear communication.

[18:29]Harasis: We’re at the halfway mark, so let’s recap. So far, we’ve covered the importance of protocol-level monitoring, unique incident response challenges, and the need for disciplined deployment. Next up, we’ll dig deeper into advanced deployment strategies and discuss more lessons learned from the field. Stick with us.

[18:47]Priya Das: Looking forward to it! There’s plenty more to unpack.

[27:30]Harasis: Alright, let's pick up where we left off. We were just starting to talk about real-world monitoring challenges. I want to dig deeper into what actually happens when things go wrong in blockchain operations. Can you share a story or example where monitoring really made a difference—or failed to?

[27:54]Priya Das: Absolutely. There was this one DeFi platform—I'll keep it anonymous—that had a subtle smart contract bug. Their monitoring was mostly focused on uptime and transaction volume, with very little on-state changes within the contracts themselves. One day, users started reporting weird balances, but the dashboard looked fine.

[28:14]Harasis: Oh wow. So what happened?

[28:29]Priya Das: It took about six hours for the ops team to realize that a single function was being exploited repeatedly. The logs weren't granular enough to flag the pattern. They were alerted only when transaction fees spiked, which was a side effect. By then, the attacker had drained a significant sum.

[28:52]Harasis: That sounds painful. What would you say was missing from their monitoring setup?

[29:08]Priya Das: State-aware monitoring. It's not enough in blockchain to just track transactions or block confirmations. You need to observe key contract variables, event emissions, and even custom metrics. This team learned it the hard way.

[29:26]Harasis: So, for teams listening, what are some basic metrics or signals they should start tracking beyond just uptime?

[29:41]Priya Das: First, monitor the frequency and patterns of smart contract calls, especially on sensitive functions—like withdrawals or admin actions. Next, track failed transactions, gas consumption spikes, and unusual token movements. And always set up anomaly detection for on-chain events.

[30:01]Harasis: Love that. What about incident response? Once something’s detected, how does that play out in blockchain environments versus traditional ops?

[30:18]Priya Das: It's trickier. In web2, you can just roll back a database. In blockchain, actions are mostly irreversible. That means your response is about containment, communication, and sometimes, governance proposals to patch issues. Speed is crucial, but so is transparency.

[30:36]Harasis: Can you walk us through a typical incident response flow for a blockchain ops team?

[30:52]Priya Das: Sure. First, detection—hopefully automated. Then, triage: figure out if it's a critical exploit or a false alarm. If it's real, you alert stakeholders, freeze relevant contracts if possible, and communicate to the community. Documentation for post-mortem is ongoing. And if needed, coordinate with exchanges to prevent further damage.

[31:15]Harasis: So, transparency is a piece of the puzzle?

[31:26]Priya Das: Absolutely. Blockchain users expect openness. If your protocol gets hit and you’re silent, trust evaporates fast. Teams that share updates—even if they’re still investigating—tend to recover reputation more easily.

[31:44]Harasis: That’s a good point. I want to circle back to deployment discipline. What are some practices teams can adopt to reduce the risk of incidents in the first place?

[31:59]Priya Das: Rigorous testing is number one, especially with immutable smart contracts. Use staging environments that mirror mainnet conditions. Do multiple audits—internal and external. And when deploying, use mechanisms like timelocks and multi-sig approvals so changes can’t go live instantly without oversight.

[32:19]Harasis: Can you share a story where deployment discipline paid off?

[32:34]Priya Das: Definitely. There was a DAO that introduced a new voting module. They set a 48-hour timelock before the contract upgrade. During that window, a community member spotted a logic bug and raised it on their forum. Because of the timelock, they paused the upgrade, fixed the bug, and avoided what could have been a major governance exploit.

[32:59]Harasis: So, the community caught what an audit missed?

[33:08]Priya Das: Exactly. That’s the power of open review and staged deployment. It’s a great example of defense in depth.

[33:21]Harasis: Let’s get tactical. What are some tools or platforms you recommend for monitoring blockchain systems?

[33:37]Priya Das: For infrastructure, there’s Prometheus and Grafana, with custom exporters for blockchain nodes. For smart contract events, tools like Tenderly and OpenZeppelin Defender are popular. And for end-to-end tracing, some teams use ELK stack or cloud-native solutions adapted for Web3 data.

[33:58]Harasis: Are there any pitfalls with these tools?

[34:10]Priya Das: Definitely. One common mistake is over-relying on generic dashboards. Blockchain data is noisy and complex. You have to customize alerts so you’re not drowning in false positives, but also not missing the subtle attacks.

[34:29]Harasis: Makes sense. Quick follow-up: how do teams keep their monitoring up-to-date as the protocol evolves?

[34:43]Priya Das: It's a process. Every contract upgrade, you review and update your monitoring logic—add new event listeners, adjust threshold levels, and test alerting workflows. Some teams automate this as part of their CI/CD pipelines.

[35:01]Harasis: Alright, time for a rapid-fire round! I’m going to throw some quick questions your way. Ready?

[35:06]Priya Das: Let’s do it.

[35:09]Harasis: Biggest monitoring myth in blockchain ops?

[35:12]Priya Das: That on-chain means transparent and obvious. The devil’s in the details.

[35:17]Harasis: Most overlooked metric?

[35:19]Priya Das: Unusual contract event sequences.

[35:23]Harasis: Best way to practice incident response?

[35:26]Priya Das: Run simulated exploits—tabletop drills with live data.

[35:31]Harasis: Favorite post-mortem question?

[35:33]Priya Das: What did we miss, and why did we miss it?

[35:36]Harasis: One thing to never skip before deploying a contract?

[35:39]Priya Das: Upgrade path review—even if you think it’s final.

[35:42]Harasis: Documentation: overkill or essential?

[35:44]Priya Das: Essential. Even for the small stuff.

[35:48]Harasis: Alright, last one: Most exciting trend in blockchain ops right now?

[35:51]Priya Das: Automated on-chain response—contracts that can defend themselves.

[35:57]Harasis: That was awesome, thanks! Let’s pivot to another mini case study. Can you share an example where incident response went really well?

[36:16]Priya Das: Sure. There was a cross-chain bridge that detected a suspicious pattern of small withdrawals. Their system flagged it early, and they had a clear runbook: they paused transactions, alerted partners, and published a notice within 20 minutes. The exploit was contained, and funds were secured. The transparency and speed reassured users, and the protocol’s token price barely flinched.

[36:43]Harasis: Contrast that with a scenario where things went wrong. What usually derails incident response?

[37:00]Priya Das: Lack of clear ownership. Sometimes, teams scramble to figure out who’s responsible for what. On-chain systems can be complex, and if you’re not prepared, minutes turn into hours. Also, not having pre-written comms templates can slow down public updates.

[37:19]Harasis: That’s a great point. Do you think teams underestimate the communications side?

[37:29]Priya Das: Definitely. Technical fixes matter, but user trust is built with clear, timely updates—even if you don’t have all the answers right away.

[37:40]Harasis: Let’s talk about deployment discipline again. What’s your take on automated deployments versus manual reviews?

[37:53]Priya Das: It’s a balance. Automation reduces human error and speeds things up, but for smart contracts, every deployment should go through at least one human review. I’m a fan of automated pipelines for peripheral updates, but core contracts need eyes on them.

[38:10]Harasis: Are there any frameworks that help teams enforce this discipline?

[38:22]Priya Das: Yes—change management frameworks adapted from DevOps, like GitOps, work well. Combine that with blockchain-specific practices like multisig sign-offs, staged rollouts, and upgrade beacons.

[38:40]Harasis: Let’s clarify multisig for anyone new: why is it important in deployments?

[38:53]Priya Das: Multisig—short for multi-signature—means that multiple people must approve a transaction before it executes. In deployments, this prevents a rogue actor or mistake from pushing code live alone. It’s a social circuit breaker.

[39:09]Harasis: What about the trade-offs? Any downsides to multisig?

[39:21]Priya Das: Speed is the obvious one. If you need urgent fixes, waiting for multiple signatures can slow you down. But for most production deployments, the safety is worth it.

[39:35]Harasis: Let’s talk about mistakes. What’s a common deployment mistake you see in blockchain projects?

[39:44]Priya Das: Skipping testnets or staging environments. Teams sometimes push directly to mainnet under pressure. That’s where invisible bugs turn catastrophic.

[39:57]Harasis: Any advice for teams under launch pressure?

[40:09]Priya Das: Set clear launch criteria ahead of time. If you’re tempted to cut corners, remember: one mainnet bug can cost more than any delay. Take the time to run full test cycles.

[40:23]Harasis: On that note, how do teams ensure their test coverage is actually meaningful?

[40:36]Priya Das: Mix automated unit tests with scenario-based fuzzing. And get creative—simulate attacks, edge cases, and user errors. Also, review code with people outside the dev team for fresh eyes.

[40:51]Harasis: How important is it to include the community in testing or monitoring?

[41:03]Priya Das: It can be a game-changer. Community bug bounties and open testnets expose your code to more diverse use cases. Some of the most critical bugs have been caught this way.

[41:18]Harasis: Let’s talk about learning from incidents. How do teams get better over time?

[41:32]Priya Das: Do honest, blameless post-mortems. Document what happened, what you missed, and update your playbooks. Share lessons with the whole team, not just ops. Continuous improvement is the heart of operational excellence.

[41:47]Harasis: And how do you make sure those lessons don’t get forgotten?

[41:57]Priya Das: Integrate them into onboarding, run periodic drills, and revisit your incident response checklists regularly. Make it a living process.

[42:10]Harasis: I want to pivot to organizational culture for a moment. What kind of culture supports operational excellence in blockchain?

[42:22]Priya Das: A culture where people feel safe raising concerns, where mistakes are learning opportunities, and where transparency is a default. No blame games—just solutions and shared accountability.

[42:37]Harasis: Let’s squeeze in one more mini case study. Can you share an example where culture made a real difference during an incident?

[42:54]Priya Das: Sure. There was a protocol that suffered a minor oracle manipulation. Instead of hiding it, the team held an open call with top users and contributors, walked through the exploit in real time, and invited suggestions. Not only did they patch the bug, but several users offered improvements that made the system stronger.

[43:18]Harasis: That’s inspiring. Shows the value of transparency and collaboration.

[43:25]Priya Das: Absolutely. It’s one of the reasons blockchain teams can move fast and still stay resilient.

[43:37]Harasis: Alright, as we head into the final stretch, let’s get practical. For listeners wanting to level up their blockchain operational excellence, can we walk through an implementation checklist?

[43:43]Priya Das: Definitely. Let’s do it step by step.

[43:47]Harasis: Alright, what’s step one?

[43:52]Priya Das: Step one: Define your key assets and risks. Know what contracts, wallets, and processes are mission-critical.

[44:00]Harasis: Step two?

[44:05]Priya Das: Map out monitoring requirements. Go beyond uptime—track contract events, admin actions, and anomaly signals.

[44:13]Harasis: Step three?

[44:17]Priya Das: Build automated alerting and escalation workflows. Make sure alerts go to the right people, fast.

[44:23]Harasis: Step four?

[44:28]Priya Das: Develop and rehearse your incident response plan. Assign clear roles, prepare communication templates, and run simulations.

[44:35]Harasis: Step five?

[44:41]Priya Das: Create deployment checklists—require reviews, multisig sign-offs, and staged rollouts. Never skip testnet deployments.

[44:48]Harasis: Step six?

[44:52]Priya Das: Continuously update your documentation. Every incident, every upgrade—document and share internally.

[44:56]Harasis: And finally?

[45:01]Priya Das: Foster a learning culture. Debrief after every incident, encourage feedback, and make operational excellence a shared mission.

[45:11]Harasis: That’s a solid checklist. For teams just starting, which step should they prioritize if they can only pick one?

[45:18]Priya Das: Start with monitoring and alerting. You can’t fix what you can’t see. Everything else builds on that.

[45:27]Harasis: Alright, we’re nearing the end. Any final words of advice for ops teams in the blockchain space?

[45:37]Priya Das: Don’t get complacent. Blockchain is a live-fire environment. Stay curious, stay humble, and always assume you’re missing something. And invest in your people as much as your code.

[45:48]Harasis: That’s great advice. Before we wrap up, what resources or communities would you point listeners to, for learning more?

[46:01]Priya Das: Check out the Ethereum Foundation’s security resources, join incident post-mortem forums, and get involved in blockchain ops communities on platforms like Discord and Telegram. There’s a wealth of battle-tested experience out there.

[46:15]Harasis: Fantastic. I want to thank you for sharing so many practical insights today. To close, let’s quickly recap our final checklist for operational excellence with blockchain:

[46:31]Priya Das: Here it is: - Identify assets and risks - Define monitoring and alerting - Build escalation and response workflows - Use deployment discipline—never skip reviews - Document and share lessons - Foster a transparent, learning culture

[46:50]Harasis: Perfect. We hope listeners can take these steps and apply them to their own projects.

[46:57]Priya Das: Absolutely. And remember, operational excellence is a journey, not a destination.

[47:05]Harasis: Alright, before we sign off, any parting thoughts or predictions for the future of blockchain ops?

[47:18]Priya Das: Expect more automation, smarter contracts that help defend themselves, and tighter integration between on-chain and off-chain monitoring. But the fundamentals—discipline, transparency, and learning—will always matter.

[47:30]Harasis: Thank you so much for joining us. This has been a fantastic deep dive into blockchain operational excellence.

[47:36]Priya Das: Thank you for having me. I enjoyed it!

[47:45]Harasis: For our listeners, you’ll find show notes and resources linked in the episode description. If you liked this conversation, please subscribe and share the episode. We’d love your feedback and suggestions for future topics.

[47:57]Priya Das: And if you’re working on blockchain ops, remember: you’re not alone—reach out to the community. We all learn together.

[48:07]Harasis: Alright, this is Softaims, signing off. Stay safe, stay operationally excellent—and we’ll see you next time.

[48:12]Priya Das: Take care, everyone.

[48:20]Harasis: And with that, we’ll leave you. Thanks for listening.

[48:35]Harasis: Softaims podcast will be back with more deep dives soon. Until then, keep innovating and keep your systems strong.

[55:00]Harasis: Thanks again for joining us on this episode about operational excellence with blockchain—focusing on monitoring, incident response, and deployment discipline. Have a great day, and goodbye!